From 9893043f52ab1cec144d1b6069497ef48edc9570 Mon Sep 17 00:00:00 2001 From: Andesha Date: Fri, 11 Oct 2019 15:32:21 -0400 Subject: [PATCH] Added example dataset for annotations and nested derivatives. --- eeg_face13/README | 4 + .../code/BIDS-Init-Face13-EEGLAB/README.md | 21 + .../BIDS-Init-Face13-EEGLAB/bids_face13.m | 105 + .../BIDS-Init-Face13-EEGLAB/bids_face13_min.m | 63 + .../code/BIDS-Init-Face13-EEGLAB/eeg_init.m | 126 + .../BIDS-Init-Face13-EEGLAB/init_script.m | 121 + eeg_face13/dataset_description.json | 17 + .../BIDS-Lossless-EEG/CONTRIBUTING.md | 1 + .../derivatives/BIDS-Lossless-EEG/LICENSE | 339 + .../derivatives/BIDS-Lossless-EEG/README.md | 48 + .../BIDS-Lossless-EEG/code/README.md | 3 + .../BIDS-Lossless-EEG/code/config/README.md | 2 + .../code/config/amica.jobinit | 1 + .../code/config/amica.sesinit | 6 + .../code/config/contextconfig.cfg | 32 + .../face13_sbatch/c01_scalpart_remote.cfg | 77 + .../face13_sbatch/c02_amica_init_remote.cfg | 36 + 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--git a/eeg_face13/code/BIDS-Init-Face13-EEGLAB/README.md b/eeg_face13/code/BIDS-Init-Face13-EEGLAB/README.md new file mode 100644 index 000000000..a98e87676 --- /dev/null +++ b/eeg_face13/code/BIDS-Init-Face13-EEGLAB/README.md @@ -0,0 +1,21 @@ +# BIDS-Init-Face13-EEGLAB +Root BIDS level initialization and BIDSification tool suite + +## How to Run +This installation assumes familiarity with the bash terminal, paths, and Matlab console use. +1. Download Face13 sourcedata folder, [here](https://drive.google.com/open?id=1xq85woDpAYXhCtzdgjkXpjjjggiWSKtc) +2. Create a project folder: `mkdir Face13` +3. Move the sourcedata into the project folder and rename as `sourcedata`. This step may differ based on download location and platform. +4. Enter the project folder: `cd Face13` +5. Create a code folder: `mkdir code` +6. Enter code folder: `cd code` +7. Use: `git clone --recursive https://github.com/BUCANL/BIDS-Init-Face13-EEGLAB.git` inside of the code folder +8. Point Matlab to project folder. In our case, Face13. +9. In the Matlab console execute: `addpath code/BIDS-Init-Face13-EEGLAB` +10. In the Matlab console execute: `addpath code/BIDS-Init-Face13-EEGLAB/eeglab` +11. In the Matlab console execute: `eeglab` +12. At this point the familiar eeglab window should appear. +13. To merge and relabel bdf files run: `init_script.m` +14. To BIDSify run: `bids_face13.m` +15. Select the project directory with the file chooser +16. Visualize your data! diff --git a/eeg_face13/code/BIDS-Init-Face13-EEGLAB/bids_face13.m b/eeg_face13/code/BIDS-Init-Face13-EEGLAB/bids_face13.m new file mode 100644 index 000000000..c405c5a57 --- /dev/null +++ b/eeg_face13/code/BIDS-Init-Face13-EEGLAB/bids_face13.m @@ -0,0 +1,105 @@ +% Global settings +bidsOpt.FileExt = 'edf'; +bidsOpt.InteralUse = true; + +% Data +data(1).file = {'./sourcedata/eeg/sub-s01_task-faceFO_eeg.set'}; +data(1).session = 1; +data(1).run = 1; + +data(2).file = {'./sourcedata/eeg/sub-s02_task-faceFO_eeg.set'}; +data(2).session = 1; +data(2).run = 1; + +data(3).file = {'./sourcedata/eeg/sub-s03_task-faceFO_eeg.set'}; +data(3).session = 1; +data(3).run = 1; + +data(4).file = {'./sourcedata/eeg/sub-s04_task-faceFO_eeg.set'}; +data(4).session = 1; +data(4).run = 1; + +data(5).file = {'./sourcedata/eeg/sub-s05_task-faceFO_eeg.set'}; +data(5).session = 1; +data(5).run = 1; + +data(6).file = {'./sourcedata/eeg/sub-s06_task-faceFO_eeg.set'}; +data(6).session = 1; +data(6).run = 1; + +data(7).file = {'./sourcedata/eeg/sub-s07_task-faceFO_eeg.set'}; +data(7).session = 1; +data(7).run = 1; + +data(8).file = {'./sourcedata/eeg/sub-s08_task-faceFO_eeg.set'}; +data(8).session = 1; +data(8).run = 1; + +data(9).file = {'./sourcedata/eeg/sub-s09_task-faceFO_eeg.set'}; +data(9).session = 1; +data(9).run = 1; + +data(10).file = {'./sourcedata/eeg/sub-s10_task-faceFO_eeg.set'}; +data(10).session = 1; +data(10).run = 1; + +% general information for dataset_description.json file +% ----------------------------------------------------- +generalInfo.Name = 'faceFO'; +generalInfo.License = 'ODbL (https://opendatacommons.org/licenses/odbl/summary/)'; +generalInfo.Authors = {'James A. Desjardins', 'Sidney J. Segalowitz'}; +generalInfo.HowToAcknowledge = 'Desjardins, J. A., & Segalowitz, S. J. (2013). Deconstructing the early visual electrocortical responses to face and house stimuli. Journal of Vision, 13(5), 22-22.'; +generalInfo.Funding = {'NSERC funding to Sidney Segalowitz'}; +generalInfo.ReferencesAndLinks = {'http://jov.arvojournals.org/article.aspx?articleid=2121634'}; +generalInfo.DatasetDOI = '10.1167/13.5.22'; + +% Readme and changes +% ----------------------------------------------------- +README = sprintf('# Face13 Dataset\n\nData used for JofV Deconstructing the early visual electrocortical response to face and house stimuli:\nhttps://jov.arvojournals.org/article.aspx?articleid=2121634'); +% CHANGES = sprintf(); + +% Task information for xxxx-eeg.json file +% ----------------------------------------------------- +tInfo.TaskName = 'FaceHouseCheck'; +tInfo.TaskDescription = 'Visual presentation of oval cropped face and house images both upright and inverted. Rare left or right half oval checkerboards were presetned as targets for keypress response.'; +tInfo.InstitutionName = 'Brock University'; +tInfo.InstitutionAddress = '500 Glenridge Ave, St.Catharines, Ontario'; +tInfo.EOGChannelCount = 7; +tInfo.EMGChannelCount = 0; +tInfo.MiscChannelCount = 0; +tInfo.TriggerChannelCount = 0; +tInfo.PowerLineFrequency = 60; +tInfo.EEGPlacementScheme = 'Custom equidistant 128 channel BioSemi montage established in coordination with Judith Schedden McMaster Univertisy'; +tInfo.Manufacturer = 'BioSemi'; +tInfo.ManufacturersModelName = 'ActiveTwo'; +tInfo.HardwareFilters = 'n/a'; +tInfo.SoftwareFilters = 'n/a'; +tInfo.SoftwareVersions = 'NI ActiView 532-Lores'; +tInfo.CapManufacturer = 'ElectroCap International'; +tInfo.CapManufacturersModelName = '10032'; + +% channel location file +% --------------------- +chanlocs = 'sourcedata/misc/BioSemi_BUCANL_EEGLAB.sfp'; + +% coordsystem string +% This string will be placed in each coordsystem.json +% --------------------- +coordsys = '{"EEGCoordinateSystem":"Other","EEGCoordinateUnits":"metres"}'; + +% call to the export function +% --------------------------- +bids_export(data, 'targetdir', uigetdir, 'taskName', ... + generalInfo.Name, 'gInfo', generalInfo, 'coordsys', coordsys, ... + 'chanlocs', chanlocs, 'README', README, 'bidsOpt', bidsOpt,'tInfo',tInfo); + +disp('Done'); + +% bids_export(data, 'targetdir', '/Users/arno/temp/bids_meditation_export', 'taskName', ... +% 'meditation', 'trialtype', trialTypes, 'gInfo', generalInfo, 'pInfo', pInfo, ... +% 'pInfoDesc', pInfoDesc, 'eInfoDesc', eInfoDesc, 'README', README, ... +% 'CHANGES', CHANGES, 'stimuli', stimuli, 'codefiles', code, 'tInfo', tInfo, 'chanlocs', chanlocs); + + +% init_bids_sub_script('.','',... 'TaskName', 'FaceHouseCheck', ... 'TaskDescription', 'Visual presentation of oval cropped face and house images both upright and inverted. Rare left or right half oval checkerboards were presetned as targets for keypress response.', ... 'InstitutionName', 'Brock University', ... 'InstitutionAddress', '500 Glenridge Ave, St.Catharines, Ontario', ... 'EOGChannelCount', 7, ... 'EMGChannelCount', 0, ... 'MiscChannelCount', 0, ... 'TriggerChannelCount', 0, ... 'PowerLineFrequency', 60, ... 'EEGPlacementScheme', 'Custom equidistant 128 channel BioSemi montage established in coordination with Judith Schedden McMaster Univertisy', ... 'Manufacturer', 'BioSemi', ... 'ManufacturersModelName', 'ActiveTwo', ... 'HardwareFilters', 'n/a', ... 'SoftwareFilters', 'n/a', ... 'SoftwareVersions', 'NI ActiView 532-Lores', ... 'CapManufacturer', 'ElectroCap International', ... 'CapManufacturersModelName', '10032'); + diff --git a/eeg_face13/code/BIDS-Init-Face13-EEGLAB/bids_face13_min.m b/eeg_face13/code/BIDS-Init-Face13-EEGLAB/bids_face13_min.m new file mode 100644 index 000000000..0e647fc4e --- /dev/null +++ b/eeg_face13/code/BIDS-Init-Face13-EEGLAB/bids_face13_min.m @@ -0,0 +1,63 @@ +% Preface: + +% Global settings +bidsOpt.FileExt = 'edf'; +bidsOpt.InteralUse = true; + +data(1).file = {'./sourcedata/eeg/sub-s01_task-faceFO_eeg.set'}; +data(1).session = 1; +data(1).run = 1; + +% general information for dataset_description.json file +% ----------------------------------------------------- +generalInfo.Name = 'faceFO'; +generalInfo.License = 'ODbL (https://opendatacommons.org/licenses/odbl/summary/)'; +generalInfo.Authors = {'James A. Desjardins', 'Sidney J. Segalowitz'}; +generalInfo.HowToAcknowledge = 'Desjardins, J. A., & Segalowitz, S. J. (2013). Deconstructing the early visual electrocortical responses to face and house stimuli. Journal of Vision, 13(5), 22-22.'; +generalInfo.Funding = {'NSERC funding to Sidney Segalowitz'}; +generalInfo.ReferencesAndLinks = {'http://jov.arvojournals.org/article.aspx?articleid=2121634'}; +generalInfo.DatasetDOI = '10.1167/13.5.22'; + +README = sprintf('# Face13 Dataset\n\nData used for JofV Deconstructing the early visual electrocortical response to face and house stimuli:\nhttps://jov.arvojournals.org/article.aspx?articleid=2121634'); + +% Task information for xxxx-eeg.json file +% ----------------------------------------------------- +tInfo.TaskName = 'FaceHouseCheck'; +tInfo.TaskDescription = 'Visual presentation of oval cropped face and house images both upright and inverted. Rare left or right half oval checkerboards were presetned as targets for keypress response.'; +tInfo.InstitutionName = 'Brock University'; +tInfo.InstitutionAddress = '500 Glenridge Ave, St.Catharines, Ontario'; +tInfo.EOGChannelCount = 7; +tInfo.EMGChannelCount = 0; +tInfo.MiscChannelCount = 0; +tInfo.TriggerChannelCount = 0; +tInfo.PowerLineFrequency = 60; +tInfo.EEGPlacementScheme = 'Custom equidistant 128 channel BioSemi montage established in coordination with Judith Schedden McMaster Univertisy'; +tInfo.Manufacturer = 'BioSemi'; +tInfo.ManufacturersModelName = 'ActiveTwo'; +tInfo.HardwareFilters = 'n/a'; +tInfo.SoftwareFilters = 'n/a'; +tInfo.SoftwareVersions = 'NI ActiView 532-Lores'; +tInfo.CapManufacturer = 'ElectroCap International'; +tInfo.CapManufacturersModelName = '10032'; + +% channel location file +% --------------------- +chanlocs = './sourcedata/misc/BioSemi_BUCANL_EEGLAB.sfp'; + +% coordsystem string +% This string will be placed in each coordsystem.json +% --------------------- +coordsys = '{"EEGCoordinateSystem":"Other","EEGCoordinateUnits":"metres"}'; + +% call to the export function +% --------------------------- +bids_export(data, 'targetdir', uigetdir, 'taskName', ... + generalInfo.Name, 'gInfo', generalInfo, 'coordsys', coordsys, ... + 'chanlocs', chanlocs, 'README', README, 'bidsOpt', bidsOpt,'tInfo',tInfo); + +disp('Done!'); + +% bids_export(data, 'targetdir', '/Users/arno/temp/bids_meditation_export', 'taskName', ... +% 'meditation', 'trialtype', trialTypes, 'gInfo', generalInfo, 'pInfo', pInfo, ... +% 'pInfoDesc', pInfoDesc, 'eInfoDesc', eInfoDesc, 'README', README, ... +% 'CHANGES', CHANGES, 'stimuli', stimuli, 'codefiles', code, 'tInfo', tInfo, 'chanlocs', chanlocs); \ No newline at end of file diff --git a/eeg_face13/code/BIDS-Init-Face13-EEGLAB/eeg_init.m b/eeg_face13/code/BIDS-Init-Face13-EEGLAB/eeg_init.m new file mode 100644 index 000000000..4a62701c1 --- /dev/null +++ b/eeg_face13/code/BIDS-Init-Face13-EEGLAB/eeg_init.m @@ -0,0 +1,126 @@ +function eeg_init(inbdffiles,outsetfile) + +%loop through inbdffiles +for i=1:length(inbdffiles); + %import bdf file + ALLEEG(i) = pop_biosig(inbdffiles{i}, 'channels', 1:128 ); + + %resample to 512 + ALLEEG(i) = pop_resample( ALLEEG(i), 512); +end + +%merge files +EEG = pop_mergeset( ALLEEG, 1:length(ALLEEG), 0); + +%load average BUCANL BioSemi head channel coordinates +EEG=pop_chanedit(EEG, 'load',{'sourcedata/misc/BioSemi_BUCANL_EEGLAB.sfp' 'filetype' 'autodetect'}); + +% Make sure each channel has the EEG type written in it for future BIDS +for i=1:length(EEG.chanlocs) + EEG.chanlocs(i).type = 'eeg'; +end + +%rename events +for i=1:length(EEG.event); + if isnumeric(EEG.event(i).type); + EEG.event(i).type=num2str(EEG.event(i).type); + end + if strcmp(EEG.event(i).type,'11'); + EEG.event(i).type='house-upright-f1'; + elseif strcmp(EEG.event(i).type,'12'); + EEG.event(i).type='house-upright-f2'; + elseif strcmp(EEG.event(i).type,'13'); + EEG.event(i).type='house-upright-f3'; + elseif strcmp(EEG.event(i).type,'14'); + EEG.event(i).type='house-upright-f4'; + elseif strcmp(EEG.event(i).type,'15'); + EEG.event(i).type='house-upright-f5'; + elseif strcmp(EEG.event(i).type,'16'); + EEG.event(i).type='house-upright-f6'; + elseif strcmp(EEG.event(i).type,'21'); + EEG.event(i).type='house-inverted-f1'; + elseif strcmp(EEG.event(i).type,'22'); + EEG.event(i).type='house-inverted-f2'; + elseif strcmp(EEG.event(i).type,'23'); + EEG.event(i).type='house-inverted-f3'; + elseif strcmp(EEG.event(i).type,'24'); + EEG.event(i).type='house-inverted-f4'; + elseif strcmp(EEG.event(i).type,'25'); + EEG.event(i).type='house-inverted-f5'; + elseif strcmp(EEG.event(i).type,'26'); + EEG.event(i).type='house-inverted-f6'; + elseif strcmp(EEG.event(i).type,'31'); + EEG.event(i).type='face-upright-f1'; + elseif strcmp(EEG.event(i).type,'32'); + EEG.event(i).type='face-upright-f2'; + elseif strcmp(EEG.event(i).type,'33'); + EEG.event(i).type='face-upright-f3'; + elseif strcmp(EEG.event(i).type,'34'); + EEG.event(i).type='face-upright-f4'; + elseif strcmp(EEG.event(i).type,'35'); + EEG.event(i).type='face-upright-f5'; + elseif strcmp(EEG.event(i).type,'36'); + EEG.event(i).type='face-upright-f6'; + elseif strcmp(EEG.event(i).type,'41'); + EEG.event(i).type='face-inverted-f1'; + elseif strcmp(EEG.event(i).type,'42'); + EEG.event(i).type='face-inverted-f2'; + elseif strcmp(EEG.event(i).type,'43'); + EEG.event(i).type='face-inverted-f3'; + elseif strcmp(EEG.event(i).type,'44'); + EEG.event(i).type='face-inverted-f4'; + elseif strcmp(EEG.event(i).type,'45'); + EEG.event(i).type='face-inverted-f5'; + elseif strcmp(EEG.event(i).type,'46'); + EEG.event(i).type='face-inverted-f6'; + elseif strcmp(EEG.event(i).type,'51'); + EEG.event(i).type='checker-f1'; + elseif strcmp(EEG.event(i).type,'52'); + EEG.event(i).type='checker-f2'; + elseif strcmp(EEG.event(i).type,'53'); + EEG.event(i).type='checker-f3'; + elseif strcmp(EEG.event(i).type,'54'); + EEG.event(i).type='checker-f4'; + elseif strcmp(EEG.event(i).type,'55'); + EEG.event(i).type='checker-f5'; + elseif strcmp(EEG.event(i).type,'56'); + EEG.event(i).type='checker-f6'; + elseif strcmp(EEG.event(i).type,'211'); + EEG.event(i).type='face-upright'; + elseif strcmp(EEG.event(i).type,'212'); + EEG.event(i).type='face-inverted'; + elseif strcmp(EEG.event(i).type,'213'); + EEG.event(i).type='house-upright'; + elseif strcmp(EEG.event(i).type,'214'); + EEG.event(i).type='house-inverted'; + elseif strcmp(EEG.event(i).type,'215'); + EEG.event(i).type='checker-left'; + elseif strcmp(EEG.event(i).type,'216'); + EEG.event(i).type='checker-right'; + elseif strcmp(EEG.event(i).type,'201'); + EEG.event(i).type='press-left'; + elseif strcmp(EEG.event(i).type,'204'); + EEG.event(i).type='press-right'; + elseif strcmp(EEG.event(i).type,'boundary'); + EEG.event(i).duration=0; + else + EEG.event(i).type=['e-',num2str(EEG.event(i).type)]; + end +end + +% Special casing for parallel port issue +if strcmp(outsetfile,'sourcedata/eeg/sub-s07_task-faceFO_eeg.set') + eind=find([EEG.event.latency]>789010 & [EEG.event.latency]<828844); + reAccum = []; + for i=1:length(eind) + if ~strcmp(EEG.event(eind(i)).type,'boundary') + reAccum = [reAccum; eind(i)]; + end + end + EEG.event(reAccum) = []; +end + +%save output set file +EEG = pop_saveset( EEG, 'filename',outsetfile); + + diff --git a/eeg_face13/code/BIDS-Init-Face13-EEGLAB/init_script.m b/eeg_face13/code/BIDS-Init-Face13-EEGLAB/init_script.m new file mode 100644 index 000000000..2abaa85fd --- /dev/null +++ b/eeg_face13/code/BIDS-Init-Face13-EEGLAB/init_script.m @@ -0,0 +1,121 @@ +diary 'sourcedata/eeg/sub-s01.log'; +%% CHECK FOR OUTPUT PATH AND CRETAE IF NECESSARY +eeg_init({'sourcedata/eeg/IC_trn_P01_F_1.bdf', ... + 'sourcedata/eeg/IC_trn_P01_F_2.bdf', ... + 'sourcedata/eeg/IC_trn_P01_f_3.bdf', ... + 'sourcedata/eeg/IC_trn_P01_f_4.bdf', ... + 'sourcedata/eeg/IC_trn_P01_O_1.bdf', ... + 'sourcedata/eeg/IC_trn_P01_O_2.bdf', ... + 'sourcedata/eeg/IC_trn_P01_O_3.bdf', ... + 'sourcedata/eeg/IC_trn_P01_O_4.bdf'}, ... + 'sourcedata/eeg/sub-s01_task-faceFO_eeg.set'); + +diary 'sourcedata/eeg/sub-s02.log'; +%% CHECK FOR OUTPUT PATH AND CRETAE IF NECESSARY +eeg_init({'sourcedata/eeg/IC_trn_P02_F_1.bdf', ... + 'sourcedata/eeg/IC_trn_P02_F_2.bdf', ... + 'sourcedata/eeg/IC_trn_P02_F_3.bdf', ... + 'sourcedata/eeg/IC_trn_P02_F_4.bdf', ... + 'sourcedata/eeg/IC_trn_P02_O_1.bdf', ... + 'sourcedata/eeg/IC_trn_P02_O_2.bdf', ... + 'sourcedata/eeg/IC_trn_P02_O_3.bdf', ... + 'sourcedata/eeg/IC_trn_P02_O_4.bdf'}, ... + 'sourcedata/eeg/sub-s02_task-faceFO_eeg.set'); + +diary 'sourcedata/eeg/sub-s03.log'; +%% CHECK FOR OUTPUT PATH AND CRETAE IF NECESSARY +eeg_init({'sourcedata/eeg/IC_trn_P03_F_1.bdf', ... + 'sourcedata/eeg/IC_trn_P03_F_2.bdf', ... + 'sourcedata/eeg/IC_trn_P03_F_3.bdf', ... + 'sourcedata/eeg/IC_trn_P03_F_4.bdf', ... + 'sourcedata/eeg/IC_trn_P03_O_1.bdf', ... + 'sourcedata/eeg/IC_trn_P03_O_2.bdf', ... + 'sourcedata/eeg/IC_trn_P03_O_3.bdf', ... + 'sourcedata/eeg/IC_trn_P03_O_4.bdf'}, ... + 'sourcedata/eeg/sub-s03_task-faceFO_eeg.set'); + +diary 'sourcedata/eeg/sub-s04.log'; +%% CHECK FOR OUTPUT PATH AND CRETAE IF NECESSARY +eeg_init({'sourcedata/eeg/IC_trn_P04_F_1.bdf', ... + 'sourcedata/eeg/IC_trn_P04_F_2.bdf', ... + 'sourcedata/eeg/IC_trn_P04_F_3.bdf', ... + 'sourcedata/eeg/IC_trn_P04_F_4.bdf', ... + 'sourcedata/eeg/IC_trn_P04_O_1.bdf', ... + 'sourcedata/eeg/IC_trn_P04_O_2.bdf', ... + 'sourcedata/eeg/IC_trn_P04_O_3.bdf', ... + 'sourcedata/eeg/IC_trn_P04_O_4.bdf'}, ... + 'sourcedata/eeg/sub-s04_task-faceFO_eeg.set'); + +diary 'sourcedata/eeg/sub-s05.log'; +%% CHECK FOR OUTPUT PATH AND CRETAE IF NECESSARY +eeg_init({'sourcedata/eeg/IC_trn_P05_F_1.bdf', ... + 'sourcedata/eeg/IC_trn_P05_F_2.bdf', ... + 'sourcedata/eeg/IC_trn_P05_F_3.bdf', ... + 'sourcedata/eeg/IC_trn_P05_F_4.bdf', ... + 'sourcedata/eeg/IC_trn_P05_O_1.bdf', ... + 'sourcedata/eeg/IC_trn_P05_O_2.bdf', ... + 'sourcedata/eeg/IC_trn_P05_O_3.bdf', ... + 'sourcedata/eeg/IC_trn_P05_O_4.bdf'}, ... + 'sourcedata/eeg/sub-s05_task-faceFO_eeg.set'); + +diary 'sourcedata/eeg/sub-s06.log'; +%% CHECK FOR OUTPUT PATH AND CRETAE IF NECESSARY +eeg_init({'sourcedata/eeg/IC_trn_P06_F_1.bdf', ... + 'sourcedata/eeg/IC_trn_P06_F_2.bdf', ... + 'sourcedata/eeg/IC_trn_P06_F_3.bdf', ... + 'sourcedata/eeg/IC_trn_P06_F_4.bdf', ... + 'sourcedata/eeg/IC_trn_P06_O_1.bdf', ... + 'sourcedata/eeg/IC_trn_P06_O_2.bdf', ... + 'sourcedata/eeg/IC_trn_P06_O_3.bdf', ... + 'sourcedata/eeg/IC_trn_P06_O_4.bdf'}, ... + 'sourcedata/eeg/sub-s06_task-faceFO_eeg.set'); + +diary 'sourcedata/eeg/sub-s07.log'; +%% CHECK FOR OUTPUT PATH AND CRETAE IF NECESSARY +eeg_init({'sourcedata/eeg/IC_trn_P07_F_1.bdf', ... + 'sourcedata/eeg/IC_trn_P07_F_2.bdf', ... + 'sourcedata/eeg/IC_trn_P07_F_3.bdf', ... + 'sourcedata/eeg/IC_trn_P07_F_4.bdf', ... + 'sourcedata/eeg/IC_trn_P07_O_1.bdf', ... + 'sourcedata/eeg/IC_trn_P07_O_2.bdf', ... + 'sourcedata/eeg/IC_trn_P07_O_3.bdf', ... + 'sourcedata/eeg/IC_trn_P07_O_4.bdf'}, ... + 'sourcedata/eeg/sub-s07_task-faceFO_eeg.set'); + +diary 'sourcedata/eeg/sub-s08.log'; +%% CHECK FOR OUTPUT PATH AND CRETAE IF NECESSARY +eeg_init({'sourcedata/eeg/IC_trn_P08_F_1.bdf', ... + 'sourcedata/eeg/IC_trn_P08_F_2.bdf', ... + 'sourcedata/eeg/IC_trn_P08_F_3.bdf', ... + 'sourcedata/eeg/IC_trn_P08_F_4.bdf', ... + 'sourcedata/eeg/IC_trn_P08_O_1.bdf', ... + 'sourcedata/eeg/IC_trn_P08_O_2.bdf', ... + 'sourcedata/eeg/IC_trn_P08_O_3.bdf', ... + 'sourcedata/eeg/IC_trn_P08_O_4.bdf'}, ... + 'sourcedata/eeg/sub-s08_task-faceFO_eeg.set'); + +diary 'sourcedata/eeg/sub-s09.log'; +%% CHECK FOR OUTPUT PATH AND CRETAE IF NECESSARY +eeg_init({'sourcedata/eeg/IC_trn_P09_F_1.bdf', ... + 'sourcedata/eeg/IC_trn_P09_F_2.bdf', ... + 'sourcedata/eeg/IC_trn_P09_F_3.bdf', ... + 'sourcedata/eeg/IC_trn_P09_F_4.bdf', ... + 'sourcedata/eeg/IC_trn_P09_O_1.bdf', ... + 'sourcedata/eeg/IC_trn_P09_O_2.bdf', ... + 'sourcedata/eeg/IC_trn_P09_O_3.bdf', ... + 'sourcedata/eeg/IC_trn_P09_O_4.bdf'}, ... + 'sourcedata/eeg/sub-s09_task-faceFO_eeg.set'); + +diary 'sourcedata/eeg/sub-s10.log'; +%% CHECK FOR OUTPUT PATH AND CRETAE IF NECESSARY +eeg_init({'sourcedata/eeg/IC_trn_P10_F_1.bdf', ... + 'sourcedata/eeg/IC_trn_P10_F_2.bdf', ... + 'sourcedata/eeg/IC_trn_P10_F_3.bdf', ... + 'sourcedata/eeg/IC_trn_P10_F_4.bdf', ... + 'sourcedata/eeg/IC_trn_P10_O_1.bdf', ... + 'sourcedata/eeg/IC_trn_P10_O_2.bdf', ... + 'sourcedata/eeg/IC_trn_P10_O_3.bdf', ... + 'sourcedata/eeg/IC_trn_P10_O_4.bdf'}, ... + 'sourcedata/eeg/sub-s10_task-faceFO_eeg.set'); + +disp('Done!'); \ No newline at end of file diff --git a/eeg_face13/dataset_description.json b/eeg_face13/dataset_description.json new file mode 100644 index 000000000..a41cae261 --- /dev/null +++ b/eeg_face13/dataset_description.json @@ -0,0 +1,17 @@ +{ + "Name": "faceFO", + "License": "ODbL (https://opendatacommons.org/licenses/odbl/summary/)", + "Authors": [ + "James A. Desjardins", + "Sidney J. Segalowitz" + ], + "HowToAcknowledge": "Desjardins, J. A., & Segalowitz, S. J. (2013). Deconstructing the early visual electrocortical responses to face and house stimuli. Journal of Vision, 13(5), 22-22.", + "Funding": [ + "NSERC funding to Sidney Segalowitz" + ], + "ReferencesAndLinks": [ + "http://jov.arvojournals.org/article.aspx?articleid=2121634" + ], + "DatasetDOI": "10.1167/13.5.22", + "BIDSVersion": "1.1.1" +} diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/CONTRIBUTING.md b/eeg_face13/derivatives/BIDS-Lossless-EEG/CONTRIBUTING.md new file mode 100644 index 000000000..177dd3ceb --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/CONTRIBUTING.md @@ -0,0 +1 @@ +See [Contribution Guide](https://git.sharcnet.ca/jdesjard/bids_lossless_eeg/wikis/contributing) \ No newline at end of file diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/LICENSE b/eeg_face13/derivatives/BIDS-Lossless-EEG/LICENSE new file mode 100644 index 000000000..ecbc05937 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/LICENSE @@ -0,0 +1,339 @@ + GNU GENERAL PUBLIC LICENSE + Version 2, June 1991 + + Copyright (C) 1989, 1991 Free Software Foundation, Inc., + 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The licenses for most software are designed to take away your +freedom to share and change it. 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Of course, the commands you use may +be called something other than `show w' and `show c'; they could even be +mouse-clicks or menu items--whatever suits your program. + +You should also get your employer (if you work as a programmer) or your +school, if any, to sign a "copyright disclaimer" for the program, if +necessary. Here is a sample; alter the names: + + Yoyodyne, Inc., hereby disclaims all copyright interest in the program + `Gnomovision' (which makes passes at compilers) written by James Hacker. + + , 1 April 1989 + Ty Coon, President of Vice + +This General Public License does not permit incorporating your program into +proprietary programs. If your program is a subroutine library, you may +consider it more useful to permit linking proprietary applications with the +library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. \ No newline at end of file diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/README.md b/eeg_face13/derivatives/BIDS-Lossless-EEG/README.md new file mode 100644 index 000000000..f2e980ae8 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/README.md @@ -0,0 +1,48 @@ +# The Lossless EEG Pipeline + +![photo](https://www.cosc.brocku.ca/~tk11br/pipelineGraphic.png) + +This repository contains the lossless EEG pipeline package that contains everything you need to get started processing EEG data. + +## Documentation +A reference manual for the pipeline can be found on the [Wiki](https://github.com/BUCANL/BIDS-Lossless-EEG/wiki) + +A tutorial for using the pipeline with a sample dataset can be found [here](https://bucanl.github.io/SDC-LOSSLESS/) + +## How to Run +This installation assumes familiarity with the bash terminal, paths, and Matlab console use, as well as requires the use of a remote computer cluster and git. These setup steps can be run to set up the pipeline for use with the Face13 sample dataset during the tutorial or for use with your own data. + +**Local Setup** +1. If necessary, create a derivatives folder within your project folder (this will be the Face13 folder if you are doing the tutorial): `mkdir derivatives` +2. Change directory into the derivatives folder: `cd derivatives` +3. Within the derivatives folder, clone the pipeline locally: `git clone --recursive https://github.com/BUCANL/BIDS-Lossless-EEG.git` + +**Remote Setup** + +4. Navigate to your remote computer cluster and create a remote project folder: `mkdir project_name` +5. In the project_name folder, create a derivatives folder: `mkdir derivatives` +6. Change directory into the derivatives folder: `cd derivatives` +7. Within the derivatives folder, clone the pipeline on the remote: `git clone --recursive https://github.com/BUCANL/BIDS-Lossless-EEG.git` +8. Navigate back to the project root: `cd ..` +9. Run the remote setup and follow the on screen prompts: `bash derivatives/BIDS-Lossless-EEG/code/install/setup-remote.sh` + +**Running Files Through the Pipeline** + +10. Copy files from local to remote. Instructions for copying files can be found in the [Face13 tutorial](https://bucanl.github.io/SDC-LOSSLESS/03-submit/index.html) +11. Point Matlab to your project_name folder (Face13 folder if you are doing the tutorial) +12. In the Matlab console execute: `addpath derivatives/BIDS-Lossless-EEG/code/install` +13. In the Matlab console execute: `lossless_path` +14. In the Matlab console execute: `eeglab` +15. Run history files s01-s05, with batch context configuration files c01-c05, and an appropriate context configuration on selected data files in a history template batch. Information on how to set up a context configuration and load the history template batch can be found in the [Face13 tutorial](https://bucanl.github.io/SDC-LOSSLESS/03-submit/index.html) +16. Copy completed files from remote to local. Instructions for copying files can be found in the [Face13 tutorial](https://bucanl.github.io/SDC-LOSSLESS/03-submit/index.html) + +## Contact / Issues +Feel free to make an issue in the [Issue Tracker](https://github.com/BUCANL/BIDS-Lossless-EEG/issues) +or [contact us directly](https://github.com/BUCANL/BIDS-Lossless-EEG/wiki/Contacting-Us) + +## License / Copyright + +Copyright (C) 2017 James Desjardins and others +This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. +This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. +You should have received a copy of the GNU General Public License along with this program (LICENSE.txt file in the root directory); if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/README.md b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/README.md new file mode 100644 index 000000000..c1971a788 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/README.md @@ -0,0 +1,3 @@ +# The Lossless Pipeline + +This level of the repository contains the pipeline scripts, configs, and their dependencies. \ No newline at end of file diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/README.md b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/README.md new file mode 100644 index 000000000..2b831b6b5 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/README.md @@ -0,0 +1,2 @@ +# The Lossless Pipeline - Configuration +This folder contains all of the configuration files and init files that are needed for the EEGLAB plugins. Configuration files will have a .cfg or .txt extension. The naming of each configuration file should mirror the script that it goes with. \ No newline at end of file diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/amica.jobinit b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/amica.jobinit new file mode 100644 index 000000000..affc4a73e --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/amica.jobinit @@ -0,0 +1 @@ +LD_LIBRARY_PATH=$LD_RUN_PATH \ diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/amica.sesinit b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/amica.sesinit new file mode 100644 index 000000000..db510a70c --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/amica.sesinit @@ -0,0 +1,6 @@ +#OMPI_MCA_mpi_cuda_support=0 +#export OMPI_MCA_mpi_cuda_support + +module unload intel +module load intel/2017.1 +module load imkl/2017.1.132 diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/contextconfig.cfg b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/contextconfig.cfg new file mode 100644 index 000000000..c82bfc870 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/contextconfig.cfg @@ -0,0 +1,32 @@ +log + derivatives/BIDS-Lossless-EEG/log +local_project + +local_dependency + derivatives/BIDS-Lossless-EEG/code/dependencies +remote_user_name + user_name +remote_exec_host + graham.computecanada.ca +remote_project_archive + graham.computecanada.ca:/project/group_name/user_name/project_name/ +remote_project_work + graham.computecanada.ca:/scratch/user_name/project_name/ +remote_dependency + graham.computecanada.ca:/scratch/user_name/project_name/derivatives/BIDS-Lossless-EEG/code/dependencies +mount_archive + remote_archive +mount_work + remote_work +misc + [config_dir] derivatives/BIDS-Lossless-EEG/code/config + [data_dir] null_value + [script_dir] derivatives/BIDS-Lossless-EEG/code/scripts +system_cmds + sshfs [remote_user_name]@[remote_project_archive] [cd]/[mount_archive] + sshfs [remote_user_name]@[remote_project_work] [cd]/[mount_work] + meld [local_project] [cd]/[mount_work] & + meld [local_project] [cd]/[mount_archive] [cd]/[mount_work] & + fusermount -u [cd]/[mount_archive] + fusermount -u [cd]/[mount_work] + diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/face13_sbatch/c01_scalpart_remote.cfg b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/face13_sbatch/c01_scalpart_remote.cfg new file mode 100644 index 000000000..04c077b3d --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/face13_sbatch/c01_scalpart_remote.cfg @@ -0,0 +1,77 @@ +file_name + c01_scalpart_remote.cfg +exec_func + ef_sbatch +replace_string + [in_path],[] + [montage_info],[0.500 -22.000 -48.000 -0.065 0.000 -1.580 1060.000 1260.000 1220.000] + [staging_script],derivatives/BIDS-Lossless-EEG/code/scripts/face13_staging.m + [aref_trim],30 + [recur_sec],1 + [limit_sec],[0 1] + [sd_t_meth],na + [sd_t_vals],[.3 .7] + [sd_t_o],16 + [sd_t_f_meth],fixed + [sd_t_f_vals], + [sd_t_f_o],.2 + [sd_t_pad],1 + [sd_ch_meth],q + [sd_ch_vals],[.3 .7] + [sd_ch_o],16 + [sd_ch_f_meth],fixed + [sd_ch_f_vals], + [sd_ch_f_o],.2 + [ref_loc_file],derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc + [low_bound_hz],1 + [high_bound_hz],100 + [save_f_res],1 + [n_nbr_ch],3 + [r_ch_meth],q + [r_ch_vals],[.3,.7] + [r_ch_o],16 + [r_ch_f_meth],fixed + [r_ch_f_vals], + [r_ch_f_o],.2 + [bridge_trim],40 + [bridge_z],6 + [n_nbr_t],3 + [r_t_meth],q + [r_t_vals],[.3 .7] + [r_t_o],16 + [r_t_f_meth],fixed + [r_t_f_vals], + [r_t_f_o],.2 + [min_gap_ms],2000 + [out_path],derivatives/BIDS-Lossless-EEG + [amica_param_file],derivatives/BIDS-Lossless-EEG/code/misc/amica15_default.param + [amica_threads_s02],8 +order + 1 +session_init + derivatives/BIDS-Lossless-EEG/code/config/octave.sesinit +job_name + [batch_hfn,.,1]_[/,-1,batch_dfn,.,-1] +mfile_name + [/,-1,batch_dfn,.,-1] +job_init + +m_init + derivatives/BIDS-Lossless-EEG/code/config/octave.minit +submit_options + +memory + 16g +time_limit + 30m +mpi + false +num_tasks + 1 +threads_per_task + +software + octave +program_options + + diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/face13_sbatch/c02_amica_init_remote.cfg b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/face13_sbatch/c02_amica_init_remote.cfg new file mode 100644 index 000000000..3ed2f7424 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/face13_sbatch/c02_amica_init_remote.cfg @@ -0,0 +1,36 @@ +file_name + c02_amica_init_remote.cfg +exec_func + ef_sbatch +replace_string + [dep_path],derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/amica + [in_path], + [amica_ver],amica15 +order + [2 1] +session_init + derivatives/BIDS-Lossless-EEG/code/config/amica.sesinit +job_name + [batch_hfn,.,1]_[/,-1,batch_dfn,.,-1] +mfile_name + [/,-1,batch_dfn,.,-1] +job_init + derivatives/BIDS-Lossless-EEG/code/config/amica.jobinit +m_init + +submit_options + +memory + 2G +time_limit + 2h +mpi + true +num_processors + +threads_per_task + 8 +software + none +program_options + diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/face13_sbatch/c03_compart_data_remote.cfg b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/face13_sbatch/c03_compart_data_remote.cfg new file mode 100644 index 000000000..e0c7ad5fa --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/face13_sbatch/c03_compart_data_remote.cfg @@ -0,0 +1,41 @@ +file_name + c03_compart_data_remote.cfg +exec_func + ef_sbatch +replace_string + [in_path],derivatives/BIDS-Lossless-EEG + [recur_sec],1 + [limit_sec],[0 1] + [sd_t_meth],q + [sd_t_vals],[.3 .7] + [sd_t_o],6 + [sd_t_f_meth],fixed + [sd_t_f_vals], + [sd_t_f_o],.2 + [min_gap_ms],2000 + [out_path],derivatives/BIDS-Lossless-EEG + [amica_param_file],derivatives/BIDS-Lossless-EEG/code/misc/amica_default.param + [amica_threads_s04],8 +order + [3 2] +session_init + derivatives/BIDS-Lossless-EEG/code/config/octave.sesinit +job_name + [batch_hfn,.,1]_[/,-1,batch_dfn,.,-1] +mfile_name + [/,-1,batch_dfn,.,-1] +job_init + +m_init + derivatives/BIDS-Lossless-EEG/code/config/octave.minit +submit_options + +memory + 16g +time_limit + 30m +mpi + false +software + octave +program_options diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/face13_sbatch/c04a_amica_remote.cfg b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/face13_sbatch/c04a_amica_remote.cfg new file mode 100644 index 000000000..27fef5b68 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/face13_sbatch/c04a_amica_remote.cfg @@ -0,0 +1,36 @@ +file_name + c04a_amica_remote.cfg +exec_func + ef_sbatch +replace_string + [dep_path],derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/amica + [in_path], + [amica_ver],amica15 +order + [4 3] +session_init + derivatives/BIDS-Lossless-EEG/code/config/amica.sesinit +job_name + [batch_hfn,.,1]_[/,-1,batch_dfn,.,-1] +mfile_name + [/,-1,batch_dfn,.,-1] +job_init + derivatives/BIDS-Lossless-EEG/code/config/amica.jobinit +m_init + +submit_options + +memory + 2G +time_limit + 2h +mpi + true +num_processors + +threads_per_task + 8 +software + none +program_options + diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/face13_sbatch/c04b_amica_remote.cfg b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/face13_sbatch/c04b_amica_remote.cfg new file mode 100644 index 000000000..2d9245d6c --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/face13_sbatch/c04b_amica_remote.cfg @@ -0,0 +1,36 @@ +file_name + c04b_amica_remote.cfg +exec_func + ef_sbatch +replace_string + [dep_path],derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/amica + [in_path], + [amica_ver],amica15 +order + [4 3] +session_init + derivatives/BIDS-Lossless-EEG/code/config/amica.sesinit +job_name + [batch_hfn,.,1]_[/,-1,batch_dfn,.,-1] +mfile_name + [/,-1,batch_dfn,.,-1] +job_init + derivatives/BIDS-Lossless-EEG/code/config/amica.jobinit +m_init + +submit_options + +memory + 2G +time_limit + 2h +mpi + true +num_processors + +threads_per_task + 8 +software + none +program_options + diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/face13_sbatch/c04c_amica_remote.cfg b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/face13_sbatch/c04c_amica_remote.cfg new file mode 100644 index 000000000..ef3ee062a --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/face13_sbatch/c04c_amica_remote.cfg @@ -0,0 +1,36 @@ +file_name + c04c_amica_remote.cfg +exec_func + ef_sbatch +replace_string + [dep_path],derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/amica + [in_path], + [amica_ver],amica15 +order + [4 3] +session_init + derivatives/BIDS-Lossless-EEG/code/config/amica.sesinit +job_name + [batch_hfn,.,1]_[/,-1,batch_dfn,.,-1] +mfile_name + [/,-1,batch_dfn,.,-1] +job_init + derivatives/BIDS-Lossless-EEG/code/config/amica.jobinit +m_init + +submit_options + +memory + 2G +time_limit + 2h +mpi + true +num_processors + +threads_per_task + 8 +software + none +program_options + diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/face13_sbatch/c05_concat_data.cfg b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/face13_sbatch/c05_concat_data.cfg new file mode 100644 index 000000000..99b4d277b --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/face13_sbatch/c05_concat_data.cfg @@ -0,0 +1,53 @@ +file_name + c05_concat_data_remote.cfg +exec_func + ef_sbatch +replace_string + [in_path],derivatives/BIDS-Lossless-EEG + [recur_sec],1 + [limit_sec],[0 1] + [sd_t_meth],q + [sd_t_vals],[.3 .7] + [sd_t_o],6 + [sd_t_f_meth],fixed + [sd_t_f_vals], + [sd_t_f_o],.2 + [min_gap_ms],2000 + [icr_crit],0.05 + [dip_vol], derivatives/BIDS-Lossless-EEG/code/misc/standard_vol.mat + [dip_mri], derivatives/BIDS-Lossless-EEG/code/misc/standard_mri.mat + [dip_elc], derivatives/BIDS-Lossless-EEG/code/misc/standard_1020.elc + [dip_transmat], [0 0 0 0 0 0 1 1 1] + [cor_transmat], [0,0,0,0,0,1.57,1,1,1] + [cor_nosedir], +X + + [out_path],derivatives/BIDS-Lossless-EEG +order + [5 4] +session_init + derivatives/BIDS-Lossless-EEG/code/config/octave.sesinit +job_name + [batch_hfn,.,1]_[/,-1,batch_dfn,.,-1] +mfile_name + [/,-1,batch_dfn,.,-1] +job_init + +m_init + derivatives/BIDS-Lossless-EEG/code/config/octave.minit +submit_options + +memory + 8g +time_limit + 1h +mpi + false +num_processors + +threads_per_task + 8 +software + octave +program_options + + diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/face13_sbatch/clean_up.cfg b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/face13_sbatch/clean_up.cfg new file mode 100644 index 000000000..bf437d01f --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/face13_sbatch/clean_up.cfg @@ -0,0 +1,50 @@ +file_name + c99_clean.cfg +exec_func + ef_sbatch +replace_string + [in_path],anaylsis/log + [preproc_amica_param],true + [preproc_amicaout],true + [preproc_concat_data],true + [preproc_concat_asr],false + [preproc_sa],true + [preproc_sa_purge],true + [preproc_asr],true + [preproc_asr_purge],true + [preproc_compart_data_purge],true + [preproc_compart_data],true + [preproc_dip],false + [trash_root],true + [zip_successful_logs],true + [delete_successful_logs],true + [use_system_xz],true + [use_system_gzip],false + [use_zip],false + +order + [13 12] +session_init + analysis/support/config/octave.sesinit +job_name + [batch_hfn,.,1]_[/,-1,batch_dfn,.,-1] +mfile_name + [/,-1,batch_dfn,.,-1] +job_init + +m_init + analysis/support/config/octave.minit +qsub_options + +memory + 3g +time_limit + 20m +mpi + false +num_processors + 1 +software + octave +program_options + diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/face13_sbatch/rebuild_remote.cfg b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/face13_sbatch/rebuild_remote.cfg new file mode 100644 index 000000000..cf6dcfa7a --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/face13_sbatch/rebuild_remote.cfg @@ -0,0 +1,38 @@ +file_name + c20_rebuild_remote.cfg +exec_func + ef_sqsub +replace_string + [in_path],analysis/data/2_preproc + [ori_in_path],analysis/data/1_init + [out_path],analysis/data/2_preproc + [datafile],concat_asrdata + [remasrloss],'YES' + [remfilterloss],'YES' +order + [20] +session_init + analysis/support/config/octave.sesinit +job_name + [batch_hfn,.,1]_[/,-1,batch_dfn,.,-1] +mfile_name + [/,-1,batch_dfn,.,-1] +job_init + +m_init + analysis/support/config/octave.minit +qsub_options + -r 1h --mpp 6G +memory + 6g +time_limit + 1h +mpi + false +numprocesors + 1 +software + octave +program_options + + diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/matlab.minit b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/matlab.minit new file mode 100644 index 000000000..7c4946ca9 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/matlab.minit @@ -0,0 +1,14 @@ +warning off; + +%addpath([pwd 'derivatives/lossless/code/dependencies/eeglab_lossless'], ... +% [pwd 'derivatives/lossless/code/dependencies/matlog']); + +% eeglab auto loads the path correctly instead of shotgun blasting with +% addpath(genpath(<>)) as we were doing prior +if ~exist('EEG') + eeglab; +end + +% Options +pop_editoptions('option_savetwofiles', 1,'option_single',0); + diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/octave.minit b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/octave.minit new file mode 100644 index 000000000..5cdf8b982 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/octave.minit @@ -0,0 +1,36 @@ +warning off; + +pkg load signal; +pkg load parallel; +pkg load io; + +file_path = fileparts(mfilename('fullpath')); + +addpath([file_path '/../../code/dependencies/eeglab_lossless'], ... + [file_path '/../../code/dependencies/matlog'], ... + [file_path '/../../code/tools']); +cd '[:,1,remote_project_work]'; + +% eeglab auto loads the path correctly instead of shotgun blasting with +% addpath(genpath(<>)) as we were doing prior +if ~exist('EEG', 'var') + eeglab; +end +% Use matlog disp function override +logging_override_disp(true); +% Use matlog INFO level debugging +logging_set_log_level('INFO') + +% Don't run with traditional but enable some of the options +save_default_options ('-mat-binary'); +confirm_recursive_rmdir(false); + +% This makes | and & || and && in earlier version of Octave where +% | and & dont short curcuit by default +if exist('do_braindead_shortcircuit_evaluation', 'builtin') + do_braindead_shortcircuit_evaluation(true); +end + +% Options +pop_editoptions('option_savetwofiles', 1,'option_single',0); + diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/octave.sesinit b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/octave.sesinit new file mode 100644 index 000000000..1403f8853 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/octave.sesinit @@ -0,0 +1,2 @@ +module load gcc/7.3.0; +module load octave/4.4.1; diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/vised_config.cfg b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/vised_config.cfg new file mode 100644 index 000000000..60cc3280a --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/vised_config.cfg @@ -0,0 +1,88 @@ +pop_gui + on +data_type + +winrej_marks_labels + +quick_evtmk + +quick_evtrm + off +quick_chanflag + off +chan_marks_struct + +time_marks_struct + +marks_y_loc + +inter_mark_int + +inter_tag_int + +marks_col_int + +marks_col_alpha + +spacing + +eloc_file + +limits + +freqlimits + +winlength + 40 +dispchans + +title + +xgrid + off +ygrid + off +ploteventdur + off +data2 + +command + +butlabel + +color + +wincolor + +submean + on +position + +tag + vef +children + +scale + on +mocap + +selectcommand + +altselectcommand + +extselectcommand + +keyselectcommand + +mouse_data_front + on +trialstag + +datastd + +normed + +envelope + +chaninfo + diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/vised_config_qc.cfg b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/vised_config_qc.cfg new file mode 100644 index 000000000..70faf18dd --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/vised_config_qc.cfg @@ -0,0 +1,99 @@ +pop_gui + on +data_type + ICA +winrej_marks_labels + manual +quick_evtmk + +quick_evtrm + off +quick_chanflag + off +chan_marks_struct + +time_marks_struct + EEG.marks.time_info +marks_y_loc + 0.8 +inter_mark_int + 0.04 +inter_tag_int + 0.002 +marks_col_int + 0.1 +marks_col_alpha + 0.4 +spacing + 12 +eloc_file + +limits + +freqlimits + +winlength + 40 +dispchans + +title + +xgrid + off +ygrid + off +ploteventdur + off +data2 + +command + EEG=ve_update(EEG);EEG.saved = 'no'; +butlabel + Update EEG structure +color + +wincolor + 0.7 1 0.9 +submean + on +position + +tag + vef +children + +scale + on +mocap + +selectcommand + ve_eegplot('defdowncom',gcbf); + ve_eegplot('defmotioncom',gcbf); + ve_eegplot('defupcom', gcbf); +altselectcommand + ve_edit('quick_chanflag','manual'); + ve_eegplot('defmotioncom', gcbf); + +extselectcommand + ve_edit; + ve_eegplot('defmotioncom', gcbf); + +keyselectcommand + t,ve_eegplot('topoplot',gcbf) + r,ve_eegplot('drawp',0) + a,ve_edit('qcf','ambig') + m,ve_edit('awm','manual') + M,ve_edit('rwm','manual') + s,ve_edit('sm','on'); +mouse_data_front + off +trialstag + +datastd + +normed + +envelope + +chaninfo + \ No newline at end of file diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/vised_config_qclite.cfg b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/vised_config_qclite.cfg new file mode 100755 index 000000000..29c26d040 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/config/vised_config_qclite.cfg @@ -0,0 +1,99 @@ +pop_gui + on +data_type + ICA +winrej_marks_labels + manual +quick_evtmk + +quick_evtrm + off +quick_chanflag + off +chan_marks_struct + +time_marks_struct + EEG.marks.time_info +marks_y_loc + 0.8 +inter_mark_int + 0.04 +inter_tag_int + 0.002 +marks_col_int + 0.1 +marks_col_alpha + 0.4 +spacing + 12 +eloc_file + +limits + +freqlimits + +winlength + 20 +dispchans + +title + +xgrid + off +ygrid + off +ploteventdur + off +data2 + +command + EEG=ve_update(EEG);EEG.saved = 'no'; +butlabel + Update EEG structure +color + +wincolor + 0.7 1 0.9 +submean + on +position + +tag + vef +children + +scale + on +mocap + +selectcommand + ve_eegplot('defdowncom',gcbf); + ve_eegplot('defmotioncom',gcbf); + ve_eegplot('defupcom', gcbf); +altselectcommand + ve_edit('quick_chanflag','manual'); + ve_eegplot('defmotioncom', gcbf); + +extselectcommand + ve_edit; + ve_eegplot('defmotioncom', gcbf); + +keyselectcommand + t,ve_eegplot('topoplot',gcbf) + r,ve_eegplot('drawp',0) + a,ve_edit('qcf','ambig') + m,ve_edit('awm','manual') + M,ve_edit('rwm','manual') + s,ve_edit('sm','on'); +mouse_data_front + off +trialstag + +datastd + +normed + +envelope + +chaninfo + diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/dependencies/README.md b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/dependencies/README.md new file mode 100644 index 000000000..2c3bb41b8 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/dependencies/README.md @@ -0,0 +1,5 @@ +# The Lossless Pipeline - Dependencies +Contains packages that the pipeline are dependent on. All folders within should be their own git repository, with their own documentation. + +# Documentation +All documentation including installation instructions is stored on the [Wiki](https://git.sharcnet.ca/bucanl_pipelines/bids_lossless_eeg/wikis/home) \ No newline at end of file diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/dependencies/matlog/CHANGELOG b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/dependencies/matlog/CHANGELOG new file mode 100644 index 000000000..ccfaeb962 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/dependencies/matlog/CHANGELOG @@ -0,0 +1,4 @@ +2017-07-25 Mae Kennedy + + * CHANGELOG: Change changelog format to actually contain changes + ensure you follow the format and put newer changes above this one \ No newline at end of file diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/dependencies/matlog/LICENSE b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/dependencies/matlog/LICENSE new file mode 100644 index 000000000..23cb79033 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/dependencies/matlog/LICENSE @@ -0,0 +1,339 @@ + GNU GENERAL PUBLIC LICENSE + Version 2, June 1991 + + Copyright (C) 1989, 1991 Free Software Foundation, Inc., + 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The licenses for most software are designed to take away your +freedom to share and change it. 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See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License along + with this program; if not, write to the Free Software Foundation, Inc., + 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. + +Also add information on how to contact you by electronic and paper mail. + +If the program is interactive, make it output a short notice like this +when it starts in an interactive mode: + + Gnomovision version 69, Copyright (C) year name of author + Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. Of course, the commands you use may +be called something other than `show w' and `show c'; they could even be +mouse-clicks or menu items--whatever suits your program. + +You should also get your employer (if you work as a programmer) or your +school, if any, to sign a "copyright disclaimer" for the program, if +necessary. Here is a sample; alter the names: + + Yoyodyne, Inc., hereby disclaims all copyright interest in the program + `Gnomovision' (which makes passes at compilers) written by James Hacker. + + {signature of Ty Coon}, 1 April 1989 + Ty Coon, President of Vice + +This General Public License does not permit incorporating your program into +proprietary programs. If your program is a subroutine library, you may +consider it more useful to permit linking proprietary applications with the +library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/dependencies/matlog/README.md b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/dependencies/matlog/README.md new file mode 100644 index 000000000..6e3ca38a7 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/dependencies/matlog/README.md @@ -0,0 +1,37 @@ +# matlog - a Matlab and Octave compatible logging library + +## Notes +### `disp` override +Using the below command you can modify the behaviour of the interpreter to +always use logging_log as disp +``` +logging_override_disp(true/false) +``` +### Usage +See help for each file +``` +help + +fname is one of +logging_fcallstack logging_ffileline logging_log +logging_fcallstack_format logging_override_disp +``` + +## License +matlog - a Matlab and Octave compatible logging library + +Copyright (C) 2016 Mae Kennedy + +This program is free software; you can redistribute it and/or +modify it under the terms of the GNU General Public License +as published by the Free Software Foundation; either version 2 +of the License, or (at your option) any later version. + +This program is distributed in the hope that it will be useful, +but WITHOUT ANY WARRANTY; without even the implied warranty of +MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +GNU General Public License for more details. + +You should have received a copy of the GNU General Public License +along with this program; if not, write to the Free Software +Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/dependencies/matlog/VERSION b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/dependencies/matlog/VERSION new file mode 100644 index 000000000..60453e690 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/dependencies/matlog/VERSION @@ -0,0 +1 @@ +v1.0.0 \ No newline at end of file diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/dependencies/matlog/disp_wrapper/disp.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/dependencies/matlog/disp_wrapper/disp.m new file mode 100644 index 000000000..1fb0ac320 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/dependencies/matlog/disp_wrapper/disp.m @@ -0,0 +1,30 @@ +% disp() - This file overrides disp with our custom logging function +% use logging_override_disp to disable or enable this, do not add +% this folder to path directly +% Usage: +% >> disp(data) +% +% Inputs: +% data - The data to display formatted + +% matlog - a Matlab and Octave compatible logging library +% Copyright (C) 2017 Mae Kennedy +% +% This program is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License +% as published by the Free Software Foundation; either version 2 +% of the License, or (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA +% 02110-1301, USA. + +function disp(data) + logging_log('INFO', data); +end diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/dependencies/matlog/logging_fcallstack.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/dependencies/matlog/logging_fcallstack.m new file mode 100644 index 000000000..5d6ec9b19 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/dependencies/matlog/logging_fcallstack.m @@ -0,0 +1,84 @@ +% logging_fcallstack() - Return the current stack trace as a string +% formatted with the provided format string and +% starting with the preamble +% Usage: +% >> output = logging_fcallstack(message, formatstr, unroll, stack) +% +% Inputs: +% message - The message to print before the stack trace +% formatstr - formatstr the string taking two strings and a number +% for each level of the stack +% unroll - Number of stack frames to unroll before printing, needed +% because we always would include the logging functions +% which arn't useful +% error - Use this stack, not dbstack() +% +% Outputs: +% output - a string containing call stack +% +% Examples: +% output = logging_fcallstack() +% returns string with defaults +% output = logging_fcallstack('Stack trace:') +% returns the string with this prefix message +% output = logging_fcallstack('ST:\n', '\tat %s(%s:%d)\n') +% returns the stack with each line formatted with the format string +% output = logging_fcallstack('ST:\n', '\tat %s(%s:%d)\n', 2) +% returns the stack with each line formatted with the format string +% and unrolls two levels of the stack instead of one + +% matlog - a Matlab and Octave compatible logging library +% Copyright (C) 2016 Mae Kennedy +% +% This program is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License +% as published by the Free Software Foundation; either version 2 +% of the License, or (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA +% 02110-1301, USA. +function output = logging_fcallstack(messagen, formatstr, unroll, lerror) + global CALL_STACK_FORMAT; + if ~exist('formatstr', 'var') || (exist('formatstr', 'var') ... + && isempty(formatstr)) + formatstr = '\tat %s(%s:%d)\n'; + end + if ~isempty(CALL_STACK_FORMAT) + formatstr = CALL_STACK_FORMAT; + end + if ~exist('messagen', 'var') || isempty(messagen) + messagen = 'Stack trace:\n'; + end + if ~exist('unroll', 'var') || isempty(unroll) + unroll = 1; + end + output = ''; + if ~exist('lerror', 'var') + stack = dbstack(); + else + stack = lerror.stack; + output = sprintf('ERROR: %s\n', lerror.message); + unroll = unroll - 1; + end + output = [output sprintf(messagen)]; + % CHECK: should this be < or <= + if numel(stack) < unroll+1 + return; + end + + stack = stack(unroll+1:end); + + for i = 1:numel(stack) + level = stack(i); + output = [output sprintf(formatstr, level.file, level.name, ... + level.line)]; + end +end + diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/dependencies/matlog/logging_fcallstack_format.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/dependencies/matlog/logging_fcallstack_format.m new file mode 100644 index 000000000..6999ae493 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/dependencies/matlog/logging_fcallstack_format.m @@ -0,0 +1,33 @@ +% logging_fcallstack_format() - +% Usage: +% >> logging_fcallstack_format(formatstr) +% +% Inputs: +% formatstr - Set the formatting for logging_fcallstack +% sets CALL_STACK_FORMAT + +% matlog - a Matlab and Octave compatible logging library +% Copyright (C) 2017 Mae Kennedy +% +% This program is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License +% as published by the Free Software Foundation; either version 2 +% of the License, or (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA +% 02110-1301, USA. +function logging_fcallstack_format(formatstr) + global CALL_STACK_FORMAT; + if nargin < 1 + help logging_fcallstack_format; + return; + end + CALL_STACK_FORMAT = formatstr; +end diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/dependencies/matlog/logging_ffileline.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/dependencies/matlog/logging_ffileline.m new file mode 100644 index 000000000..7df67863e --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/dependencies/matlog/logging_ffileline.m @@ -0,0 +1,56 @@ +% logging_ffileline() - return a string containing the file and line +% Usage: +% >> output = logging_ffileline(formatstr, unroll) +% +% Inputs: +% formatstr - format string containing string and line number +% unroll - number of levels of recursion to unroll +% +% Outputs: +% output - formatted string to return +% +% Examples: +% output = strfileline() +% return the current file:line +% output = logging_ffileline('%s:%d') +% returns the file with the current line and column +% output = logging_ffileline('%s:%d', 2) +% returns the file with the current line and column +% but unrolls two lines of the stack not one + +% matlog - a Matlab and Octave compatible logging library +% Copyright (C) 2016 Mae Kennedy +% +% This program is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License +% as published by the Free Software Foundation; either version 2 +% of the License, or (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA +% 02110-1301, USA. +function output = logging_ffileline(formatstr, unroll) + if ~exist('formatstr') || (exist('formatstr') && formatstr == 0) + formatstr = '%s:%d'; + end + if ~exist('unroll') + unroll = 1; + end + stack = dbstack(); + if numel(stack) <= unroll + output = 'HEAD'; + return; + end + stack = stack(unroll+1); + if ~exist('stack.column') + stack.column = ''; + end + output = sprintf(formatstr, stack.name, stack.line, stack.column); +end + diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/dependencies/matlog/logging_log.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/dependencies/matlog/logging_log.m new file mode 100644 index 000000000..5a231ae32 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/dependencies/matlog/logging_log.m @@ -0,0 +1,109 @@ +% logging_log() - logging_log logs messages if required by the current_level +% or LOG_LEVEL current_level superceeds log_level. if neither +% are set 'NOTICE' is used levels are ERROR, WARN, NOTICE, +% INFO, DEBUG from least verbose to most by default warning +% and below print the stack trace +% +% Usage: +% >> logging_log(log_level, message, current_level) +% +% Inputs: +% log_level - the log level of this message, from one of the above levels +% message - the message that will be formatted with disp +% current_level - the log level of this message, from one of the above levels + +% matlog - a Matlab and Octave compatible logging library +% Copyright (C) 2016 Mae Kennedy +% +% This program is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License +% as published by the Free Software Foundation; either version 2 +% of the License, or (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA +% 02110-1301, USA. +function logging_log(log_level, message, current_level) + format = '%s:\t%s\t%f\t%f\t%s\t%s'; + persistent clockstart; + persistent last; + global LOGGING_LEVEL; + if isempty(clockstart) + clockstart = clock(); + last = clock(); + end + + levelc = 'INFO'; % Default level + stack_level = 'WARN'; % Level for stack trace to be included, WARN is best + % All levels ordered from least verbose to most + levels = {'ERROR', 'WARN', 'NOTICE', 'INFO', 'DEBUG'}; + if exist('current_level', 'var') + levelc = current_level; + elseif exist('LOGGING_LEVEL', 'var') && ~isempty(LOGGING_LEVEL) + levelc = LOGGING_LEVEL; + end + + % Check if we are being called from disp, if so, unroll the stack twice + % if not, just use 1 for our line & stack functions unrolling parameter + db = dbstack(); + unroll = 2; + if numel(db) >= 2 && strcmp(db(2).name, 'disp') + unroll = 3; + end + where = logging_ffileline(0, unroll); + + % Verify it is one of our levels + difftime = etime(clock(), last); + if ~any(strcmp(levels,levelc)) + builtin('disp', sprintf(format, 'ERROR', bucanl_get_iso8601(), ... + bucanl_get_clocktime(clockstart), difftime, ... + where, ... + ['Level specified to logging_log ' ... + 'does not exist, check LOG_LEVEL and current_level ' ... + 'and verify that it is one of '])); + builtin('disp', levels); + + return + end + + + % Write the message with the formating if log_level is greater than the + % current log level in current_level or LOG_LEVEL + if exist('evalc', 'builtin') + fmessage = evalc('builtin(''disp'', message)'); + else + % Polyfill for octave >=4.0 + % This may also be triggered by matlab without evalc and will fail + try + fmessage = builtin('disp', message); + catch + % Give up and just try to interpret the message as a string + fmessage = sprintf('%s', message); + end + end + if find(strcmp(levels, levelc), 1) >= find(strcmp(levels, log_level), 1) + builtin('disp', sprintf(format, log_level, bucanl_get_iso8601(), ... + bucanl_get_clocktime(clockstart), difftime, where, fmessage)); + % If the level is greater or equal to warn print stack trace + if find(strcmp(levels, stack_level), 1) >= ... + find(strcmp(levels, log_level), 1) + builtin('disp', logging_fcallstack([], [], unroll)); + end + last = clock(); + end +end + +% Returns a formatted iso8601 datetime without timezone +function time = bucanl_get_iso8601() + time = datestr(now, 'yyyy-mm-ddTHH:MM:SS'); +end +% Returns time difference between now and the clockstart in seconds +function timediff = bucanl_get_clocktime(clockstart) + timediff = etime(clock(), clockstart); +end diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/dependencies/matlog/logging_override_disp.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/dependencies/matlog/logging_override_disp.m new file mode 100644 index 000000000..67eaf5860 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/dependencies/matlog/logging_override_disp.m @@ -0,0 +1,49 @@ +% logging_override_disp() - Return the current stack trace as a string +% formatted with the provided format string and +% starting with the preamble +% Usage: +% >> logging_override_disp(display) +% +% Inputs: +% display - true|false if you want to override disp function + +% matlog - a Matlab and Octave compatible logging library +% Copyright (C) 2017 Mae Kennedy +% +% This program is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License +% as published by the Free Software Foundation; either version 2 +% of the License, or (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA +% 02110-1301, USA. +function logging_override_disp(display) + global LOGGING_OVERRIDE_DISP; + if nargin < 1 || ~islogical(display) + help logging_override_disp; + return; + end + if LOGGING_OVERRIDE_DISP ~= display + LOGGING_OVERRIDE_DISP = display; + this_file = which(mfilename()); + [this_dir, ~, ~] = fileparts(this_file); + disp_wrapper_path = fullfile(this_dir, 'disp_wrapper'); + % Ignorance is bliss + warning('off','MATLAB:dispatcher:nameConflict') + if LOGGING_OVERRIDE_DISP + % Turn on + addpath(disp_wrapper_path); + else + % Turn off + rmpath(disp_wrapper_path); + end + warning('on','MATLAB:dispatcher:nameConflict') + end +end diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/dependencies/matlog/logging_set_log_level.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/dependencies/matlog/logging_set_log_level.m new file mode 100644 index 000000000..acba77b37 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/dependencies/matlog/logging_set_log_level.m @@ -0,0 +1,33 @@ +% logging_set_log_level() - sets the logging level of logging_log messages +% to the provided level, if the level doesn't exist +% the behaviour is undefined +% +% Usage: +% >> logging_set_log_level(strlevel) +% +% Inputs: +% strlevel - maximum level to log char type + +% matlog - a Matlab and Octave compatible logging library +% Copyright (C) 2016 Mae Kennedy +% +% This program is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License +% as published by the Free Software Foundation; either version 2 +% of the License, or (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA +% 02110-1301, USA. + +function logging_set_log_level(strlevel) + global LOGGING_LEVEL; + LOGGING_LEVEL = strlevel; +end + diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/dependencies/matlog/private/stackTest.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/dependencies/matlog/private/stackTest.m new file mode 100644 index 000000000..cfc7b1ed6 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/dependencies/matlog/private/stackTest.m @@ -0,0 +1,21 @@ + +% Make this a script +1; +function res = logging_genStackTest(v) + if v > 4 + res = strfcallstack(); + return; + end + res = logging_genStackTest(v+1); + return; + +% Check that genStackTest produces the preamble +v = logging_genStackTest(1); +teststr = sprintf('Stack trace:\n\tat'); +assert(v(1:16) == teststr); + +% Check that strffileline +% produces a file line +res = logging_ffileline(); +assert(res(1:10)=='stackTest:'); + diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/install/lossless_path.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/install/lossless_path.m new file mode 100644 index 000000000..3ea8f587b --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/install/lossless_path.m @@ -0,0 +1,35 @@ +% This script sets the path to include the dependencies subdirectories + +% Copyright (C) 2017 Brock University Cognitive and Affective Neuroscience Lab +% +% Code written by Brad Kennedy +% +% This program is free software; you can redistribute it and/or modify +% it under the terms of the GNU General Public License as published by +% the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program (LICENSE.txt file in the root directory); if not, +% write to the Free Software Foundation, Inc., 59 Temple Place, +% Suite 330, Boston, MA 02111-1307 USA + +%if ~exist('derivatives/lossless/code/dependencies/eeglab_lossless', 'dir') ... +% || ~exist('derivatives/lossless/code/dependencies/matlog', 'dir') +% error(['derivatives/lossless/code/dependencies does not not contain ' ... +% 'eeglab_lossless and/or matlog, or is not readable, or ' ... +% 'your current folder in matlab is not the root of your ' ... +% 'project']); +%end +fileName = mfilename('fullpath'); +pathPrefix = fileparts(fileName); + +addpath([pathPrefix '/../dependencies/eeglab_lossless']) +addpath([pathPrefix '/../dependencies/matlog']) +addpath([pathPrefix '/../scripts']) +addpath([pathPrefix '/../tools/diagnostics']) diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/install/setup-remote.sh b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/install/setup-remote.sh new file mode 100755 index 000000000..66bd63449 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/install/setup-remote.sh @@ -0,0 +1,140 @@ +#!/bin/bash + +set -u +DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" +functionsource="$DIR/../misc/bashfunc" + +cat </dev/null 2>&1 && pwd )" +lockdir="$DIR/locks" + +# Colours +B='\033[0;34m' +R='\033[0;31m' +G='\033[0;32m' +Y='\033[0;33m' +# No colour +NC='\033[0m' + +function bold { + printf "${B}$1${NC}" +} + +function good { + printf "${G}$1${NC}\n" +} + +function error_msg { + printf "${R}Error: ${NC}$1\n" +} +function test_lockset { + mkdir -p "$lockdir" + [ -f "$lockdir/$1.lock" ] + return $? +} + +function set_lockset { + touch "$lockdir/$1.lock" +} + +function checkset { + if test_lockset $1 ; then + printf "${Y}WARN:${NC}" + cat < /dev/null 2>&1 + isinstalled=$? + if [ 0 -eq "$isinstalled" ] ; then + echo "$1 is installed" + return 0 + fi + bold "INFO:" ; echo "$1 is not available, attempting to install it" + bold "INFO:" ; echo "This will take some time, please wait patiently" + cd "../dependencies/Octave_pkg" + + octave --eval "`cat < /dev/null 2>&1 + wasinstalled=$? + cd - + if [ "$wasinstalled" -eq 0 ] ; then + bold "INFO:"; echo "$1 installed correctly" + return 0 + else + printf "${R}Error:${NC}" + echo "$1 failed to install from `pwd`" + exit 1 + fi +} +function test_which { + if ! [ "$(which $1 2>/dev/null || true)" ] ; then + printf "${R}Error:${NC}" + cat <&2 + echo "We need to load session environment variables for" >&2 + echo "$1" >&2 + bold "Prompt:" >&2 + echo "Select a file containing environment variables" >&2 + bold "Note:" >&2 + echo "You may need to select a different version based on your scheduler" >&2 + filen="" + while ! [ -f "$filen" ]; do + read -e -p "File name: [../config/$1.sesinit]: " filen + if [ -z "$filen" ] ; then + filen="../config/$1.sesinit" + fi + if [ -f "$filen" ]; then + echo "$filen" + return + fi + done +} diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/misc/standard_1005.elc b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/misc/standard_1005.elc new file mode 100644 index 000000000..4e695328b --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/misc/standard_1005.elc @@ -0,0 +1,698 @@ +# ASA electrode file +ReferenceLabel avg +UnitPosition mm +NumberPositions= 346 +Positions +-86.0761 -19.9897 -47.9860 +85.7939 -20.0093 -48.0310 +0.0083 86.8110 -39.9830 +-29.4367 83.9171 -6.9900 +0.1123 88.2470 -1.7130 +29.8723 84.8959 -7.0800 +-48.9708 64.0872 -47.6830 +-54.8397 68.5722 -10.5900 +-45.4307 72.8622 5.9780 +-33.7007 76.8371 21.2270 +-18.4717 79.9041 32.7520 +0.2313 80.7710 35.4170 +19.8203 80.3019 32.7640 +35.7123 77.7259 21.9560 +46.5843 73.8078 6.0340 +55.7433 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65.6240 +-35.5131 -47.2919 91.3150 +0.3858 -47.3180 99.4320 +38.3838 -47.0731 90.6950 +66.6118 -46.6372 65.5800 +83.3218 -46.1013 31.2060 +85.5488 -45.5453 -7.1300 +86.1618 -47.0353 -45.8690 +-73.0093 -73.7657 -40.9980 +-72.4343 -73.4527 -2.4870 +-67.2723 -76.2907 28.3820 +-53.0073 -78.7878 55.9400 +-28.6203 -80.5249 75.4360 +0.3247 -81.1150 82.6150 +31.9197 -80.4871 76.7160 +55.6667 -78.5602 56.5610 +67.8877 -75.9043 28.0910 +73.0557 -73.0683 -2.5400 +73.8947 -74.3903 -41.2200 +-54.9104 -98.0448 -35.4650 +-54.8404 -97.5279 2.7920 +-48.4244 -99.3408 21.5990 +-36.5114 -100.8529 37.1670 +-18.9724 -101.7680 46.5360 +0.2156 -102.1780 50.6080 +19.8776 -101.7930 46.3930 +36.7816 -100.8491 36.3970 +49.8196 -99.4461 21.7270 +55.6666 -97.6251 2.7300 +54.9876 -98.0911 -35.5410 +-29.4134 -112.4490 8.8390 +0.1076 -114.8920 14.6570 +29.8426 -112.1560 8.8000 +-29.8184 -114.5700 -29.2160 +0.0045 -118.5650 -23.0780 +29.7416 -114.2600 -29.2560 +-43.2897 75.8552 -28.2440 +-38.5517 79.9532 -4.9950 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+-80.1330 2.5853 27.3120 +-64.1610 5.8313 60.8850 +-35.7490 8.3091 85.4590 +0.3911 9.5080 95.5600 +36.0700 8.6519 83.8320 +65.1640 6.6198 60.0520 +81.5440 3.6637 27.2010 +83.1680 0.1817 -10.3640 +85.3930 -0.9523 -49.5200 +-86.6321 -31.2377 -47.1780 +-85.9331 -31.0927 -8.4740 +-81.5431 -30.1727 30.2730 +-66.1281 -29.2957 65.8980 +-36.9301 -28.5699 91.7340 +0.3959 -28.1630 101.2690 +38.5399 -28.2251 90.9760 +68.8539 -28.6403 66.4100 +84.5529 -29.3783 30.8780 +85.9999 -30.2803 -8.4350 +86.7619 -31.7313 -47.2530 +-80.7152 -60.6457 -43.5940 +-78.5992 -59.7237 -4.7580 +-73.6642 -61.9227 30.3800 +-59.4112 -63.9248 62.6720 +-32.7283 -65.3199 85.9440 +0.3658 -65.7500 94.0580 +35.8918 -65.1381 85.9800 +62.2558 -63.6152 62.7190 +76.6708 -61.5483 30.5430 +79.3188 -59.3033 -4.8400 +81.5598 -61.2153 -43.8000 +-64.5703 -86.4318 -38.3240 +-64.5833 -86.2218 0.0330 +-58.7123 -88.7048 25.1930 +-46.1603 -90.8878 47.4460 +-24.6483 -92.2919 62.0760 +0.2727 -92.7580 67.3420 +26.4367 -92.2951 63.1990 +47.1437 -90.7122 47.6780 +60.8127 -88.5042 25.6620 +65.1517 -85.9432 -0.0090 +65.0377 -86.7182 -38.4480 +-43.1284 -107.5160 -32.3870 +-42.9764 -106.4930 5.7730 +-36.2344 -107.7160 17.7500 +-25.9844 -108.6160 26.5440 +-13.6644 -109.2660 32.8560 +0.1676 -109.2760 32.7900 +13.6506 -109.1060 30.9360 +26.6636 -108.6680 26.4150 +37.7006 -107.8400 18.0690 +43.6696 -106.5990 5.7260 +43.1766 -107.4440 -32.4630 +-29.3914 -114.5110 -10.0200 +0.0525 -119.3430 -3.9360 +29.5526 -113.6360 -10.0510 +-84.1611 -16.0187 -9.3460 +-72.4343 -73.4527 -2.4870 +85.0799 -15.0203 -9.4900 +73.0557 -73.0683 -2.5400 +-86.0761 -44.9897 -67.9860 + 85.7939 -45.0093 -68.0310 +-86.0761 -24.9897 -67.9860 + 85.7939 -25.0093 -68.0310 +Labels +LPA +RPA +Nz +Fp1 +Fpz +Fp2 +AF9 +AF7 +AF5 +AF3 +AF1 +AFz +AF2 +AF4 +AF6 +AF8 +AF10 +F9 +F7 +F5 +F3 +F1 +Fz +F2 +F4 +F6 +F8 +F10 +FT9 +FT7 +FC5 +FC3 +FC1 +FCz +FC2 +FC4 +FC6 +FT8 +FT10 +T9 +T7 +C5 +C3 +C1 +Cz +C2 +C4 +C6 +T8 +T10 +TP9 +TP7 +CP5 +CP3 +CP1 +CPz +CP2 +CP4 +CP6 +TP8 +TP10 +P9 +P7 +P5 +P3 +P1 +Pz +P2 +P4 +P6 +P8 +P10 +PO9 +PO7 +PO5 +PO3 +PO1 +POz +PO2 +PO4 +PO6 +PO8 +PO10 +O1 +Oz +O2 +I1 +Iz +I2 +AFp9h +AFp7h +AFp5h +AFp3h +AFp1h +AFp2h +AFp4h +AFp6h +AFp8h +AFp10h +AFF9h +AFF7h +AFF5h +AFF3h +AFF1h +AFF2h +AFF4h +AFF6h +AFF8h +AFF10h +FFT9h +FFT7h +FFC5h +FFC3h +FFC1h +FFC2h +FFC4h +FFC6h +FFT8h +FFT10h +FTT9h +FTT7h +FCC5h +FCC3h +FCC1h +FCC2h +FCC4h +FCC6h +FTT8h +FTT10h +TTP9h +TTP7h +CCP5h +CCP3h +CCP1h +CCP2h +CCP4h +CCP6h +TTP8h +TTP10h +TPP9h +TPP7h +CPP5h +CPP3h +CPP1h +CPP2h +CPP4h +CPP6h +TPP8h +TPP10h +PPO9h +PPO7h +PPO5h +PPO3h +PPO1h +PPO2h +PPO4h +PPO6h +PPO8h +PPO10h +POO9h +POO7h +POO5h +POO3h +POO1h +POO2h +POO4h +POO6h +POO8h +POO10h +OI1h +OI2h +Fp1h +Fp2h +AF9h +AF7h +AF5h +AF3h +AF1h +AF2h +AF4h +AF6h +AF8h +AF10h +F9h +F7h +F5h +F3h +F1h +F2h +F4h +F6h +F8h +F10h +FT9h +FT7h +FC5h +FC3h +FC1h +FC2h +FC4h +FC6h +FT8h +FT10h +T9h +T7h +C5h +C3h +C1h +C2h +C4h +C6h +T8h +T10h +TP9h +TP7h +CP5h +CP3h +CP1h +CP2h +CP4h +CP6h +TP8h +TP10h +P9h +P7h +P5h +P3h +P1h +P2h +P4h +P6h +P8h +P10h +PO9h +PO7h +PO5h +PO3h +PO1h +PO2h +PO4h +PO6h +PO8h +PO10h +O1h +O2h +I1h +I2h +AFp9 +AFp7 +AFp5 +AFp3 +AFp1 +AFpz +AFp2 +AFp4 +AFp6 +AFp8 +AFp10 +AFF9 +AFF7 +AFF5 +AFF3 +AFF1 +AFFz +AFF2 +AFF4 +AFF6 +AFF8 +AFF10 +FFT9 +FFT7 +FFC5 +FFC3 +FFC1 +FFCz +FFC2 +FFC4 +FFC6 +FFT8 +FFT10 +FTT9 +FTT7 +FCC5 +FCC3 +FCC1 +FCCz +FCC2 +FCC4 +FCC6 +FTT8 +FTT10 +TTP9 +TTP7 +CCP5 +CCP3 +CCP1 +CCPz +CCP2 +CCP4 +CCP6 +TTP8 +TTP10 +TPP9 +TPP7 +CPP5 +CPP3 +CPP1 +CPPz +CPP2 +CPP4 +CPP6 +TPP8 +TPP10 +PPO9 +PPO7 +PPO5 +PPO3 +PPO1 +PPOz +PPO2 +PPO4 +PPO6 +PPO8 +PPO10 +POO9 +POO7 +POO5 +POO3 +POO1 +POOz +POO2 +POO4 +POO6 +POO8 +POO10 +OI1 +OIz +OI2 +T3 +T5 +T4 +T6 +M1 +M2 +A1 +A2 diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/misc/standard_1020.elc b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/misc/standard_1020.elc new file mode 100644 index 000000000..2f68b5147 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/misc/standard_1020.elc @@ -0,0 +1,200 @@ +# ASA electrode file +ReferenceLabel avg +UnitPosition mm +NumberPositions= 97 +Positions +-86.0761 -19.9897 -47.9860 +85.7939 -20.0093 -48.0310 +0.0083 86.8110 -39.9830 +-29.4367 83.9171 -6.9900 +0.1123 88.2470 -1.7130 +29.8723 84.8959 -7.0800 +-48.9708 64.0872 -47.6830 +-54.8397 68.5722 -10.5900 +-45.4307 72.8622 5.9780 +-33.7007 76.8371 21.2270 +-18.4717 79.9041 32.7520 +0.2313 80.7710 35.4170 +19.8203 80.3019 32.7640 +35.7123 77.7259 21.9560 +46.5843 73.8078 6.0340 +55.7433 69.6568 -10.7550 +50.4352 63.8698 -48.0050 +-70.1019 41.6523 -49.9520 +-70.2629 42.4743 -11.4200 +-64.4658 48.0353 16.9210 +-50.2438 53.1112 42.1920 +-27.4958 56.9311 60.3420 +0.3122 58.5120 66.4620 +29.5142 57.6019 59.5400 +51.8362 54.3048 40.8140 +67.9142 49.8297 16.3670 +73.0431 44.4217 -12.0000 +72.1141 42.0667 -50.4520 +-84.0759 14.5673 -50.4290 +-80.7750 14.1203 -11.1350 +-77.2149 18.6433 24.4600 +-60.1819 22.7162 55.5440 +-34.0619 26.0111 79.9870 +0.3761 27.3900 88.6680 +34.7841 26.4379 78.8080 +62.2931 23.7228 55.6300 +79.5341 19.9357 24.4380 +81.8151 15.4167 -11.3300 +84.1131 14.3647 -50.5380 +-85.8941 -15.8287 -48.2830 +-84.1611 -16.0187 -9.3460 +-80.2801 -13.7597 29.1600 +-65.3581 -11.6317 64.3580 +-36.1580 -9.9839 89.7520 +0.4009 -9.1670 100.2440 +37.6720 -9.6241 88.4120 +67.1179 -10.9003 63.5800 +83.4559 -12.7763 29.2080 +85.0799 -15.0203 -9.4900 +85.5599 -16.3613 -48.2710 +-85.6192 -46.5147 -45.7070 +-84.8302 -46.0217 -7.0560 +-79.5922 -46.5507 30.9490 +-63.5562 -47.0088 65.6240 +-35.5131 -47.2919 91.3150 +0.3858 -47.3180 99.4320 +38.3838 -47.0731 90.6950 +66.6118 -46.6372 65.5800 +83.3218 -46.1013 31.2060 +85.5488 -45.5453 -7.1300 +86.1618 -47.0353 -45.8690 +-73.0093 -73.7657 -40.9980 +-72.4343 -73.4527 -2.4870 +-67.2723 -76.2907 28.3820 +-53.0073 -78.7878 55.9400 +-28.6203 -80.5249 75.4360 +0.3247 -81.1150 82.6150 +31.9197 -80.4871 76.7160 +55.6667 -78.5602 56.5610 +67.8877 -75.9043 28.0910 +73.0557 -73.0683 -2.5400 +73.8947 -74.3903 -41.2200 +-54.9104 -98.0448 -35.4650 +-54.8404 -97.5279 2.7920 +-48.4244 -99.3408 21.5990 +-36.5114 -100.8529 37.1670 +-18.9724 -101.7680 46.5360 +0.2156 -102.1780 50.6080 +19.8776 -101.7930 46.3930 +36.7816 -100.8491 36.3970 +49.8196 -99.4461 21.7270 +55.6666 -97.6251 2.7300 +54.9876 -98.0911 -35.5410 +-29.4134 -112.4490 8.8390 +0.1076 -114.8920 14.6570 +29.8426 -112.1560 8.8000 +-29.8184 -114.5700 -29.2160 +0.0045 -118.5650 -23.0780 +29.7416 -114.2600 -29.2560 +-84.1611 -16.0187 -9.3460 +-72.4343 -73.4527 -2.4870 +85.0799 -15.0203 -9.4900 +73.0557 -73.0683 -2.5400 +-86.0761 -44.9897 -67.9860 + 85.7939 -45.0093 -68.0310 +-86.0761 -24.9897 -67.9860 + 85.7939 -25.0093 -68.0310 +Labels +LPA +RPA +Nz +Fp1 +Fpz +Fp2 +AF9 +AF7 +AF5 +AF3 +AF1 +AFz +AF2 +AF4 +AF6 +AF8 +AF10 +F9 +F7 +F5 +F3 +F1 +Fz +F2 +F4 +F6 +F8 +F10 +FT9 +FT7 +FC5 +FC3 +FC1 +FCz +FC2 +FC4 +FC6 +FT8 +FT10 +T9 +T7 +C5 +C3 +C1 +Cz +C2 +C4 +C6 +T8 +T10 +TP9 +TP7 +CP5 +CP3 +CP1 +CPz +CP2 +CP4 +CP6 +TP8 +TP10 +P9 +P7 +P5 +P3 +P1 +Pz +P2 +P4 +P6 +P8 +P10 +PO9 +PO7 +PO5 +PO3 +PO1 +POz +PO2 +PO4 +PO6 +PO8 +PO10 +O1 +Oz +O2 +O9 +Iz +O10 +T3 +T5 +T4 +T6 +M1 +M2 +A1 +A2 diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc new file mode 100644 index 000000000..c38918495 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc @@ -0,0 +1,44 @@ +# ASA electrode file +ReferenceLabel avg +UnitPosition mm +NumberPositions= 19 +Positions +0.1123 88.2470 -1.7130 +-54.8397 68.5722 -10.5900 +55.7433 69.6568 -10.7550 +-50.2438 53.1112 42.1920 +0.3122 58.5120 66.4620 +51.8362 54.3048 40.8140 +-80.7750 14.1203 -11.1350 +81.8151 15.4167 -11.3300 +-65.3581 -11.6317 64.3580 +0.4009 -9.1670 100.2440 +67.1179 -10.9003 63.5800 +-84.8302 -46.0217 -7.0560 +85.5488 -45.5453 -7.1300 +-53.0073 -78.7878 55.9400 +0.3247 -81.1150 82.6150 +55.6667 -78.5602 56.5610 +-54.8404 -97.5279 2.7920 +55.6666 -97.6251 2.7300 +0.1076 -114.8920 14.6570 +Labels +Fpz +AF7 +AF8 +F3 +Fz +F4 +FT7 +FT8 +C3 +Cz +C4 +TP7 +TP8 +P3 +Pz +P4 +PO7 +PO8 +Oz diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/misc/standard_mri.mat b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/misc/standard_mri.mat new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/misc/standard_vol.mat b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/misc/standard_vol.mat new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/misc/standard_vol_SCCN.mat b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/misc/standard_vol_SCCN.mat new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/README.md b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/README.md new file mode 100644 index 000000000..b37dbdc0d --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/README.md @@ -0,0 +1,2 @@ +# The Lossless Pipeline - Scripts +Folder containing all of the script files that make up processes behind the pipeline. These have the extension .htb and are called History Template Batch as they are executed in the batch_context extensions. \ No newline at end of file diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/face13_staging.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/face13_staging.m new file mode 100644 index 000000000..808fe0fd5 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/face13_staging.m @@ -0,0 +1,60 @@ +%FHBC_fade +EEG = pop_marks_event_gap(EEG,{'-f'},3000,'in_task_fhbc_fade',[.7 .7 1], ... + 'interval','off', ... + 'critdir','max', ... + 'exact','off', ... + 'offsets',[3000 -3000], ... + 'ref_point','both', ... + 'invert_flags','on'); +EEG = pop_marks_event_gap(EEG,{'-f'},3000,'in_leadup_fhbc_fade',[.4 .4 1], ... + 'interval','off', ... + 'critdir','max', ... + 'exact','off', ... + 'offsets',[-4000 4000], ... + 'ref_point','second', ... + 'invert_flags','off'); + +%FHBC_onset +EEG = pop_marks_event_gap(EEG,{'face-upright', ... + 'face-inverted', ... + 'house-upright', ... + 'house-inverted', ... + 'checker-left', ... + 'checker-right'}, ... + 3000,'in_task_fhbc_onset',[.7 1 .7], ... + 'interval','off', ... + 'critdir','max', ... + 'exact','on', ... + 'offsets',[3000 -3000], ... + 'ref_point','both', ... + 'invert_flags','on'); +EEG = pop_marks_event_gap(EEG,{'face-upright', ... + 'face-inverted', ... + 'house-upright', ... + 'house-inverted', ... + 'checker-left', ... + 'checker-right'}, ... + 3000,'in_leadup_fhbc_onset',[.4 1 .4], ... + 'interval','off', ... + 'critdir','max', ... + 'exact','on', ... + 'offsets',[-4000 4000], ... + 'ref_point','second', ... + 'invert_flags','off'); + +%mark out_task +gapflags=marks_label2index(EEG.marks.time_info,{'in_task'},'flags','exact','off'); +EEG.marks=marks_add_label(EEG.marks,'time_info',{'out_task',[0 0 0],~gapflags}); + +%MARK FLAG GAPS THAT ARE < 2 SECONDS... +EEG=pop_marks_flag_gap(EEG,{'manual','out_task'},2000,'mark_gap',[.2,.2,.2],'offsets',[0 0],'ref_point','both'); + +%COMBINE MARKS STRUCTURE INTO MANUAL FLAGS.... +EEG=pop_marks_merge_labels(EEG,'time_info',{'manual', ... + 'out_task', ... + 'mark_gap', ... + 'in_leadup_fhbc_fade', ... + 'in_leadup_fhbc_onset'}, ... + 'target_label','manual'); + + diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/fixMarks.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/fixMarks.m new file mode 100644 index 000000000..894159762 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/fixMarks.m @@ -0,0 +1,41 @@ +lossless_marks = load('derivatives/BIDS-Lossless-EEG/code/scripts/lossless_marks.mat'); + +for mark=1:length(lossless_marks.extra.chan_info) + id = 0; + for i=1:length(EEG.marks.chan_info) + if(strcmp(EEG.marks.chan_info(i).label,lossless_marks.extra.chan_info(mark).label)) + id = i; + end + end + if id + EEG.marks.chan_info(id).line_color = lossless_marks.extra.chan_info(mark).line_color; + EEG.marks.chan_info(id).tag_color = lossless_marks.extra.chan_info(mark).tag_color; + EEG.marks.chan_info(id).order = lossless_marks.extra.chan_info(mark).order; + end +end + +for mark=1:length(lossless_marks.extra.comp_info) + id = 0; + for i=1:length(EEG.marks.comp_info) + if(strcmp(EEG.marks.comp_info(i).label,lossless_marks.extra.comp_info(mark).label)) + id = i; + end + end + if id + EEG.marks.comp_info(id).line_color = lossless_marks.extra.comp_info(mark).line_color; + EEG.marks.comp_info(id).tag_color = lossless_marks.extra.comp_info(mark).tag_color; + EEG.marks.comp_info(id).order = lossless_marks.extra.comp_info(mark).order; + end +end + +for mark=1:length(lossless_marks.extra.time_info) + id = 0; + for i=1:length(EEG.marks.time_info) + if(strcmp(EEG.marks.time_info(i).label,lossless_marks.extra.time_info(mark).label)) + id = i; + end + end + if id + EEG.marks.time_info(id).color = lossless_marks.extra.time_info(mark).color; + end +end \ No newline at end of file diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/ll_validate.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/ll_validate.m new file mode 100644 index 000000000..30b0ffaa5 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/ll_validate.m @@ -0,0 +1,14 @@ +function valid = ll_validate(EEG) + valid = false; + + [fA, fB] = uigetfile('.','Select an EDF file','*.edf'); + edfPath = [fB fA]; + EEG = pop_bidsload(edfPath); + [EEG, transform] = warp_locs( EEG,'derivatives/BIDS-Lossless-EEG/code/misc/standard_1005.elc', ... + 'mesh', 'derivatives/BIDS-Lossless-EEG/code/misc/standard_vol.mat', ... + 'transform',[0 0 0 0 0 0 1 1 1], ... + 'manual','on'); + disp('[montage_info] for c01_scalpart.cfg:'); + out = sprintf('%4.3f ',transform); + disp(out); +end \ No newline at end of file diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/lossless_marks.mat b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/lossless_marks.mat new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/qc.htb b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/qc.htb new file mode 100644 index 000000000..bd94ff40a --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/qc.htb @@ -0,0 +1,99 @@ +%LOAD BIDS FILES +sphereLoc = '[batch_dfp]/[batch_dfn,_,-1]_icasphere.tsv' +weightsLoc = '[batch_dfp]/[batch_dfn,_,-1]_icaweights.tsv' +annoFile = '[batch_dfp]/[batch_dfn,_,-1]_annotations.tsv' + +elecFile = './[batch_dfn,_,-1]_electrodes.tsv' +eventsFile = './[batch_dfn,_,-1]_events.tsv' + +EEG = pop_bidsload('[batch_dfp]/[batch_dfn]','elecLoc',elecFile,'eventLoc',eventsFile,'icaSphere',sphereLoc,'icaWeights',weightsLoc,'annoLoc',annoFile); + +% Update color and flag information with fix script: +fixMarks; + +% Load extra ICLabel inforation from via non-bids method +tmp = load('[batch_dfp]/[batch_dfn,_,-1]_iclabel.mat'); +EEG.etc.ic_classification = tmp.tmp; % Octave makes this a bit strange... +EEG.etc.ic_classification.ICLabel.classifications = real(EEG.etc.ic_classification.ICLabel.classifications); + +bfh=figure; +bar(EEG.etc.ic_classification.ICLabel.classifications,'stacked','Horizontal','on'); +colormap(jet); +axis 'tight'; +legend({ 'brain','muscle','eye','heart','line_noise','chan_noise','other'}); +view(180,90); +set(gca,'ytick',[1:min(size(EEG.icasphere))]) +set(bfh,'Position',[1986 1 348 973]); + +%PLOT CHAN DATA SCROLL PLOT USING VISED PLUGIN... +EEG_scalp = pop_vised(EEG,'data_type','EEG', ... + 'pop_gui','off', ... + 'event_type',[], ... + 'spacing',80, ... + 'command','', ... + 'tag','ve_eeg', ... + 'keyselectcommand',{'t,ve_eegplot(''topoplot'',gcbf)';'r,ve_eegplot(''drawp'',0)';'o,qc_ovl_tog';'u,qc_ovl_upd'}, ... + 'dispchans',32); + +sfh = gcf; +set(sfh,'Position',[66 1 1484 1003]); + +%PLOT FULL DURATION NON-MANUAL ICAACT SURFACE PLOT +%notman_pnts = marks_label2index(EEG.marks.time_info,{'manual'},'indexes','invert','on'); +%normact=zeros(size(EEG.icaact(:,notman_pnts))); +fullnormact=zeros(size(EEG.icaact)); + +%sortnormact=zeros(size(EEG.icaact(:,notman_pnts))); +for i=1:min(size(EEG.icaweights)); + %normact(i,:)=(EEG.icaact(i,notman_pnts)-min(EEG.icaact(i,notman_pnts)))/(max(EEG.icaact(i,notman_pnts))-min(EEG.icaact(i,notman_pnts))); + fullnormact(i,:) = (EEG.icaact(i,:)-min(EEG.icaact(i,:)))/(max(EEG.icaact(i,:))-min(EEG.icaact(i,:))); + %sortnormact(i,:)=sort(normact(i,:)); +end + +%nash=figure; +%surf(normact,'linestyle','none');axis('tight');view(0,270); +%set(nash,'Position',[1550 1 371 973]); + +fullnash=figure; +surf(fullnormact,'linestyle','none');axis('tight');view(0,270); +set(fullnash,'Position',[1550 1 371 973]); + +%snash=figure; +%surf(sortnormact,'linestyle','none');axis('tight');view(0,270); +%set(snash,'Position',[1567 1 371 973]); + +%PLOT IC DATA SCROLL PLOT USING VISED PLUGIN... +EEG = pop_vised(EEG,'data_type','ICA','children','ve_eeg','pop_gui','off','tag','ve_ica'); +cfh = gcf; +set(cfh,'Position',[2332 1 1509 1003]); + +%PLOT IC TOPOGRAPHIES... +%pop_topoplot(EEG,0, [1:min(size(EEG.icawinv))] , 'qc',0,1, 'electrodes', 'off', 'masksurf', 'on'); +pop_viewprops(EEG,0,[1:min(size(EEG.icaweights))],{'freqrange',[1,100]}); + +tic; + +%WAIT FOR SCROLL PLOT TO CLOSE... +uiwait(cfh); + +qc_time = num2str(toc); +disp(['Time taken to perform QC: ' qc_time 'sec.']); + +%CLOSE/CLEAR TOPOGRAPHY PLOT HANDLES +tfh = findobj('Type', 'figure'); +close(tfh(find(strncmp('View components properties - pop_viewprops() (dataset:',get(tfh,'Name'),54)))); % backwards compatible with MATLAB 2012 + +%CLOSE/CLEAR ICLABEL BAR GRAPH +try close(bfh),clear bfh, catch clear bfh, end +try close(fullnash),clear fullnash, catch clear fullnash, end +%try close(nash),clear nash, catch clear nash, end +%try close(snash),clear snash, catch clear snash, end + +%CLOSE/CLEAR EEG SCROLL PLOT HANDLES +try close(sfh),clear sfh, catch clear sfh, end + +% SAVE *_qc.SET DATASET FILE... +% EEG = pop_saveset( EEG,'filename','[batch_dfn,_,-1]_qcr.set','filepath','[batch_dfp]'); + +% BIDS - Annotation export only +export_anno(EEG,'[batch_dfp]/[batch_dfn]','qc'); diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/qc_init.htb b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/qc_init.htb new file mode 100644 index 000000000..885cc579e --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/qc_init.htb @@ -0,0 +1,155 @@ +%LOAD DATASET FILE... +EEG = pop_loadset('filename','[batch_dfn]','filepath','[batch_dfp]'); + +if ~find(strcmpi({EEG.marks.chan_info.label},'ch_s_sd')); + EEG.marks=marks_add_label(EEG.marks,'chan_info',{'ch_s_sd',[1,0,0],[]}); +end; + +EEG = eeg_checkset(EEG); + +% CHECK FOR IC CLASSIFICATION MARKS +%% Use the ICLabel plugin +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% ICLabel is a database style plugin that classifies ICA components into the +% following seven categories + +% (Brain)(Muscle)(Eye)(Heart)(Line Noise)(Channel Noise)(Other) +% ( 1 )( 2 )( 3 )( 4 )( 5 )( 6 )( 7 ) + +% Colour structure and groupings are as follows: +% ---------- +% Brain -> [0, 0.4, 0] +% ---------- +% Muscle -> [0.4, 0, 0] +% Eye -> [0.8, 0, 0] +% Heart -> [0.8, 0.4, 0] +% ---------- +% Line Noise -> [0.2, 0.4, 0.8] +% Channel noise -> [0.4, 0, 0.8] +% ---------- +% Other -> [0.8, 0.8, 0] +% ---------- + +% Create lookup table of the above information. +pairs = {'brain',[0, 0.4, 0]; +'muscle',[0.4,0,0]; +'eye',[0.8,0,0]; +'heart',[0.8,0.4,0]; +'line_noise',[0.2,0.4,0.8]; +'chan_noise',[0.4,0,0.8]; +'other',[.7 .4 0]}; +lookupTable = cell2struct(pairs, {'name', 'colour'}, 2); + +EEG = pop_marks_select_data(EEG,'time marks',[],'labels',{'manual'},'remove','on'); + +% Run ICLabel +%EEG = iclabel(EEG); + +% extract features +disp 'ICLabel: extracting features...' +features = ICL_feature_extractor(EEG, 40); + +% run ICL +disp 'ICLabel: calculating labels...' +labels = run_ICL('default',features{:}); + +% save into EEG +disp 'ICLabel: saving results...' +EEG.etc.ic_classification.ICLabel.classes = ... + {'Brain', 'Muscle', 'Eye', 'Heart', 'Line', 'Channel', 'Other'}; +EEG.etc.ic_classification.ICLabel.classifications = labels; + +tmp=EEG.etc.ic_classification; + +EEG = pop_loadset('filename','[batch_dfn]','filepath','[batch_dfp]'); +EEG = eeg_checkset(EEG); + +EEG.etc.ic_classification=tmp; + +% Extract labels from the structure. +rawLabels = zeros([1,length(EEG.etc.ic_classification.ICLabel.classifications)]); +for index = 1:length(EEG.etc.ic_classification.ICLabel.classifications) + [p,i] = max(EEG.etc.ic_classification.ICLabel.classifications(index,1:end-1)); + if p<.3; + i=7; + end + rawLabels(index) = i; +end + +%ICLabel Exporting to marks +%%%%%%%%%%%%%%%%%%%%%%%%%%%% +logging_log('INFO','UPDATE MARKS STRUCTURE WITH ICLABEL BRAIN COMPS...'); +for compStage = 1:7 + ICLabel_pass= zeros([1,length(rawLabels)]); + ICLabel_pass(find(rawLabels==compStage)) = 1; + ICLabel_pass = ICLabel_pass'; + EEG.marks=marks_add_label(EEG.marks,'comp_info', ... + {lookupTable(compStage).name,lookupTable(compStage).colour, ... + lookupTable(compStage).colour,1,ICLabel_pass}); +end +% make any brain components always on top: +EEG.marks.comp_info(1,find(strcmp({EEG.marks.comp_info.label},'brain'))).order=2; + + +%RESET/INITIATE CHAN MARKS... +manual_ind=find(strcmp('manual',{EEG.marks.chan_info.label})); + + +%RESET/INITIATE IC MARKS... +EEG.marks=marks_add_label(EEG.marks,'comp_info',{'ambig',[.3,.3,1],[.3,.3,1],3,zeros(min(size(EEG.icaweights)),1)}); +manual_ind=find(strcmp('manual',{EEG.marks.comp_info.label})); +ch_s_sd_ind=find(strcmp('ch_s_sd',{EEG.marks.chan_info.label})); +ch_sd_ind=find(strcmp('ch_sd',{EEG.marks.chan_info.label})); +low_r_ind=find(strcmp('low_r',{EEG.marks.chan_info.label})); +bridge_ind=find(strcmp('bridge',{EEG.marks.chan_info.label})); +rank_ind=find(strcmp('rank',{EEG.marks.chan_info.label})); +icrt_ind=find(strcmp('ic_rt',{EEG.marks.comp_info.label})); +brain_ind=find(strcmp('brain',{EEG.marks.comp_info.label})); +muscle_ind=find(strcmp('muscle',{EEG.marks.comp_info.label})); +eye_ind=find(strcmp('eye',{EEG.marks.comp_info.label})); +heart_ind=find(strcmp('heart',{EEG.marks.comp_info.label})); +line_noise_ind=find(strcmp('line_noise',{EEG.marks.comp_info.label})); +chan_noise_ind=find(strcmp('chan_noise',{EEG.marks.comp_info.label})); +other_ind=find(strcmp('other',{EEG.marks.comp_info.label})); +ambig_ind=find(strcmp('ambig',{EEG.marks.comp_info.label})); + +EEG.marks.comp_info(manual_ind).order=1; +if ~isempty(ch_s_sd_ind); % skip study-wide criteria if not used + EEG.marks.comp_info(ch_s_sd_ind).order=2; +end; +EEG.marks.comp_info(ch_sd_ind).order=2; +EEG.marks.comp_info(low_r_ind).order=2; +EEG.marks.comp_info(bridge_ind).order=2; +EEG.marks.comp_info(rank_ind).order=2; +EEG.marks.comp_info(icrt_ind).order=1; + +EEG.marks.comp_info(brain_ind).order=2; +EEG.marks.comp_info(muscle_ind).order=2; +EEG.marks.comp_info(eye_ind).order=2; +EEG.marks.comp_info(heart_ind).order=2; +EEG.marks.comp_info(line_noise_ind).order=2; +EEG.marks.comp_info(chan_noise_ind).order=2; +EEG.marks.comp_info(other_ind).order=2; +EEG.marks.comp_info(ambig_ind).order=3; + +bioa_inds = marks_label2index(EEG.marks.comp_info,{'muscle','eye','heart','chan_noise'},'indexes'); +EEG.marks.comp_info(manual_ind).flags=zeros(min(size(EEG.icaweights)),1); +EEG.marks.comp_info(manual_ind).flags(bioa_inds)=1; + +% BIDS Export calls - quick disabled varible below +bidsMode = 1; +if bidsMode + % Dot for path is on purpose. Don't worry - it's safe. + fName = '.[batch_dfp]/[batch_dfn,_,-1].edf'; + pop_writeeeg(EEG, fName, 'TYPE','EDF'); + dlmwrite(strrep(fName,'_eeg.edf','_icaweights.tsv'),{EEG.icaweights},'\t'); + dlmwrite(strrep(fName,'_eeg.edf','_icasphere.tsv'),{EEG.icasphere},'\t'); + s = {}; + s.algorithm = 'amica15'; + s.icachansind = EEG.icachansind; + s.intendedFor = fName; + savejson('',s,strrep(fName,'_eeg.edf','_icaweights.json')); + export_anno(EEG,fName,'ll'); +else + EEG = pop_saveset( EEG,'filename','[batch_dfn,_,-1]_qc.set','filepath','[batch_dfp]'); +end \ No newline at end of file diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/qc_lite.htb b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/qc_lite.htb new file mode 100755 index 000000000..1af4ce429 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/qc_lite.htb @@ -0,0 +1,88 @@ +%LOAD DATASET FILE... +EEG = pop_loadset('filename','[batch_dfn]','filepath','[batch_dfp]'); +EEG = eeg_checkset(EEG); + +screendims = get(0,'ScreenSize'); +xdim = screendims(3); +ydim = screendims(4); + +%bfh=figure; +%bar(EEG.etc.ic_classification.ICLabel.classifications,'stacked','Horizontal','on'); +%colormap(jet); +%axis 'tight'; +%legend({ 'brain','muscle','eye','heart','line_noise','chan_noise','other'}); +%view(180,90); +%set(gca,'ytick',[1:min(size(EEG.icasphere))]) +%set(bfh,'Position',[1986 1 348 973]); + +%PLOT CHAN DATA SCROLL PLOT USING VISED PLUGIN... +EEG_scalp = pop_vised(EEG,'data_type','EEG', ... + 'pop_gui','off', ... + 'event_type',[], ... + 'spacing',80, ... + 'winlength',20, ... + 'command','', ... + 'tag','ve_eeg', ... + 'keyselectcommand',{'t,ve_eegplot(''topoplot'',gcbf)';'r,ve_eegplot(''drawp'',0)';'o,qc_ovl_tog';'u,qc_ovl_upd'}, ... + 'dispchans',32); + +sfh = gcf; +set(sfh,'Position',[1 1 xdim/2 ydim]); + +%PLOT FULL DURATION NON-MANUAL ICAACT SURFACE PLOT +%notman_pnts = marks_label2index(EEG.marks.time_info,{'manual'},'indexes','invert','on'); +%normact=zeros(size(EEG.icaact(:,notman_pnts))); +%fullnormact=zeros(size(EEG.icaact)); + +%sortnormact=zeros(size(EEG.icaact(:,notman_pnts))); +%for i=1:min(size(EEG.icaweights)); + %normact(i,:)=(EEG.icaact(i,notman_pnts)-min(EEG.icaact(i,notman_pnts)))/(max(EEG.icaact(i,notman_pnts))-min(EEG.icaact(i,notman_pnts))); +% fullnormact(i,:) = (EEG.icaact(i,:)-min(EEG.icaact(i,:)))/(max(EEG.icaact(i,:))-min(EEG.icaact(i,:))); + %sortnormact(i,:)=sort(normact(i,:)); +%end + +%nash=figure; +%surf(normact,'linestyle','none');axis('tight');view(0,270); +%set(nash,'Position',[1550 1 371 973]); + +%fullnash=figure; +%surf(fullnormact,'linestyle','none');axis('tight');view(0,270); +%set(fullnash,'Position',[1550 1 371 973]); + +%snash=figure; +%surf(sortnormact,'linestyle','none');axis('tight');view(0,270); +%set(snash,'Position',[1567 1 371 973]); + +%PLOT IC DATA SCROLL PLOT USING VISED PLUGIN... +EEG = pop_vised(EEG,'data_type','ICA','children','ve_eeg','pop_gui','off','tag','ve_ica'); +cfh = gcf; +set(cfh,'Position',[xdim/2 1 xdim/2 ydim]); + +%PLOT IC TOPOGRAPHIES... +%pop_topoplot(EEG,0, [1:min(size(EEG.icawinv))] , 'qc',0,1, 'electrodes', 'off', 'masksurf', 'on'); +%pop_viewprops(EEG,0,[1:min(size(EEG.icaweights))],{'freqrange',[1,100]}); +pop_viewprops(EEG,0,[1:35],{'freqrange',[1,100]}); % view only the first 35 component topos + +tic; + +%WAIT FOR SCROLL PLOT TO CLOSE... +uiwait(cfh); + +qc_time = num2str(toc); +disp(['Time taken to perform QC: ' qc_time 'sec.']); + +%CLOSE/CLEAR TOPOGRAPHY PLOT HANDLES +tfh = findobj('Type', 'figure'); +close(tfh(find(strncmp('View components properties - pop_viewprops() (dataset:',get(tfh,'Name'),54)))); % backwards compatible with MATLAB 2012 + +%CLOSE/CLEAR ICLABEL BAR GRAPH +%try close(bfh),clear bfh, catch clear bfh, end +%try close(fullnash),clear fullnash, catch clear fullnash, end +%try close(nash),clear nash, catch clear nash, end +%try close(snash),clear snash, catch clear snash, end + +%CLOSE/CLEAR EEG SCROLL PLOT HANDLES +try close(sfh),clear sfh, catch clear sfh, end + +%SAVE *_qc.SET DATASET FILE... +EEG = pop_saveset( EEG,'filename','[batch_dfn,_,-1]_qcr.set','filepath','[batch_dfp]'); diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/qc_ovl_tog.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/qc_ovl_tog.m new file mode 100644 index 000000000..858710244 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/qc_ovl_tog.m @@ -0,0 +1,17 @@ +scalp_ud=get(findobj('Tag','ve_eeg'),'UserData'); + +if isempty(scalp_ud.data2) + comp_ud=get(findobj('Tag','ve_ica'),'UserData'); + comp_keep=~comp_ud.chan_marks_struct(1).flags; + + comp_proj=EEG.data; + comp_proj(EEG.icachansind,:) = EEG.icawinv(:, find(comp_keep))*eeg_getdatact(EEG, 'component', find(comp_keep)); + + scalp_ud.data2=comp_proj; +else + scalp_ud.data2=[]; +end + +set(findobj('Tag','ve_eeg'),'UserData',scalp_ud) + +ve_eegplot('drawp',0); \ No newline at end of file diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/qc_ovl_upd.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/qc_ovl_upd.m new file mode 100644 index 000000000..09d225b78 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/qc_ovl_upd.m @@ -0,0 +1,13 @@ +scalp_ud=get(findobj('Tag','ve_eeg'),'UserData'); + +comp_ud=get(findobj('Tag','ve_ica'),'UserData'); +comp_keep=~comp_ud.chan_marks_struct(1).flags; + +comp_proj=EEG.data; +comp_proj(EEG.icachansind,:) = EEG.icawinv(:, find(comp_keep))*eeg_getdatact(EEG, 'component', find(comp_keep)); + +scalp_ud.data2=comp_proj; + +set(findobj('Tag','ve_eeg'),'UserData',scalp_ud) + +ve_eegplot('drawp',0); \ No newline at end of file diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/s01_scalpart.htb b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/s01_scalpart.htb new file mode 100644 index 000000000..a5478912a --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/s01_scalpart.htb @@ -0,0 +1,493 @@ +%% SCRIPT DESCRIPTION +% This script features the main preprocessing and cleaning of the data before the ICA process begin. Data is flagged for high standard deveiavtion +% by channel to remove comically bad channels. The data is then rereferenced to the average site, and high/low pass filtered. +% Channel neighbor correlations are then calculated to find odd channels and bridged channels. The outermost channel is also flagged to reduce the rank of the data. +% After another rereference channel correlations are calculated by time epoch and flagged. The file is saved containing the, marks structure, then saved again +% with all of the mentioned flags puregd from the data in order to prime it for ICA. +% +%% From Config key_strswap Description +%----------------------------------------------------------------------------------------------------------------------------- +% in_path = [in_path] Relative path to input data files assuming cd = work_path +% montage_info = [montage_info] Montage information to fit channel locations on a standard surface. +% This can be a transformations matrix (e.g. [-.14,-22,-48,-.07,0,-1.57,1080,1260,1240]) +% ... or a a file containing a transformation matrix (e.g. [batch_dfn]_transmat.mat). +% ... or a BIDS channel location tsv file (assuming that during BIDS init the channels were coregistered to the standard surface). +% staging_script = [staging_script] Script file path/name that creates marks based on events in the raw data file. +% recur_sec = [recur_sec] Recurrence (sec) for artifact detection epoching (e.g. .5, Default 1) +% limit_sec = [limit_sec] Limits (sec) for artifact detection epoching (e.g. [-.5 0], Default [0 recur_sec]) +% sd_t_meth = [sd_t_meth] Method used for flagging epochs (e.g. 'q' (quantiles), or 'na' (default)) +% sd_t_vals = [sd_t_vals] Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7]) +% sd_t_o = [sd_t_o] z threshold for flagging epochs during standard deviation criteria (e.g. 6) +% sd_t_f_meth = [sd_t_f_meth] Fixed method used for flagging epochs (should be 'fixed') +% sd_t_f_vals = [sd_t_f_vals] Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% sd_t_f_o = [sd_t_f_o] z threshold for flagging epochs during fixed standard deviation criteria (e.g. .2) +% sd_t_pad = [sd_t_pad] Number of windows to pad onto each side of the ch_sd time flag +% sd_ch_meth = [sd_ch_meth] Method used for flagging channels (e.g. 'q' (quantiles), or 'na' (default)) +% sd_ch_vals = [sd_ch_vals] Percentage trim for confidence intervals during channel standard deviation criteria (e.g. [.3 .7]) +% sd_ch_o = [sd_ch_o] z threshold for flagging channels during standard deviation criteria (e.g. 6) +% sd_ch_f_meth = [sd_ch_f_meth] Fixed method used for flagging channels (should be 'fixed') +% sd_ch_f_vals = [sd_ch_f_vals] Percentage trim for confidence intervals during channel standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% sd_ch_f_o = [sd_ch_f_o] z threshold for flagging channel during fixed standard deviation criteria (e.g. 6) +% ref_loc_file = [ref_loc_file] Name of file containing the reference locations (including the relative path) +% low_bound_hz = [low_bound_hz] Lower bound of the filter bass-band +% high_bound_hz = [high_bound_hz] Upper bound of the filter bass-band +% save_f_res = [save_f_res] 1 if you want to save the filter residuals (Default: 1) +% n_nbr_ch = [n_nbr_ch] Number of channels to use in nearest neighbour r calculation (for channel criteria) +% r_ch_meth = [r_ch_meth] Method used for flagging low r channels (e.g. 'q' (quantiles), or 'na' (default)) +% r_ch_vals = [r_ch_vals] Percentage trim for confidence intervals during low r channel standard deviation criteria (e.g. [.3 .7]) +% r_ch_o = [r_ch_o] z threshold for flagging channel during neighbour r criteria (e.g. 3) +% r_ch_f_meth = [r_ch_f_meth] Fixed method used for flagging low r channels (should be 'fixed') +% r_ch_f_vals = [r_ch_f_vals] Percentage trim for confidence intervals during low r channel standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% r_ch_f_o = [r_ch_f_o] z threshold for flagging channel during fixed neighbour r criteria (e.g. 3) +% bridge_trim = [bridge_trim] Percentage trim for z calculation of bridged channels (e.g. 40 = 20% top and bottom) +% bridge_z = [bridge_z] z threshold for flagging channel during bridging criteria (e.g. 8) +% n_nbr_t = [n_nbr_t] Number of channels to use in nearest neighbour r calculation (for epoch criteria) +% r_t_meth = [r_t_meth] Method used for flagging low r epochs (e.g. 'q' (quantiles), or 'na' (default)) +% r_t_vals = [r_t_vals] Percentage trim for confidence intervals during low r epoch standard deviation criteria (e.g. [.3 .7]) +% r_t_o = [r_t_o] z threshold for flagging epochs during neighbour r criteria (e.g. 3) +% r_t_f_meth = [r_t_f_meth] Fixed method used for flagging low r epochs (should be 'fixed') +% r_t_f_vals = [r_t_f_vals] Percentage trim for confidence intervals during low r epoch standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% r_t_f_o = [r_t_f_o] z threshold for flagging epochs during fixed neighbour r criteria (e.g. 3) +% min_gap_ms = [min_gap_ms] Minimum time (ms) to allow between periods marked for rejection +% out_path = [out_path] Relative path to output data files assuming cd = work_path +% amica_param_file = [amica_param_file] template amicadefs.param file to modify +% amica_threads_s02 = [amica_threads_s02] number of threads to use for running s02 amica script (Default: 8) + +%% CHECK FOR OUTPUT PATH AND CRETAE IF NECESSARY +if ~exist('[out_path]/[batch_dfn,/,-1]','dir'); + disp('Making directory [out_path]/[batch_dfn,/,-1]'); + mkdir [out_path]/[batch_dfn,/,-1] +end + +%% LOAD DATASET +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +EEG = pop_bidsload('[batch_dfn]','gui',0); + +if ~isfield(EEG,'marks'); + if isempty(EEG.icaweights) + EEG.marks=marks_init(size(EEG.data)); + else + EEG.marks=marks_init(size(EEG.data),min(size(EEG.icaweights))); + end +end + +EEG.marks = marks_add_label(EEG.marks,'time_info', ... +{'init_ind',[0,0,1],[1:EEG.pnts]}); + +%execute the staging script if specified. +if exist('[staging_script]'); + [ssp,ssn,sse]=fileparts('[staging_script]'); + addpath(ssp); + eval(ssn); +end + +%add channel coregistration procedure here. +%warp locations to standard head surface: +if ~isempty('[montage_info]'); + if isempty(str2num('[montage_info]')); + %if it is a BIDS channel tsv, load the tsv,sd_t_f_vals + %else read the file that is assumed to be a transformation matrix. + else + EEG = warp_locs( EEG,'[ref_loc_file]', ... + 'transform',[[montage_info]], ... + 'manual','off'); + end +end + +% Apply average re-reference +%chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +%chan_m = mean(EEG.data(chan_inds,:),1); +%chan_m_mat = repmat(chan_m,size(EEG.data,1),1); +%EEG.data = EEG.data - chan_m_mat; +%clear chan_m_mat; + +% Window the continuous data +logging_log('INFO', 'Windowing the continous data...'); +EEG = marks_continuous2epochs(EEG,'recurrence',[[recur_sec]],'limits',[[limit_sec]]); +logging_log('INFO', 'LOADED DATASET...'); + +% rereference to selected channels.. +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +epoch_inds = marks_label2index(EEG.marks.time_info,{'manual'},'indexes','invert','on'); +[EEG,trim_ch_sd]=chan_variance(EEG,'data_field','data', ... + 'chan_inds',chan_inds, ... + 'epoch_inds',epoch_inds, ... + 'plot_figs','off'); + +chan_dist=zeros(size(trim_ch_sd)); +for i=1:size(trim_ch_sd,2); + chan_dist(:,i)=(trim_ch_sd(:,i)-median(trim_ch_sd(:,i)))/diff(quantile(trim_ch_sd(:,i),[.3,.7])); +end +mean_chan_dist=mean(chan_dist,2); +m=median(mean_chan_dist); +q=quantile(mean_chan_dist,[.3,.7]); + +refchans=find(mean_chan_distve_trimmean(msr,[bridge_trim],1)+ve_trimstd(msr,[bridge_trim],1)*[bridge_z]); +logging_log('INFO', 'IDENTIFIED BRIDGED CHANNELS...'); + +% Edit the channel flag info structure +logging_log('INFO', 'Updating chflaginfo structure...'); +lnkflags = zeros(EEG.nbchan,1); +lnkflags(chan_inds(flag_b_chan_inds))=1; +EEG.marks = marks_add_label(EEG.marks,'chan_info', ... + {'bridge',[.7,1,.7],[.2,1,.2],-1,lnkflags}); +logging_log('INFO', 'EDITED CHANFLAGINFO STRUCT...'); + +% Combine low_rand bridge chan_info flags into 'manual'... +EEG = pop_marks_merge_labels(EEG,'chan_info',{'low_r','bridge'},'target_label','manual'); +logging_log('INFO', 'COMBINED MARKS STRUCTURE INTO MANUAL FLAGS...'); + +%% FLAG RANK CHAN +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Flags the channel that is the least unique (the best channel to remove prior +% to ICA in order to account for the rereference rank deficiency. + +logging_log('INFO', 'Updating chflaginfo structure...'); +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +[r_max,rank_ind] = max(data_r_ch(chan_inds)); +rankflags = zeros(EEG.nbchan,1); +rankflags(chan_inds(rank_ind))=1; +EEG.marks = marks_add_label(EEG.marks,'chan_info', ... + {'rank',[.1,.1,.24],[.1,.1,.24],-1,rankflags}); +logging_log('INFO', 'EDITED CHANFLAGINFO STRUCT...'); + +%% REREFERENCE TO INTERPOLATED AVERAGE SITE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Rereference the data to an average interpolated site containined 19 channels +logging_log('INFO', 'Rereferencing to averaged interpolated site...'); +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +EEG = interp_ref(EEG,'[ref_loc_file]','chans',chan_inds); +EEG = eeg_checkset(EEG); +logging_log('INFO', 'REREFERENCED TO INTERPOLATED AVERAGE SITE...'); + +%% CALCULATE NEAREST NEIGHBOUR R VALUES +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Similarly to the neighbor r calculation done between channels this section looks +% at the correlation, but between all channels and for epochs of time. Time segmenents +% are flagged for removal. + +logging_log('INFO', 'Calculating nearest neighbour r array for window criteria...'); +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +epoch_inds = marks_label2index(EEG.marks.time_info,{'manual'},'indexes','invert','on'); +[EEG,data_r_t,~,~,~] = chan_neighbour_r(EEG, ... + [n_nbr_t],'max', ... + 'chan_inds',chan_inds, ... + 'epoch_inds',epoch_inds, ... + 'plotfigs','off'); +logging_log('INFO', 'CALCULATED NEAREST NEIGHBOUR R VALUES...'); + +% Create the window criteria vector for flagging low_r time_info... +logging_log('INFO', 'Assessing epoch r distributions criteria...') +[~,flag_r_t_inds]=marks_array2flags(data_r_t, ... + 'flag_dim','col', ... + 'init_method','[r_t_meth]', ... + 'init_vals',[r_t_vals], ... + 'init_dir','neg', ... + 'init_crit',[r_t_o], ... + 'flag_method','[r_t_f_meth]', ... + 'flag_vals',[ [r_t_f_vals] ], ... + 'flag_crit',[r_t_f_o], ... + 'plot_figs','off'); +logging_log('INFO', 'CREATED EPOCH CRITERIA VECTOR...'); + +% Edit the time flag info structure +logging_log('INFO', 'Updating latflaginfo structure...'); +lowr_epoch_flags = zeros(size(EEG.data(1,:,:))); +lowr_epoch_flags(1,:,epoch_inds(flag_r_t_inds))=1; +EEG.marks = marks_add_label(EEG.marks,'time_info', ... + {'low_r',[0,1,0],lowr_epoch_flags}); +clear lowr_epoch_flags; +logging_log('INFO', 'TIME TO: UPDATE REJECTION STRUCTURE...'); + +% Combine low_r time_info flags into 'manual'... +EEG = pop_marks_merge_labels(EEG,'time_info',{'low_r'},'target_label','manual'); +logging_log('INFO', 'COMBINED MARKS STRUCTURE INTO MANUAL FLAGS...'); + +% Concatenate epoched data back to continuous data +logging_log('INFO', 'Concatenating windowed data...'); +EEG = marks_epochs2continuous(EEG); +EEG = eeg_checkset(EEG,'eventconsistency'); +logging_log('INFO', 'CONCATENATED THE WINDOWED DATA INTO CONTINUOUS DATA...'); + +EEG=pop_marks_flag_gap(EEG,{'manual'},[min_gap_ms],'mark_gap',[.8,.8,.8],'offsets',[0 0],'ref_point','both'); + +% Combine mark_gap time_info flags into 'manual'... +EEG = pop_marks_merge_labels(EEG,'time_info',{'mark_gap'},'target_label','manual'); +logging_log('INFO', 'COMBINED MARKS STRUCTURE INTO MANUAL FLAGS...'); + +%%%----------------------------------------------------------------------- +%% SAVE sa.set FILE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +logging_log('INFO', 'Saving file: [batch_dfn,_,-1]_sa.set...'); +if ~exist('[out_path]/[batch_dfn,/,-1]','dir'); + mkdir('[out_path]/[batch_dfn,/,-1]'); +end +EEG = pop_saveset( EEG, 'filename','[out_path]/[batch_dfn,_,-1]_sa.set'); +logging_log('INFO', 'SAVED sa FILE...'); + +%%%----------------------------------------------------------------------- +%% PREPARE FILE FOR AMICA +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Purges the flagged data to make sure it is primed for ICA and creates the amica paramaeter file. + +% Removed flagged channels and time segments +sprintf('%s','Purging flagged channels...\n'); +EEG = pop_marks_select_data(EEG,'channel marks',[],'labels',{'manual','rank'},'remove','on'); +EEG = pop_marks_select_data(EEG,'time marks',[],'labels',{'manual'},'remove','on'); +EEG = eeg_checkset(EEG); +logging_log('INFO', 'TIME TO: PURGE DATA...'); + +% Make sure EEG.data is doubles for use in amica +EEG.data = double(EEG.data); + +% Save the SA purge file +logging_log('INFO',sprintf('%s','Saving file: [out_path]/[batch_dfn,_,-1]_sa_purge.set...')); +EEG = pop_saveset( EEG, 'filename','[out_path]/[batch_dfn,_,-1]_sa_purge.set'); + +% Save diagnostic arrays +try OCTAVE_VERSION; + save('-mat7-binary','[out_path]/[batch_dfn,_,-1]_data_sd_t.mat','data_sd_t'); + save('-mat7-binary','[out_path]/[batch_dfn,_,-1]_data_sd_ch.mat','data_sd_ch'); + save('-mat7-binary','[out_path]/[batch_dfn,_,-1]_data_r_ch.mat','data_r_ch'); + save('-mat7-binary','[out_path]/[batch_dfn,_,-1]_data_r_t.mat','data_r_t'); +catch + save('[out_path]/[batch_dfn,_,-1]_data_sd_t.mat','data_sd_t'); + save('[out_path]/[batch_dfn,_,-1]_data_sd_ch.mat','data_sd_ch'); + save('[out_path]/[batch_dfn,_,-1]_data_r_ch.mat','data_r_ch'); + save('[out_path]/[batch_dfn,_,-1]_data_r_t.mat','data_r_t'); +end + +% Save the AMICA parameter file +swapstr = file_strswap('[amica_param_file]', ... + '[repstr_fdt_fname]','[out_path]/[batch_dfn,_,-1]_sa_purge.fdt', ... + '[repstr_outpath]','[out_path]/[batch_dfn,_,-1]_amicaout_init', ... + '[repstr_nbchan]', num2str(EEG.nbchan), ... + '[repstr_pnts]', sprintf('%12.0f',EEG.pnts), ... + '[repstr_tpp]', '[amica_threads_s02]', ... + '[repstr_pca]', num2str(EEG.nbchan)); +fidparam = fopen('[out_path]/[batch_dfn,_,-1]_init.param','w'); +fprintf(fidparam,'%s',swapstr); +fclose(fidparam); + +% CREATE THE AMICA OUTPUT FOLDER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Needed for MPI jobs on cluster only in order to prevent a race effect bug. + +[~] = rmdir('[out_path]/[batch_dfn,_,-1]_amicaout_init','s'); +mkdir [out_path]/[batch_dfn,_,-1]_amicaout_init; +logging_log('INFO', 'Created Amica Output Folder'); + +logging_log('INFO', 'Scalpart complete'); +logging_log('INFO', 'Scheduler: [scheduler]'); +print_chan_sample(EEG); diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/s02_amica_init.htb b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/s02_amica_init.htb new file mode 100644 index 000000000..fc6e972aa --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/s02_amica_init.htb @@ -0,0 +1 @@ +[dep_path]/[amica_ver] derivatives/BIDS-Lossless-EEG/[batch_dfn,_,-1]_init.param diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/s03_compart_data.htb b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/s03_compart_data.htb new file mode 100644 index 000000000..e38a65cc5 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/s03_compart_data.htb @@ -0,0 +1,249 @@ +%% SCRIPT DESCRIPTION +% This script is loads the results of a preliminary ICA and uses the +% standard deviation of the components to flag and remove data time that +% does not fit a well behaved model. This will help make the next +% components stronger. The log likelihood of the model is also flagged for +% reference. Sections marked for high SD often have low log likelihoods +% as well. It then prepares the required parameter files for the parrallel amicas ABC. +% +%% From Config key_strswap Description +%----------------------------------------------------------------------------------------------------------------------------- +% data_path = [in_path] Path to input and output data files assuming cd = work_path +% recur_sec = [recur_sec] Recurrence (sec) for artifact detection epoching (e.g. .3) +% limit_sec = [limit_sec] Limits (sec) for artifact detection epoching (e.g. [-.3 .3]) +% sd_t_meth = [sd_t_meth] Method used for flagging epochs (e.g. 'q' (quantiles), or 'na' (default)) +% sd_t_vals = [sd_t_vals] Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7]) +% sd_t_o = [sd_t_o] z threshold for flagging epochs during standard deviation criteria (e.g. 6) +% sd_t_f_meth = [sd_t_f_meth] Fixed method used for flagging epochs (should be 'fixed') +% sd_t_f_vals = [sd_t_f_vals] Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% sd_t_f_o = [sd_t_f_o] z threshold for flagging epochs during fixed standard deviation criteria (e.g. .2) +% out_path = [out_path] Relative path to output data files assuming cd = work_path +% min_gap_ms = [min_gap_ms] Minimum time (ms) to allow between periods marked for rejection +% amica_param_file = [amica_param_file] template amicadefs.param file to modify +% amica_threads_s04 = [amica_threads_s04] number of threads to use for running s04a, s04b, s04c amica scripts (Default: 8) + +%% LOAD SA DATASET +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Loads the batched EEG datafile and the AMICA output files. Since ICA is +% done on a channel subset the components need to be returned to the +% correct indicies. + +% Load the EEG datafile +logging_log('INFO', 'Loading set file: [batch_dfn,_,-1]_sa.set...'); +tic; +EEG = pop_loadset('filepath','[in_path]','filename','[batch_dfn,_,-1]_sa.set'); +EEG = eeg_checkset( EEG ); +logging_log('INFO', 'TIME TO: LOAD DATASET...'); +toc + +% Prepare returning components +EEG.icachansind = marks_label2index(EEG.marks.chan_info,{'manual','rank'},'indices','invert','on'); +icatimeind = find(~EEG.marks.time_info(1).flags); + +% Load AMICA Model 1 +tic; +EEG.amica(1).models=loadmodout15('[out_path]/[batch_dfn,_,-1]_amicaout_init'); +EEG.icaweights=EEG.amica(1).models.W; +EEG.icasphere=EEG.amica(1).models.S(1:EEG.amica(1).models.num_pcs,:); +EEG=eeg_checkset(EEG); +logging_log('INFO', 'TIME TO: LOAD ICA INFO...'); +toc +%% PASS EEGDATA THROUGH THE AMICA MATRIX +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%Uses the ICA weights and the channel data to recreate the ICA components. + +tic; +% In place of EEG checkset +tmpdata = EEG.data(EEG.icachansind,:); +tmpindices = find(~sum(isnan(tmpdata))); +EEG.icaact = zeros(size(EEG.icaweights,1), size(tmpdata,2)); EEG.icaact(:) = NaN; +EEG.icaact(:,tmpindices) = (EEG.icaweights*EEG.icasphere)*tmpdata(:,tmpindices); +clear tmpdata; + +%% LOG LIKELIHOOD FLAGS +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%Creates marks based off of the log likelihood of the ICA model to the data. +% Create initial log likelihood marks + +logl_init_flags=zeros(size(EEG.data(1,:))); +logl_init_flag_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indices','invert','on'); +logl_init_flags(logl_init_flag_inds)=(EEG.amica(1).models.Lt-min(EEG.amica(1).models.Lt))/(max(EEG.amica(1).models.Lt)-min(EEG.amica(1).models.Lt)); + +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'logl_init',[1,0.45,0],logl_init_flags}); +logging_log('INFO', 'TIME TO: RECREATE ICA COMPONENTS...'); +toc + +% Window the continous Data +logging_log('INFO', 'Windowing the continous data...'); +tic; +EEG=marks_continuous2epochs(EEG,'recurrence',[recur_sec],'limits',[limit_sec]); +logging_log('INFO', 'TIME TO: WINDOW THE CONTINUOUS DATA...'); +toc +%% CALCULATE ICAACT SD +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates the IC standard Deviation by epoch window. Flags windows with +% too much standard deviation. + +% Calculate IC sd by window +logging_log('INFO', 'Calculating the IC sd array for window criteria...'); +tic; +epoch_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indexes','invert','on'); +[EEG,icaact_sd1_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'plot_figs','off'); + +% Create the windowing sd criteria +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_t_inds]=marks_array2flags(icaact_sd1_t, ... + 'flag_dim','col', ... + 'init_method','[sd_t_meth]', ... + 'init_vals',[sd_t_vals], ... + 'init_crit',[sd_t_o], ... + 'flag_method','[sd_t_f_meth]', ... + 'flag_vals',[ [sd_t_f_vals] ], ... + 'flag_crit',[sd_t_f_o], ... + 'plot_figs','off'); + +% Update marks rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +icsd_epoch_flags=zeros(size(EEG.data(1,:,:))); +icsd_epoch_flags(1,:,epoch_inds(flag_t_inds))=1; +icsd_epoch_flags=padflags(EEG,icsd_epoch_flags,1,'value',.5); +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_sd1',[.4,.6,.6],icsd_epoch_flags}); +logging_log('INFO', 'TIME TO: UPDATE REJECTION STRUCTURE WITH IC SD MARKS...'); +toc + +% Concat the windowed data back to continuous +logging_log('INFO', 'Concatenating windowed data...'); +tic; +EEG=marks_epochs2continuous(EEG); +EEG=eeg_checkset(EEG,'eventconsistency'); +logging_log('INFO', 'TIME TO: CONCATENATE THE WINDOWED DATA INTO CONTINUOUS DATA...'); +toc +%% FLAG GAPS +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Flags small gaps between rejection flags so that there are not as many +% short segments to be analyzed + +% Mark any flag gaps < 2 sec. +tic; +EEG=pop_marks_flag_gap(EEG,{'manual','mark_gap','ic_sd1'}, ... + [min_gap_ms],'mark_gap',[.8,.8,.8],'offsets',[0 0],'ref_point','both'); + +%%COMBINE MARKS STRUCTURE INTO MANUAL FLAGS... +EEG=pop_marks_merge_labels(EEG,'time_info',{'manual','ic_sd1','mark_gap'},'target_label','manual'); +logging_log('INFO', 'TIME TO: FLAG GAPS AND COMBINE MARKS STRUCTURE INTO MANUAL FLAGS...'); +toc +%% SAVE compart_data.set FILE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% A saved copy is made before the file is purged to its ready for ICA +% version. + +tic; +logging_log('INFO', 'Saving file: [batch_dfn,_,-1]_compart_data.set...'); +EEG = pop_saveset( EEG, 'filename','[out_path]/[batch_dfn,_,-1]_compart_data.set'); +logging_log('INFO', 'TIME TO: SAVE compart_data FILE...'); +toc; +%% SAVE compart_data_purge FILE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% All EEG parts associated with ICA need to be cleared as models seem to +% adapt based on previous data. Old and New flagged channels/time need to +% be purged again in preparation of ICA. + +% Clearing ICA Data +EEG.icaact = []; +EEG.icawinv = []; +EEG.icasphere = []; +EEG.icaweights = []; +EEG.icachansind = []; +EEG.amica = []; +logging_log('INFO', 'ICA data Reset'); + +% Removing Flagged Channels +tic; +sprintf('%s','Purging flagged channels...\n'); +EEG=pop_marks_select_data(EEG,'channel marks',[],'labels',{'manual','rank'},'remove','on'); +EEG=pop_marks_select_data(EEG,'time marks',[],'labels',{'manual'},'remove','on'); +EEG=eeg_checkset(EEG); +logging_log('INFO', 'TIME TO: PURGE DATA...'); +toc; + +% Saving File to enter next ICAs +logging_log('INFO', 'Saving the Param files'); +sprintf('%s','Saving file: [batch_dfn,_,-1]_compart_data_purge.set...\n'); +EEG = pop_saveset( EEG, 'filename','[out_path]/[batch_dfn,_,-1]_compart_data_purge.set'); + +% Save diagnostic arrays +try OCTAVE_VERSION; + save('-mat7-binary','[out_path]/[batch_dfn,_,-1]_icaact_sd1_t.mat','icaact_sd1_t'); +catch + save('[out_path]/[batch_dfn,_,-1]_icaact_sd1_t.mat','icaact_sd1_t'); +end + +%% SAVE AMICA PARAMETER FILES +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% A parameter file need to be made for each of the ICAs running after this +% script. In This case AMICA A, B and C are run. + +tic; +% A +% Saving AMICA Parameter File +swapstr=file_strswap('[amica_param_file]', ... + '[repstr_fdt_fname]','[out_path]/[batch_dfn,_,-1]_compart_data_purge.fdt', ... + '[repstr_outpath]','[out_path]/[batch_dfn,_,-1]_amicaout_A', ... + '[repstr_nbchan]', num2str(EEG.nbchan), ... + '[repstr_pnts]', sprintf('%12.0f',EEG.pnts), ... + '[repstr_tpp]', '[amica_threads_s04]', ... + '[repstr_pca]', num2str(EEG.nbchan)); +fidparam=fopen('[out_path]/[batch_dfn,_,-1]_A.param','w'); +fprintf(fidparam,'%s',swapstr); +fclose(fidparam); + +% B +% Saving AMICA Parameter File +swapstr=file_strswap('[amica_param_file]', ... + '[repstr_fdt_fname]','[out_path]/[batch_dfn,_,-1]_compart_data_purge.fdt', ... + '[repstr_outpath]','[out_path]/[batch_dfn,_,-1]_amicaout_B', ... + '[repstr_nbchan]', num2str(EEG.nbchan), ... + '[repstr_pnts]', sprintf('%12.0f',EEG.pnts), ... + '[repstr_tpp]', '[amica_threads_s04]', ... + '[repstr_pca]', num2str(EEG.nbchan)); +fidparam=fopen('[out_path]/[batch_dfn,_,-1]_B.param','w'); +fprintf(fidparam,'%s',swapstr); +fclose(fidparam); + +% C +% Saving AMICA Parameter File +swapstr=file_strswap('[amica_param_file]', ... + '[repstr_fdt_fname]','[out_path]/[batch_dfn,_,-1]_compart_data_purge.fdt', ... + '[repstr_outpath]','[out_path]/[batch_dfn,_,-1]_amicaout_C', ... + '[repstr_nbchan]', num2str(EEG.nbchan), ... + '[repstr_pnts]', sprintf('%12.0f',EEG.pnts), ... + '[repstr_tpp]', '[amica_threads_s04]', ... + '[repstr_pca]', num2str(EEG.nbchan)); +fidparam=fopen('[out_path]/[batch_dfn,_,-1]_C.param','w'); +fprintf(fidparam,'%s',swapstr); +fclose(fidparam); + +%% Create the Amica Output Folder +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Needed for MPI jobs on cluster. Otherwise race effect will occur. + +[~] = rmdir('[out_path]/[batch_dfn,_,-1]_amicaout_A','s'); +[~] = rmdir('[out_path]/[batch_dfn,_,-1]_amicaout_B','s'); +[~] = rmdir('[out_path]/[batch_dfn,_,-1]_amicaout_C','s'); + + +mkdir '[out_path]/[batch_dfn,_,-1]_amicaout_A'; +mkdir '[out_path]/[batch_dfn,_,-1]_amicaout_B'; +mkdir '[out_path]/[batch_dfn,_,-1]_amicaout_C'; + +logging_log('INFO', 'TIME TO: CREATE AMICA PARAMETER FILES...'); +toc + +% FINISH +logging_log('INFO', '***********COMPART DATA COMPLETE!**********'); +logging_log('INFO', 'Scheduler: [scheduler]'); +print_chan_sample(EEG); diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/s04a_amica.htb b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/s04a_amica.htb new file mode 100644 index 000000000..4774a0ee5 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/s04a_amica.htb @@ -0,0 +1 @@ +[dep_path]/[amica_ver] derivatives/BIDS-Lossless-EEG/[batch_dfn,_,-1]_A.param diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/s04b_amica.htb b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/s04b_amica.htb new file mode 100644 index 000000000..5d8da5716 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/s04b_amica.htb @@ -0,0 +1 @@ +[dep_path]/[amica_ver] derivatives/BIDS-Lossless-EEG/[batch_dfn,_,-1]_B.param diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/s04c_amica.htb b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/s04c_amica.htb new file mode 100644 index 000000000..65cce5992 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/s04c_amica.htb @@ -0,0 +1 @@ +[dep_path]/[amica_ver] derivatives/BIDS-Lossless-EEG/[batch_dfn,_,-1]_C.param diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/s05_concat_data.htb b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/s05_concat_data.htb new file mode 100644 index 000000000..c8f860522 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/scripts/s05_concat_data.htb @@ -0,0 +1,647 @@ +%% SCRIPT DESCRIPTION +% This script combineds the results of the amicas A,B and C. It takes this information and using ICAtest it determines which components were +% and were not replicated across and flags them, along with time sections with high standard deviation. Beta,theta and Alpha power is calculated +% arcoss time and is marked but not primed for removal. +% +%% From Config key_strswap Description +%----------------------------------------------------------------------------------------------------------------------------- +% in_path = [in_path] Relative path to input data files assuming cd = work_path +% recur_sec = [recur_sec] Recurrence (sec) for the current artifact detection epoching (e.g. 1) +% limit_sec = [limit_sec] Limits (sec) for the current artifact detection epoching (e.g. [0 1]) +% sd_t_meth = [sd_t_meth] Method used for flagging epochs (e.g. 'q' (quantiles), or 'na' (default)) +% sd_t_vals = [sd_t_vals] Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7]) +% sd_t_o = [sd_t_o] z threshold for flagging epochs during standard deviation criteria (e.g. 6) +% sd_t_f_meth = [sd_t_f_meth] Fixed method used for flagging epochs (should be 'fixed') +% sd_t_f_vals = [sd_t_f_vals] Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% sd_t_f_o = [sd_t_f_o] z threshold for flagging epochs during fixed standard deviation criteria (e.g. .2) +% min_gap_ms = [min_gap_ms] Minimum time (ms) to allow between periods marked for rejection +% icr_crit = [icr_crit] False discovery rate of component replicatablilty, measure of sensitivity % +% dip_vol = [dip_vol] file path/name of the standard volume file used in dipole fitting (e.g. derivatives/lossless/code/misc/standard_vol.mat). +% dip_mri = [dip_mri] file path/name of the standard mri file used in dipole fitting (e.g. derivatives/lossless/code/misc/standard_mri.mat). +% dip_elc = [dip_elc] file path/name of the standard electrode file used in dipole fitting (derivatives/lossless/code/misc/standard_1020.elc). +% dip_transmat = [dip_transmat] Transformation matrix to apply during dipole fit (e.g. [0 0 0 0 0 0 1 1 1]) +% cor_transmat = [cor_transmat] The transformation matrix to apply between dipole fitting to the standard head and performing ICLabel to the EEGLAB default orientation (e.g.[0,0,0,0,0,1.57,1,1,1]) +% cor_nosedir = [cor_nosedir] Nose direction of montage to apply between dipole fitting to the standard head and performing ICLabel to the EEGLAB default orientation (e.g. +Y) +% out_path = [out_path] Relative path to output data files assuming cd = work_path + +%% LOAD COMPART DATASET +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Loads the data file and the first amica model. + +logging_log('INFO', 'Loading set file: [batch_dfn,_,-1]_compart_data.set...'); +tic; +EEG = pop_loadset('filepath','[in_path]','filename','[batch_dfn,_,-1]_compart_data.set'); +EEG = eeg_checkset( EEG ); +logging_log('INFO', 'TIME TO: LOAD DATASET...'); +toc + +% Set up the ICA channels +EEG.icachansind = marks_label2index(EEG.marks.chan_info,{'manual','rank'},'indices','invert','on'); + +% Load Amica A +logging_log('INFO', 'Loading Amica ABC...'); +tic; +EEG.amica(2).models=loadmodout15('[in_path]/[batch_dfn,_,-1]_amicaout_A'); +EEG.icaweights=EEG.amica(2).models.W; +EEG.icasphere=EEG.amica(2).models.S(1:EEG.amica(2).models.num_pcs,:); + +%% PASS EEG DATA THROUGH THE AMICA MATRIX +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Loads the B and C amica models. Amica A is used to fill in the ICA information. + +% Instead of EEG=eeg_checkset(EEG); +tmpdata = EEG.data(EEG.icachansind,:); +tmpindices = find(~sum(isnan(tmpdata))); +EEG.icaact = zeros(size(EEG.icaweights,1), size(tmpdata,2)); EEG.icaact(:) = NaN; +EEG.icaact(:,tmpindices) = (EEG.icaweights*EEG.icasphere)*tmpdata(:,tmpindices); +clear tmpdata; + +% Create winv for topo plots manually +EEG.icawinv = []; +EEG.icawinv = pinv(EEG.icaweights * EEG.icasphere); + +% Load Amica B +EEG.amica(3).models=loadmodout15('[in_path]/[batch_dfn,_,-1]_amicaout_B'); +% Load Amica C +EEG.amica(4).models=loadmodout15('[in_path]/[batch_dfn,_,-1]_amicaout_C'); +logging_log('INFO', 'TIME TO: LOAD ICA ABC INFO...'); +toc + +%% UPDATE REJECTION STRUCTURE... +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Creating flag marks to based on the log liklyhodd models found for each of the ICA models + +% Flags for amicas A B and C +logging_log('INFO', 'Updating latflaginfo structure...'); +tic; + +% A +logl_A_flags=zeros(size(EEG.data(1,:))); +logl_A_flag_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indices','invert','on'); + +% Check to see if the overwrite will be successful. If the overwirte is a different size then some of the old data will be retained. +if length(EEG.amica(2).models.Lt) ~= length(logl_A_flag_inds) + EEG.amica(2).models.Lt = EEG.amica(2).models.Lt(1:length(logl_A_flag_inds)); + warning('YOU FORGOT TO DELETE YOUR OLD AMICA FILES AND THE NEW CHANNEL SIZES DO NOT MATCH UP'); +end + +logl_A_flags(logl_A_flag_inds)=(EEG.amica(2).models.Lt-min(EEG.amica(2).models.Lt))/(max(EEG.amica(2).models.Lt)-min(EEG.amica(2).models.Lt)); +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'logl_A',[1,0.45,0],logl_A_flags}); +clear logl_A_flags; +% B +logl_B_flags=zeros(size(EEG.data(1,:))); +logl_B_flag_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indices','invert','on'); +logl_B_flags(logl_B_flag_inds)=(EEG.amica(3).models.Lt-min(EEG.amica(3).models.Lt))/(max(EEG.amica(3).models.Lt)-min(EEG.amica(3).models.Lt)); +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'logl_B',[1,0.45,0],logl_B_flags}); +clear logl_B_flags; +% C +logl_C_flags=zeros(size(EEG.data(1,:))); +logl_C_flag_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indices','invert','on'); +logl_C_flags(logl_C_flag_inds)=(EEG.amica(4).models.Lt-min(EEG.amica(4).models.Lt))/(max(EEG.amica(4).models.Lt)-min(EEG.amica(4).models.Lt)); +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'logl_C',[1,0.45,0],logl_C_flags}); +clear logl_C_flags; + +logging_log('INFO', 'TIME TO: UPDATE REJECTION STRUCTURE...'); +toc + +%% IDENTIFY RELIABLE COMPONENTS USING ISCTEST +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Gives the A variable of each of the amicas to the function icatest. Returned are the indecies of the +% Components that were not replicated across all 3 of the models. + +tic; +% Clear a in case of local models are saved already +a = []; +for i=2:length(EEG.amica); + a(:,:,i-1)=EEG.amica(i).models(1).A; +end + +% Calculating the replicated component flag +% Replicate criteria changed from 0.05,0.05 to 0.1,0.1 to be less sensitive +ca=isctest(a,[icr_crit],[icr_crit],'mixing'); +if isempty(ca) + mess = ['ISCTest failed to return reasonable values. ISCTest is ' ... + 'meant to find duplicate channels in amica_{A,B,C} if this ' ... + 'failed consider investigating the datafile']; + logging_log('ERROR', mess); + error(mess); +end +icr_inds=setdiff(1:min(size(EEG.amica(2).models(1).A)),ca(:,1)); + +% Creating the replicated component flag +logging_log('INFO', 'Updating chflaginfo structure...'); +icr_comp_flags=zeros(min(size(EEG.amica(2).models(1).A)),1); +icr_comp_flags(icr_inds)=1; +EEG.marks=marks_add_label(EEG.marks,'comp_info', ... + {'ic_rt',[0,0,1],[0,0,1],-1,icr_comp_flags}); +logging_log('INFO', 'TIME TO: EDIT FIND REPLICATED COMPONENTS AND UPDATE COMPINFO STRUCT...'); +toc + +% Window the continuous data +logging_log('INFO', 'Windowing the continous data...'); +tic; +EEG=marks_continuous2epochs(EEG,'recurrence',[recur_sec],'limits',[limit_sec]); +logging_log('INFO', 'TIME TO: WINDOW THE CONTINUOUS DATA...'); +toc + +%% CALCULATE ICAACT SD +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Similarly to the IC_SD flag created in compart. Marks sections of time where the components +% have an abnormally high standard deviation. + +% Calculating the replicated component flag +logging_log('INFO', 'Calculating the IC sd array for window criteria...'); +tic; +epoch_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indexes','invert','on'); +[EEG,icaact_sd2_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'varmeasure','absmean', ... + 'detrend','off', ... + 'plot_figs','off'); + +% Creating the ic_sd component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_sd_t_inds]=marks_array2flags(icaact_sd2_t, ... + 'flag_dim','col', ... + 'init_method','[sd_t_meth]', ... + 'init_vals',[sd_t_vals], ... + 'init_crit',[sd_t_o], ... + 'flag_method','[sd_t_f_meth]', ... + 'flag_vals',[ [sd_t_f_vals] ], ... + 'flag_crit',[sd_t_f_o], ... + 'plot_figs','off'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); + +icsd_epoch_flags=zeros(size(EEG.data(1,:,:))); +icsd_epoch_flags(1,:,epoch_inds(flag_sd_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_sd2',[.4,.6,.6],icsd_epoch_flags}); +logging_log('INFO', 'TIME TO: EDIT FIND IC_SD AND UPDATE TIMEINFO REJECTION STRUCT...'); +toc +clear icsd_epoch_flags; + +%% CALCULATE DELTA/THETA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high Theta/Alpha Power + +% Calculating the Delta/Theta component flag +logging_log('INFO', 'Calculating the delta/theta IC power array for window criteria...'); +tic; +[EEG,icaact_dt_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[0 7], ... + 'plot_figs','off'); + +% Creating the Delta/Theta component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_dt_t_inds]=marks_array2flags(icaact_dt_t, ... + 'flag_dim','col', ... + 'init_method','[sd_t_meth]', ... + 'init_vals',[sd_t_vals], ... + 'init_crit',[sd_t_o], ... + 'flag_method','[sd_t_f_meth]', ... + 'flag_vals',[ [sd_t_f_vals] ], ... + 'flag_crit',[sd_t_f_o], ... + 'plot_figs','off'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +icdt_epoch_flags=zeros(size(EEG.data(1,:,:))); +icdt_epoch_flags(1,:,epoch_inds(flag_dt_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_dt',[1,1,0],icdt_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE DELTA/THETA ICAACT POWER AND UPDATE MARKS...'); +toc +clear icdt_epoch_flags; + +%% CALCULATE ALPHA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high Alpha Power + +% Calculating the Alpha component flag +logging_log('INFO', 'Calculating the alpha IC power array for window criteria...'); +tic; +[EEG,icaact_a_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[8 13], ... + 'plot_figs','off'); + +% Creating the Alpha component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_a_t_inds]=marks_array2flags(icaact_a_t, ... + 'flag_dim','col', ... + 'init_method','[sd_t_meth]', ... + 'init_vals',[sd_t_vals], ... + 'init_crit',[sd_t_o], ... + 'flag_method','[sd_t_f_meth]', ... + 'flag_vals',[ [sd_t_f_vals] ], ... + 'flag_crit',[sd_t_f_o], ... + 'plot_figs','off'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +ica_epoch_flags=zeros(size(EEG.data(1,:,:))); +ica_epoch_flags(1,:,epoch_inds(flag_a_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_a',[1,1,0],ica_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE ALPHA ICAACT POWER AND UPDATE MARKS...'); +toc +clear ica_epoch_flags; + +%% CALCULATE BETA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high Beta Power. +% Note that at the moment this mark is used for removals. + +% Calculating the Beta component flag +logging_log('INFO', 'Calculating the beta IC power array for window criteria...'); +tic; +[EEG,icaact_b_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[14 30], ... + 'plot_figs','off'); +logging_log('INFO', 'TIME TO: CALCULATE BETA ICAACT POWER...'); + +% Creating the Beta component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_b_t_inds]=marks_array2flags(icaact_b_t, ... + 'flag_dim','col', ... + 'init_method','[sd_t_meth]', ... + 'init_vals',[sd_t_vals], ... + 'init_crit',[sd_t_o], ... + 'flag_method','[sd_t_f_meth]', ... + 'flag_vals',[ [sd_t_f_vals] ], ... + 'flag_crit',[sd_t_f_o], ... + 'plot_figs','off'); + +logging_log('INFO', 'TIME TO: CREATE WINDOW CRITERIA VECTOR...'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +icb_epoch_flags=zeros(size(EEG.data(1,:,:))); +icb_epoch_flags(1,:,epoch_inds(flag_b_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_b',[0,1,1],icb_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE BETA ICAACT POWER AND UPDATE MARKS...'); +toc +clear icb_epoch_flags; + +%% CALCULATE LOW GAMMA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high Low-Gamma Power. +% Note that at the moment this mark is used for removals. + +% Calculating the Beta component flag +logging_log('INFO', 'Calculating the low-gamma IC power array for window criteria...'); +tic; +[EEG,icaact_lg_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[31 60], ... + 'plot_figs','off'); +logging_log('INFO', 'TIME TO: CALCULATE LOW-GAMMA ICAACT POWER...'); + +% Creating the LOW-GAMMA component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_lg_t_inds]=marks_array2flags(icaact_lg_t, ... + 'flag_dim','col', ... + 'init_method','[sd_t_meth]', ... + 'init_vals',[sd_t_vals], ... + 'init_crit',[sd_t_o], ... + 'flag_method','[sd_t_f_meth]', ... + 'flag_vals',[ [sd_t_f_vals] ], ... + 'flag_crit',[sd_t_f_o], ... + 'plot_figs','off'); + +logging_log('INFO', 'TIME TO: CREATE WINDOW CRITERIA VECTOR...'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +iclg_epoch_flags=zeros(size(EEG.data(1,:,:))); +iclg_epoch_flags(1,:,epoch_inds(flag_lg_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_lg',[0,1,1],iclg_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE LOW-GAMMA ICAACT POWER AND UPDATE MARKS...'); +toc +clear iclg_epoch_flags; + + +%% CALCULATE HIGH GAMMA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high High-Gamma Power. +% Note that at the moment this mark is used for removals. + +% Calculating the Beta component flag +logging_log('INFO', 'Calculating the high-gamma IC power array for window criteria...'); +tic; +[EEG,icaact_hg_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[61 100], ... + 'plot_figs','off'); +logging_log('INFO', 'TIME TO: CALCULATE HIGH-GAMMA ICAACT POWER...'); + +% Creating the HIGH-GAMMA component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_hg_t_inds]=marks_array2flags(icaact_hg_t, ... + 'flag_dim','col', ... + 'init_method','[sd_t_meth]', ... + 'init_vals',[sd_t_vals], ... + 'init_crit',[sd_t_o], ... + 'flag_method','[sd_t_f_meth]', ... + 'flag_vals',[ [sd_t_f_vals] ], ... + 'flag_crit',[sd_t_f_o], ... + 'plot_figs','off'); + +logging_log('INFO', 'TIME TO: CREATE WINDOW CRITERIA VECTOR...'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +ichg_epoch_flags=zeros(size(EEG.data(1,:,:))); +ichg_epoch_flags(1,:,epoch_inds(flag_hg_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_hg',[0,1,1],ichg_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE HIGH-GAMMA ICAACT POWER AND UPDATE MARKS...'); +toc +clear ichg_epoch_flags; + +%% MARKS UPDATE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Merges the epochs back to continuous data. Flags marks gaps and combines time +% the new time marks into the manual marks. + +% Concat epochs back into continuous data +logging_log('INFO', 'Concatenating windowed data...'); +tic; +EEG=marks_epochs2continuous(EEG); +EEG=eeg_checkset(EEG,'eventconsistency'); +logging_log('INFO', 'TIME TO: CONCATENATE THE WINDOWED DATA INTO CONTINUOUS DATA...'); +toc + +% Mark flag gaps between time marks +tic; +EEG=pop_marks_flag_gap(EEG,{'manual','mark_gap','ic_sd2'}, ... + [min_gap_ms],'mark_gap',[.8,.8,.8],'offsets',[0 0],'ref_point','both'); +logging_log('INFO', 'TIME TO: MARK FLAG GAPS THAT ARE < 2 SECONDS...'); +toc + +% Combine the removal marks into manual +tic; +EEG=pop_marks_merge_labels(EEG,'time_info',{'manual','ic_sd2','mark_gap'},'target_label','manual'); +logging_log('INFO', 'TIME TO: COMBINE MARKS STRUCTURE INTO MANUAL FLAGS...'); +toc + +% begin prep for dipole fit +logging_log('INFO','Checking for JVM...'); +javachk('jvm'); + +% TODO(mae) this isn't added for some reason +addpath([pwd '/derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/Fieldtrip-lite141209/src']) + +%% DIPFIT +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Does the dipole fitting procedures. This takes a substantial amount of +% time. +% TODO: Make sure this is all needed, seems to be repeating things +logging_log('INFO','FITTING DIPOLES...'); +tic; +EEG = pop_dipfit_settings( EEG, 'hdmfile','[dip_vol]', ... + 'coordformat','MNI', ... + 'mrifile','[dip_mri]', ... + 'chanfile','[dip_elc]', ... + 'coord_transform',[dip_transmat], ... + 'chansel',[1:EEG.nbchan] ); + +% OLD SINGLE THREADED SETUP +% EEG = pop_multifit(EEG, [1:EEG.nbchan] ,'threshold',100,'plotopt',{'normlen' 'on'}); + +% NEW MULTI-THREADED SETUP +EEG = pop_multifit_bucanl_parallel(EEG, [1:EEG.nbchan], 8,'threshold',100,'plotopt',{'normlen' 'on'}); + +EEG=eeg_checkset(EEG); +logging_log('INFO','TIME TO: FIT DIPOLES...'); +toc + +%% MARK ICs FOR REJECTION BASED ON RV +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Creates marks based on dipfit results. Dipfit marks are added +% for rejection while ICMARC are just for reference. +% IC RESIDUAL VARIANCE FLAG +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Residuals greater than 15% are flagged - can be done here as it only references +% dipoles and their values. +%logging_log('INFO','UPDATE MARKS STRUCTURE WITH IC_RV...'); +%tic; +% Dipole criteria changed from 0.15 to 0.2 to be less sensitive +%EEG.reject.gcompreject(find([EEG.dipfit.model.rv]>.15))=1; +%EEG.reject.gcompreject(find(isnan([EEG.dipfit.model.rv])))=1; +%EEG.marks=marks_add_label(EEG.marks,'comp_info', ... +% {'ic_rv',[1,0,0],[1,0,0],-1,EEG.reject.gcompreject'}); +%logging_log('INFO','TIME TO: UPDATE MARKS STRUCTURE...'); +%toc + +% COMBINE MARKS STRUCTURE INTO MANUAL FLAGS... +%tic; +%EEG=pop_marks_merge_labels(EEG,'comp_info',{'manual','ic_rv'},'target_label','manual'); +%logging_log('INFO','TIME TO: COMBINE MARKS STRUCTURE INTO MANUAL FLAGS...'); +%toc + +% Adjust montage +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% This will pretty much always be dataset specific - so be careful +EEG = warp_locs( EEG, '[dip_elc]', ... + 'transform',[cor_transmat], ... + 'manual','off'); +EEG.chaninfo.nosedir='[cor_nosedir]'; +EEG=eeg_checkset(EEG); + +%% SAVE LOSSLESS DERIVATIVE *_eeg_ll.set FILE (same name as the input file from the +% BIDS root directory [but located in the derivatives/lossless directory]). +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +logging_log('INFO','Saving set file: [out_path]/[batch_dfn,_,-1]_eeg_ll.set...'); +tic; +EEG = pop_saveset( EEG, 'filename','[out_path]/[batch_dfn,_,-1]_eeg_ll.set'); +logging_log('INFO','TIME TO: SAVE DIP DATA SET...'); +toc + +% Save diagnostic arrays +try OCTAVE_VERSION; + save('-mat7-binary','[out_path]/[batch_dfn,_,-1]_icaact_sd2_t.mat','icaact_sd2_t'); +catch + save('[out_path]/[batch_dfn,_,-1]_icaact_sd2_t.mat','icaact_sd2_t'); +end + +logging_log('INFO', 'Scheduler: [scheduler]'); +print_chan_sample(EEG); + + +if ~find(strcmpi({EEG.marks.chan_info.label},'ch_s_sd')); + EEG.marks=marks_add_label(EEG.marks,'chan_info',{'ch_s_sd',[1,0,0],[]}); +end; + +EEG = eeg_checkset(EEG); + +% CHECK FOR IC CLASSIFICATION MARKS +%% Use the ICLabel plugin +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% ICLabel is a database style plugin that classifies ICA components into the +% following seven categories + +% (Brain)(Muscle)(Eye)(Heart)(Line Noise)(Channel Noise)(Other) +% ( 1 )( 2 )( 3 )( 4 )( 5 )( 6 )( 7 ) + +% Colour structure and groupings are as follows: +% ---------- +% Brain -> [0, 0.4, 0] +% ---------- +% Muscle -> [0.4, 0, 0] +% Eye -> [0.8, 0, 0] +% Heart -> [0.8, 0.4, 0] +% ---------- +% Line Noise -> [0.2, 0.4, 0.8] +% Channel noise -> [0.4, 0, 0.8] +% ---------- +% Other -> [0.8, 0.8, 0] +% ---------- + +% Create lookup table of the above information. +pairs = {'brain',[0, 0.4, 0]; +'muscle',[0.4,0,0]; +'eye',[0.8,0,0]; +'heart',[0.8,0.4,0]; +'line_noise',[0.2,0.4,0.8]; +'chan_noise',[0.4,0,0.8]; +'other',[.7 .4 0]}; +lookupTable = cell2struct(pairs, {'name', 'colour'}, 2); + +EEG = pop_marks_select_data(EEG,'time marks',[],'labels',{'manual'},'remove','on'); + +% Run ICLabel +%EEG = iclabel(EEG); + +% extract features +disp 'ICLabel: extracting features...' +features = ICL_feature_extractor(EEG, 40); + +% run ICL +disp 'ICLabel: calculating labels...' +labels = run_ICL('default',features{:}); + +% save into EEG +disp 'ICLabel: saving results...' +EEG.etc.ic_classification.ICLabel.classes = ... + {'Brain', 'Muscle', 'Eye', 'Heart', 'Line', 'Channel', 'Other'}; +EEG.etc.ic_classification.ICLabel.classifications = labels; + +tmp=EEG.etc.ic_classification; + +% Reload dataset: +EEG = pop_loadset('filepath','[in_path]','filename','[batch_dfn,_,-1]_eeg_ll.set'); +EEG = eeg_checkset(EEG); + +EEG.etc.ic_classification=tmp; + +% Extract labels from the structure. +rawLabels = zeros([1,length(EEG.etc.ic_classification.ICLabel.classifications)]); +for index = 1:length(EEG.etc.ic_classification.ICLabel.classifications) + [p,i] = max(EEG.etc.ic_classification.ICLabel.classifications(index,1:end-1)); + if p<.3; + i=7; + end + rawLabels(index) = i; +end + +%ICLabel Exporting to marks +%%%%%%%%%%%%%%%%%%%%%%%%%%%% +logging_log('INFO','UPDATE MARKS STRUCTURE WITH ICLABEL BRAIN COMPS...'); +for compStage = 1:7 + ICLabel_pass= zeros([1,length(rawLabels)]); + ICLabel_pass(find(rawLabels==compStage)) = 1; + ICLabel_pass = ICLabel_pass'; + EEG.marks=marks_add_label(EEG.marks,'comp_info', ... + {lookupTable(compStage).name,lookupTable(compStage).colour, ... + lookupTable(compStage).colour,1,ICLabel_pass}); +end +% make any brain components always on top: +EEG.marks.comp_info(1,find(strcmp({EEG.marks.comp_info.label},'brain'))).order=2; + + +%RESET/INITIATE CHAN MARKS... +manual_ind=find(strcmp('manual',{EEG.marks.chan_info.label})); + + +%RESET/INITIATE IC MARKS... +EEG.marks=marks_add_label(EEG.marks,'comp_info',{'ambig',[.3,.3,1],[.3,.3,1],3,zeros(min(size(EEG.icaweights)),1)}); +manual_ind=find(strcmp('manual',{EEG.marks.comp_info.label})); +ch_s_sd_ind=find(strcmp('ch_s_sd',{EEG.marks.chan_info.label})); +ch_sd_ind=find(strcmp('ch_sd',{EEG.marks.chan_info.label})); +low_r_ind=find(strcmp('low_r',{EEG.marks.chan_info.label})); +bridge_ind=find(strcmp('bridge',{EEG.marks.chan_info.label})); +rank_ind=find(strcmp('rank',{EEG.marks.chan_info.label})); +icrt_ind=find(strcmp('ic_rt',{EEG.marks.comp_info.label})); +brain_ind=find(strcmp('brain',{EEG.marks.comp_info.label})); +muscle_ind=find(strcmp('muscle',{EEG.marks.comp_info.label})); +eye_ind=find(strcmp('eye',{EEG.marks.comp_info.label})); +heart_ind=find(strcmp('heart',{EEG.marks.comp_info.label})); +line_noise_ind=find(strcmp('line_noise',{EEG.marks.comp_info.label})); +chan_noise_ind=find(strcmp('chan_noise',{EEG.marks.comp_info.label})); +other_ind=find(strcmp('other',{EEG.marks.comp_info.label})); +ambig_ind=find(strcmp('ambig',{EEG.marks.comp_info.label})); + +EEG.marks.comp_info(manual_ind).order=1; +if ~isempty(ch_s_sd_ind); % skip study-wide criteria if not used + EEG.marks.comp_info(ch_s_sd_ind).order=2; +end; +EEG.marks.comp_info(ch_sd_ind).order=2; +EEG.marks.comp_info(low_r_ind).order=2; +EEG.marks.comp_info(bridge_ind).order=2; +EEG.marks.comp_info(rank_ind).order=2; +EEG.marks.comp_info(icrt_ind).order=1; + +EEG.marks.comp_info(brain_ind).order=2; +EEG.marks.comp_info(muscle_ind).order=2; +EEG.marks.comp_info(eye_ind).order=2; +EEG.marks.comp_info(heart_ind).order=2; +EEG.marks.comp_info(line_noise_ind).order=2; +EEG.marks.comp_info(chan_noise_ind).order=2; +EEG.marks.comp_info(other_ind).order=2; +EEG.marks.comp_info(ambig_ind).order=3; + +bioa_inds = marks_label2index(EEG.marks.comp_info,{'muscle','eye','heart','chan_noise'},'indexes'); +EEG.marks.comp_info(manual_ind).flags=zeros(min(size(EEG.icaweights)),1); +EEG.marks.comp_info(manual_ind).flags(bioa_inds)=1; + +% BIDS Export calls +% Dot for path is on purpose. Don't worry - it's safe. +fName = '.[batch_dfp]/[out_path]/[batch_dfn]'; +pop_writeeeg(EEG, strrep(fName,'_eeg.edf','_desc-ll_eeg.edf'), 'TYPE','EDF'); +dlmwrite(strrep(fName,'_eeg.edf','_desc-ll_icaweights.tsv'),EEG.icaweights,'\t'); +dlmwrite(strrep(fName,'_eeg.edf','_desc-ll_icasphere.tsv'),EEG.icasphere,'\t'); +s = {}; +s.algorithm = 'amica15'; +s.icachansind = EEG.icachansind; +s.intendedFor = fName; +savejson('',s,strrep(fName,'_eeg.edf','_desc-ll_icaweights.json')); +export_anno(EEG,fName,'ll'); + +% Should also export the dipole information +dipFname = strrep(fName,'_eeg.edf','_desc-ll_dipole.mat'); +dipVar = EEG.dipfit; +save(dipFname,'dipVar'); + +% ICLabel quick export +iclFname = strrep(fName,'_eeg.edf','_desc-ll_iclabel.mat'); +save(iclFname, 'tmp'); % see above ICLabel section for tmp definition diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/tools/README.md b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/tools/README.md new file mode 100644 index 000000000..7691c18a6 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/tools/README.md @@ -0,0 +1,2 @@ +# The Lossless Pipeline - Tools +Contains a set of bash/command line tools that can make working with clusters easier. \ No newline at end of file diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/tools/diagnostics/diagnostics.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/tools/diagnostics/diagnostics.m new file mode 100644 index 000000000..ece3f320a --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/tools/diagnostics/diagnostics.m @@ -0,0 +1,511 @@ +function diagnostics(ALLEEG,type) +% diagnostics(ALLEEG,type) - Creates figures to aid user in understanding +% the generated marks, and the overal quality of the data. +% +% Required Inputs: +% ALLEEG = the ALLEEG structure resulting from loading single or multiple +% eeg *.set files. +% type = 'single' or 'study', single will resault in many detailed +% plots about the currently selected EEG set. Study will +% display more broad plots summarizing all of the loaded files. +% +% Usage: +% diagnostics(ALLEEG,'study'); +% +% Notes: +% This function only works on completed dipfit data at the moment. A future +% version will be compatibple with ASR marks. +% See also: dipfit.htb + +%Copyright (C) 2017 BUCANL +% +%Code originally written by James A. Desjardins with contributions from +%Allan Campopiano and Andrew Lofts, supported by NSERC funding to +%Sidney J. Segalowitz at the Jack and Nora Walker Canadian Centre for +%Lifespan Development Research (Brock University), and a Dedicated Programming +%award from SHARCNet, Compute Ontario. +% +%This program is free software; you can redistribute it and/or modify +%it under the terms of the GNU General Public License as published by +%the Free Software Foundation; either version 2 of the License, or +%(at your option) any later version. +% +%This program is distributed in the hope that it will be useful, +%but WITHOUT ANY WARRANTY; without even the implied warranty of +%MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +%GNU General Public License for more details. +% +%You should have received a copy of the GNU General Public License +%along with this program (LICENSE.txt file in the root directory); if not, +%write to the Free Software Foundation, Inc., 59 Temple Place, +%Suite 330, Boston, MA 02111-1307 USA + +%% Call for study subject diagnostics +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +if strcmp(type,'study') + + %% Channels + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + for i = 1:length(ALLEEG) + % Divide by b1 to make %s + ball = length(ALLEEG(i).marks.chan_info(1).flags); % All time + b2 = length(find(ALLEEG(i).marks.chan_info(2).flags))/ball; % 2 + b3 = length(find(ALLEEG(i).marks.chan_info(3).flags))/ball; % 3 + b4 = length(find(ALLEEG(i).marks.chan_info(4).flags))/ball; % 4 + bnm = 1-b2-b3-b4; % not manual/remaining + + bdata1(i,:) = [bnm,b2,b3,b4]; + end + + % BAR CHART + figure; + ax1 = subplot(2,1,1); + bar(ax1,1:length(ALLEEG),bdata1(:,:),'stacked','Horizontal','on');colormap(jet); + title('Channel Designation'); xlabel('% of Channels (Sum to 100%)'); ylabel('Subject Number'); + legend({'Remaining Channels','Comically Bad','Low Correlation','Bridged'}); + %axis([0 1 0 (length(EEG.reject.probabilities) + 1)]); + + %% Time + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + for i = 1:length(ALLEEG) + % Divide by b1 to make %s + ball = length(ALLEEG(i).marks.time_info(1).flags); % All time + b2 = length(find(ALLEEG(i).marks.time_info(2).flags))/ball; % 2 + b3 = length(find(ALLEEG(i).marks.time_info(3).flags))/ball; % 3 + b4 = length(find(ALLEEG(i).marks.time_info(4).flags))/ball; % 4 + b6 = length(find(ALLEEG(i).marks.time_info(6).flags))/ball; % 6 + b10 = length(find(ALLEEG(i).marks.time_info(10).flags))/ball; % 10 + bnm = 1-b2-b3-b4-b6-b10; % not manual/remaining + + bdata2(i,:) = [bnm,b2,b3,b4,b6,b10]; + end + + % BAR CHART + ax2 = subplot(2,1,2); + bar(ax2,1:length(ALLEEG),bdata2(:,:),'stacked','Horizontal','on');colormap(jet); + title('Time Designation'); xlabel('% of Time (Sum to 100%)'); ylabel('Subject Number'); + legend({'Remaining Channels','Out Task','Marks Gap','Low Correlation','IC SD 1','IC SD 2'}); + %axis([0 1 0 (length(EEG.reject.probabilities) + 1)]); + + %% ASR CORRECTION + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % Amount of datd that was ASR corrected + + %% Components - 1 - Marks + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + figure; + for i = 1:length(ALLEEG) + % Divide by b1 to make %s + ball = length(ALLEEG(i).marks.comp_info(1).flags); % All time + bnm = (ball - length(find(ALLEEG(i).marks.comp_info(1).flags)))/ball; % not manual/remaining + b2 = (length(find(ALLEEG(i).marks.comp_info(2).flags)))/ball; % 2 + b3 = (length(find(ALLEEG(i).marks.comp_info(3).flags)))/ball; % 3 + bb = abs(1 - bnm - b2 - b3); + b2 = b2 - bb; + b3 = b3 - bb; + bdata3(i,:) = [bnm,b2,b3,bb]; + end + + % BAR CHART + ax3 = subplot(2,1,1); + bar(ax3,1:length(ALLEEG),bdata3(:,:),'stacked','Horizontal','on');colormap(jet); + title('Component Designation'); xlabel('% of Components (Sum to 100%)'); ylabel('Subject Number'); + legend({'Remaining Components','Not Replicated','Dipole Matching', 'Both reasons'}); + %axis([0 1 0 (length(EEG.reject.probabilities) + 1)]); + + %% Components - 2 - ICMARC + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + for i = 1:length(ALLEEG) + % Divide by ball for % + ball = length((ALLEEG(i).reject.classtype == 1)); + b1 = length(find(ALLEEG(i).reject.classtype == 1))/ball; + b2 = length(find(ALLEEG(i).reject.classtype == 2))/ball; + b3 = length(find(ALLEEG(i).reject.classtype == 3))/ball; + b4 = length(find(ALLEEG(i).reject.classtype == 4))/ball; + b5 = length(find(ALLEEG(i).reject.classtype == 5))/ball; + b6 = length(find(ALLEEG(i).reject.classtype == 6))/ball; + + bdata4(i,:) = [b1,b2,b3,b4,b5,b6]; + end + + % BAR CHART + ax4 = subplot(2,1,2); + bar(ax4,1:length(ALLEEG),bdata4(:,:),'stacked','Horizontal','on');colormap(jet); + title('ICMARC Classification'); xlabel('% of Components (Sum to 100%)'); ylabel('Subject Number'); + legend({ 'blink','neural','heart','lateye','muscle','mixed'}); + %axis([0 1 0 (length(EEG.reject.probabilities) + 1)]); + +end + + + +%% Call for single subject diagnostics +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +if strcmp(type,'single') + + EEG = ALLEEG(1); + + %% Channels removed + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + fprintf('\nChannels\n'); + fprintf('==================================================\n'); + for j = 1:length(EEG.marks.chan_info) + %just flags + x = length(find(EEG.marks.chan_info(j).flags)); + % All chans + y = length(EEG.marks.chan_info(j).flags); + disp(['Flagged: ' num2str(x) '/' num2str(y) ' Channels for ' EEG.marks.chan_info(j).label]); + end + + % outtask vs manual vs time remaining piechart + c1 = length(EEG.marks.chan_info(1).flags); % All time + c2 = length(find(EEG.marks.chan_info(2).flags)); % 2 + c3 = length(find(EEG.marks.chan_info(3).flags)); % 3 + c4 = length(find(EEG.marks.chan_info(4).flags)); % 4 + cm = length(find(EEG.marks.chan_info(1).flags)); % 5 + c1 = c1-cm; % Remaining All time + cpie = [c1 c2 c3 c4]; + figure; + explode = [1 0 0 0]; + ax1 = subplot(1,2,1); + labels = {'Remaining Channels','Comically Bad','Low Correlation','Bridged'}; + pie(ax1,cpie,explode,labels); colormap(jet); + title('Data Channel Classification'); + + %% Time removed + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + fprintf('\nTime\n'); + fprintf('==================================================\n'); + for j = 1:length(EEG.marks.time_info) + %just flags + x = length(find(EEG.marks.time_info(j).flags)); + % All time + y = length(EEG.marks.time_info(j).flags); + disp(['Flagged: ' num2str((x/y)*100) ' % of Time for ' EEG.marks.time_info(j).label]); + end + + % outtask vs manual vs time remaining piechart + c1 = length(EEG.marks.time_info(1).flags); % All time + c2 = length(find(EEG.marks.time_info(2).flags)); % 2 Out Task + c3 = length(find(EEG.marks.time_info(3).flags)); % 3 + c4 = length(find(EEG.marks.time_info(4).flags)); % 4 + c5 = length(find(EEG.marks.time_info(6).flags)); % 6 + c6 = length(find(EEG.marks.time_info(10).flags)); % 10 + cm = length(find(EEG.marks.time_info(1).flags)); % manual + c1 = c1-cm; % Remaining All time + cpie = [c1 c2 c3 c4 c5 c6]; + explode = [1 1 0 0 0 0]; + ax2 = subplot(1,2,2); + labels = {'Remaining Time Data','sd of volt','Mark Gap','Low Correlation','ICA SD 1','ICA SD 2'}; + pie(ax2,cpie,explode,labels); colormap(hot); + title('Data Time Classification'); + + %% Components + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + fprintf('\nICA Components\n'); + fprintf('==================================================\n'); + disp(['Identified ' num2str(min(size(EEG.icaweights))) ' components from ' num2str(length(EEG.icachansind)) ' channels']); + + % PIE CHART + c1 = length(find(EEG.reject.classtype == 1)); + c2 = length(find(EEG.reject.classtype == 2)); + c3 = length(find(EEG.reject.classtype == 3)); + c4 = length(find(EEG.reject.classtype == 4)); + c5 = length(find(EEG.reject.classtype == 5)); + c6 = length(find(EEG.reject.classtype == 6)); + cpie = [c1 c2 c3 c4 c5 c6]; + figure; + ax1 = subplot(1,2,1); + explode = [0 1 0 0 0 0]; + labels = { 'blink','neural','heart','lateye','muscle','mixed'}; + pie(ax1,cpie,explode,labels);colormap(jet); + title('Component Majority Classifications'); + + % BAR CHART + ax2 = subplot(1,2,2); + bar(ax2,1:length(EEG.reject.probabilities),EEG.reject.probabilities(:,:),'stacked','Horizontal','on');colormap(jet); + title('Component Composistion'); xlabel('% Composition (Sum to 100%)'); ylabel('Component Number'); + axis([0 1 0 (length(EEG.reject.probabilities) + 1)]); + + % MULTI PIE CHART + % figure; + % for j = 1:size(EEG.reject.probabilities,1) + % s1 = EEG.reject.probabilities(j,:); + % explode = [0 0 0 0 0 0]; + % explode(find((EEG.reject.probabilities(4,:))==(max(EEG.reject.probabilities(4,:))))) = 1; + % labels = { 'B','N','H','LE','M','Mix'}; + % ax1 = subplot(10,10,j); + % pie(s1,explode,labels);colormap(jet); + % title(ax1,['Channel: ' num2str(j)]); + % end + + %% Correlations + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + fprintf('\nICA Correlations\n'); + fprintf('==================================================\n'); + + % channel x epoch + % figure; + % for j = 1:size(EEG.icaact_sd_t,2) + % hold on; + % scatter(1:size(EEG.icaact_sd_t,2),EEG.icaact_sd_t(j,:)); + % end + % + % + % figure; + % for j = 1:size(EEG.icaact_sd_t,2) + % hold on; + % scatter(1:size(EEG.c_icaact_sd_t,2),EEG.c_icaact_sd_t(j,:)); + % end + + %% Standard deviation (mc_data_sd_ch) + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % Data + z1 = EEG.data_sd_ch; + z1(z1>100) = 100; % Cap + + % Surface Plot + figure; + ax1 = subplot(1,3,1); + surf(ax1,z1,'LineStyle','none','FaceAlpha',0.9);view(0,90); + title('Standard Deviations'); xlabel('Epoch Number'); ylabel('Channel Number'); + axis([0 size(z1,2) 0 size(z1,1)]); + ax2 = subplot(1,3,2); + surf(ax2,EEG.c_data_sd_ch,'LineStyle','none','FaceAlpha',0.9);view(0,90); colormap(jet); + title('Flags'); xlabel('Epoch Number'); ylabel('Channel Number'); + axis([0 size(z1,2) 0 size(z1,1)]); + + % Waveform + critval data + x = mean(EEG.c_data_sd_ch,2); % Average of flags + y = 1:size(EEG.c_data_sd_ch,1); % Channels + x(:,2) = 0.1; % 10% crit in config + ax3 = subplot(1,3,3); + plot(ax3,x,y); + title('Mean of Marks Critical Value'); xlabel('% 0f Channel Flagged'); ylabel('Channel Number'); + axis([-0.05 1 0 (length(x + 1))]); + + %% Correlation by channel (mc_data_r_ch) + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % Data + z1 = EEG.data_r_ch; + z1(z1>100) = 100; % Cap + + % Surface Plot + figure; + ax1 = subplot(1,4,1); + surf(ax1,z1,'LineStyle','none','FaceAlpha',0.9);view(0,90); + title('Channel Correlations'); xlabel('Epoch Number'); ylabel('Channel Number'); + axis([0 size(z1,2) 0 size(z1,1)]); + + % Flags Plot + ax2 = subplot(1,4,2); + surf(ax2,EEG.c_data_r_ch,'LineStyle','none','FaceAlpha',0.9);view(0,90); colormap(jet); + title('Flags'); xlabel('Epoch Number'); ylabel('Channel Number'); + axis([0 size(z1,2) 0 size(z1,1)]); + + % Waveform + critval data plot + x = mean(EEG.c_data_r_ch,2); % Average of flags + y = 1:size(EEG.c_data_r_ch,1); % Channels + x(:,2) = 0.1; % 10% crit in config + ax3 = subplot(1,4,3); + plot(ax3,x,y); + title('Mean of Marks Critical Value'); xlabel('% 0f Channel Flagged'); ylabel('Channel Number'); + axis([-0.05 1 0 (length(x + 1))]); + + % bridge measure + mr = mean(fisherz(EEG.data_r_ch),2); + sr = std(fisherz(EEG.data_r_ch),[],2); + x = mr./sr; + y = 1:size(EEG.c_data_r_ch,1); % Channels + x(:,2) = zeros(size(x(:,1)))+(ve_trimmean(x(:,1),20)+ve_trimstd(x(:,1),20)*6); % 10% crit in config + ax3 = subplot(1,4,4); + plot(ax3,x,y); + title('mean corr/sd'); xlabel('corr mean/sd'); ylabel('Channel Number'); + axis([-0.05 max(x(:,1))+5 0 (length(x + 1))]); + + %% Standard Deviation of activation by Time (mc_data_r_t) + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % Data + z1 = EEG.data_sd_t; + z1(z1<0.5) = 0.5; % Cap + + % Surface Plot + figure; + ax1 = subplot(3,1,1); + surf(ax1,(z1),'LineStyle','none','FaceAlpha',0.9);view(0,90); + title('Time Standard deviation of activation'); xlabel('Epoch Number'); ylabel('Channel Number') + axis([0 size(z1,2) 0 size(z1,1)]); + + % Flags Plot + ax2 = subplot(3,1,2); + surf(ax2,EEG.c_data_sd_t,'LineStyle','none','FaceAlpha',0.9);view(0,90); colormap(jet); + title('Flags'); xlabel('Epoch Number'); ylabel('Channel Number'); + axis([0 size(z1,2) 0 size(z1,1)]); + + % Waveform + critval data plot + y = mean(EEG.c_data_sd_t,1); % Average of flags + x = 1:size(EEG.c_data_sd_t,2); % Channels + y(2,:) = 0.2; % 20% crit in config + ax3 = subplot(3,1,3); + plot(ax3,x,y); + title('Mean of Marks Critical Value'); xlabel('Epoch Number'); ylabel('% of Channels flagged'); + axis([ 0 (length(x + 1)) -0.05 1]); + + %% Correlation by Time (mc_data_r_t) + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % Data + z1 = EEG.data_r_t; + z1(z1<0.5) = 0.5; % Cap + + % Surface Plot + figure; + ax1 = subplot(3,1,1); + surf(ax1,(z1),'LineStyle','none','FaceAlpha',0.9);view(0,90); + title('Time Cross Electrode Correlation'); xlabel('Epoch Number'); ylabel('Channel Number') + axis([0 size(z1,2) 0 size(z1,1)]); + + % Flags Plot + ax2 = subplot(3,1,2); + surf(ax2,EEG.c_data_r_t,'LineStyle','none','FaceAlpha',0.9);view(0,90); colormap(jet); + title('Flags'); xlabel('Epoch Number'); ylabel('Channel Number'); + axis([0 size(z1,2) 0 size(z1,1)]); + + % Waveform + critval data plot + y = mean(EEG.c_data_r_t,1); % Average of flags + x = 1:size(EEG.c_data_r_t,2); % Channels + y(2,:) = 0.2; % 20% crit in config + ax3 = subplot(3,1,3); + plot(ax3,x,y); + title('Mean of Marks Critical Value'); xlabel('Epoch Number'); ylabel('% of Channels flagged'); + axis([ 0 (length(x + 1)) -0.05 1]); + + %% first IC_SD by Time (mc_icaact_sd_t) + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % EEG.icaact_sd1_t EEG.c_icaact_sd1_t + % EEG.icaact_sd2_t EEG.c_icaact_sd2_t + % EEG.icaact_sd1asr_t EEG.c_icaact_sd1asr_t + % EEG.icaact_sd2asr_t EEG.c_icaact_sd2asr_t + + % Data + z1 = EEG.icaact_sd1_t; + z1(z1>2) = 2.236; % Cap + + % Surface Plot + figure; + ax1 = subplot(3,1,1); + surf(ax1,(z1),'LineStyle','none','FaceAlpha',0.9);view(0,90); + title('Time Component Standard Deviation'); xlabel('Epoch Number'); ylabel('Component Number') + axis([0 size(z1,2) 0 size(z1,1)]); + + % Flags Plot + ax2 = subplot(3,1,2); + surf(ax2,EEG.c_icaact_sd_t,'LineStyle','none','FaceAlpha',0.9);view(0,90); colormap(jet); + title('Flags'); xlabel('Epoch Number'); ylabel('Component Number'); + axis([0 size(z1,2) 0 size(z1,1)]); + + % Waveform + critval data plot + y = mean(EEG.c_icaact_sd_t,1); % Average of flags + x = 1:size(EEG.c_icaact_sd_t,2); % Channels + y(2,:) = 0.1; % 10% crit in config + ax3 = subplot(3,1,3); + plot(ax3,x,y); + title('Mean of Marks Critical Value'); xlabel('Epoch Number'); ylabel('% of Channels flagged'); + axis([ 0 (length(x + 1)) -0.05 1]); + + %% second IC_SD by Time (mc_icaact_sd_t) + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % EEG.icaact_sd1_t EEG.c_icaact_sd1_t + % EEG.icaact_sd2_t EEG.c_icaact_sd2_t + % EEG.icaact_sd1asr_t EEG.c_icaact_sd1asr_t + % EEG.icaact_sd2asr_t EEG.c_icaact_sd2asr_t + + % Data + z1 = EEG.icaact_sd2_t; + z1(z1>2) = 2.236; % Cap + + % Surface Plot + figure; + ax1 = subplot(3,1,1); + surf(ax1,(z1),'LineStyle','none','FaceAlpha',0.9);view(0,90); + title('Time Component Standard Deviation'); xlabel('Epoch Number'); ylabel('Component Number') + axis([0 size(z1,2) 0 size(z1,1)]); + + % Flags Plot + ax2 = subplot(3,1,2); + surf(ax2,EEG.c_icaact_sd2_t,'LineStyle','none','FaceAlpha',0.9);view(0,90); colormap(jet); + title('Flags'); xlabel('Epoch Number'); ylabel('Component Number'); + axis([0 size(z1,2) 0 size(z1,1)]); + + % Waveform + critval data plot + y = mean(EEG.c_icaact_sd2_t,1); % Average of flags + x = 1:size(EEG.c_icaact_sd2_t,2); % Channels + y(2,:) = 0.1; % 10% crit in config + ax3 = subplot(3,1,3); + plot(ax3,x,y); + title('Mean of Marks Critical Value'); xlabel('Epoch Number'); ylabel('% of Channels flagged'); + axis([ 0 (length(x + 1)) -0.05 1]); + + %% Alpha Power + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % Data + z1 = EEG.icaact_ta_t; + z1(z1>100) = 100; % Cap + + % Surface Plot + figure; + ax1 = subplot(3,2,1); + surf(ax1,(z1),'LineStyle','none','FaceAlpha',0.9);view(0,90); + title('Component Alpha Power'); xlabel('Epoch Number'); ylabel('Component Number') + axis([0 size(z1,2) 0 size(z1,1)]); + + % Flags Plot + ax2 = subplot(3,2,3); + surf(ax2,EEG.c_icaact_ta_t,'LineStyle','none','FaceAlpha',0.9);view(0,90); colormap(jet); + title('Flags'); xlabel('Epoch Number'); ylabel('Component Number'); + axis([0 size(z1,2) 0 size(z1,1)]); + + % Waveform + critval data plot + y = mean(EEG.c_icaact_ta_t,1); % Average of flags + x = 1:size(EEG.c_icaact_ta_t,2); % Channels + y(2,:) = 0.1; % 10% crit in config + ax3 = subplot(3,2,5); + plot(ax3,x,y); + title('Mean of Marks Critical Value'); xlabel('Epoch Number'); ylabel('% of Channels flagged'); + axis([ 0 (length(x + 1)) -0.05 1]); + + %% Beta Power + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + % Data + z1 = EEG.icaact_b_t; + z1(z1>100) = 100; % Cap + + % Surface Plot + ax1 = subplot(3,2,2); + surf(ax1,(z1),'LineStyle','none','FaceAlpha',0.9);view(0,90); + title('Component Beta Power'); xlabel('Epoch Number'); ylabel('Component Number') + axis([0 size(z1,2) 0 size(z1,1)]); + + % Flags Plot + ax2 = subplot(3,2,4); + surf(ax2,EEG.c_icaact_b_t,'LineStyle','none','FaceAlpha',0.9);view(0,90); colormap(jet); + title('Flags'); xlabel('Epoch Number'); ylabel('Component Number'); + axis([0 size(z1,2) 0 size(z1,1)]); + + % Waveform + critval data plot + y = mean(EEG.c_icaact_b_t,1); % Average of flags + x = 1:size(EEG.c_icaact_b_t,2); % Channels + y(2,:) = 0.1; % 10% crit in config + ax3 = subplot(3,2,6); + plot(ax3,x,y); + title('Mean of Marks Critical Value'); xlabel('Epoch Number'); ylabel('% of Channels flagged'); + axis([ 0 (length(x + 1)) -0.05 1]); + + %% ASR Correction + % Percent time corrected via ASR - intask + % Percent time corrected via ASR - outtask +end + + +end % End of function + diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/tools/diagnostics/eeg_diagnostics.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/tools/diagnostics/eeg_diagnostics.m new file mode 100644 index 000000000..fb25c3534 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/tools/diagnostics/eeg_diagnostics.m @@ -0,0 +1,326 @@ +function eeg_diagnostics(EEG,batch_config) +% diagnostics(ALLEEG,type) - Creates figures to aid user in understanding +% the generated marks, and the overal quality of the data. +% +%Copyright (C) 2017 BUCANL +% +%Code originally written by James A. Desjardins with contributions from +%Allan Campopiano and Andrew Lofts, supported by NSERC funding to +%Sidney J. Segalowitz at the Jack and Nora Walker Canadian Centre for +%Lifespan Development Research (Brock University), and a Dedicated Programming +%award from SHARCNet, Compute Ontario. +% +%This program is free software; you can redistribute it and/or modify +%it under the terms of the GNU General Public License as published by +%the Free Software Foundation; either version 2 of the License, or +%(at your option) any later version. +% +%This program is distributed in the hope that it will be useful, +%but WITHOUT ANY WARRANTY; without even the implied warranty of +%MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +%GNU General Public License for more details. +% +%You should have received a copy of the GNU General Public License +%along with this program (LICENSE.txt file in the root directory); if not, +%write to the Free Software Foundation, Inc., 59 Temple Place, +%Suite 330, Boston, MA 02111-1307 USA + + %% Channels removed + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + fprintf('\nChannels\n'); + fprintf('==================================================\n'); + for j = 1:length(EEG.marks.chan_info) + %just flags + x = length(find(EEG.marks.chan_info(j).flags)); + % All chans + y = length(EEG.marks.chan_info(j).flags); + disp(['Flagged: ' num2str(x) '/' num2str(y) ' Channels for ' EEG.marks.chan_info(j).label]); + end + + fin_underscore = regexp(EEG.filename,'_'); + fin_underscore = fin_underscore(end); + base_name = EEG.filename(1:fin_underscore-1); + + % outtask vs manual vs time remaining piechart + ca = length(EEG.marks.chan_info(1).flags); % All time + c2 = length(find(EEG.marks.chan_info(2).flags)); % 2 + c3 = length(find(EEG.marks.chan_info(3).flags)); % 3 + c4 = length(find(EEG.marks.chan_info(4).flags)); % 4 + cm = length(find(EEG.marks.chan_info(1).flags)); % 5 + ca = ca-cm; % Remaining All time + cpie = [ca c2 c3 c4]; + figure; + explode = [1 0 0 0]; + %ax1 = subplot(1,2,1); + labels = {'Remaining Channels','ch-sd','low-r','bridge'}; + pie(cpie,explode,labels); colormap(jet); + title('Data Channel Classification'); + + %% Time removed + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + fprintf('\nTime\n'); + fprintf('==================================================\n'); + for j = 1:length(EEG.marks.time_info) + %just flags + x = length(find(EEG.marks.time_info(j).flags)); + % All time + y = length(EEG.marks.time_info(j).flags); + disp(['Flagged: ' num2str((x/y)*100) ' % of Time for ' EEG.marks.time_info(j).label]); + end + + % outtask vs manual vs time remaining piechart + ca = length(EEG.marks.time_info(1).flags); % All time + + co=length(marks_label2index(EEG.marks.time_info, ... + marks_match_label({'out_task','in_leadup'},{EEG.marks.time_info.label}), ... + 'indices')); + + mgap=length(marks_label2index(EEG.marks.time_info, ... + marks_match_label('mark_gap',{EEG.marks.time_info.label}), ... + 'indices')); + + chsd=length(marks_label2index(EEG.marks.time_info, ... + marks_match_label('ch_sd',{EEG.marks.time_info.label}), ... + 'indices')); + + lowr=length(marks_label2index(EEG.marks.time_info, ... + marks_match_label('low_r',{EEG.marks.time_info.label}), ... + 'indices')); + + icsd1=length(marks_label2index(EEG.marks.time_info, ... + marks_match_label('ic_sd1',{EEG.marks.time_info.label}), ... + 'indices')); + + icsd2=length(marks_label2index(EEG.marks.time_info, ... + marks_match_label('ic_sd2',{EEG.marks.time_info.label}), ... + 'indices')); + + % c2 = length(find(EEG.marks.time_info(2).flags)); % 2 Out Task +% c3 = length(find(EEG.marks.time_info(3).flags)); % 3 +% c4 = length(find(EEG.marks.time_info(4).flags)); % 4 +% c5 = length(find(EEG.marks.time_info(6).flags)); % 6 +% c6 = length(find(EEG.marks.time_info(10).flags)); % 10 + +cm=length(marks_label2index(EEG.marks.time_info, ... + marks_match_label('manual',{EEG.marks.time_info.label}), ... + 'indices')); +%cm = length(find(EEG.marks.time_info(1).flags)); % manual +cr = ca-cm; % Remaining All time +cpie = [cr co mgap chsd lowr icsd1 icsd2]; +figure; +explode = [1 0 0 0 0 0 0]; +%ax2 = subplot(1,2,2); +labels = {'Remaining Time','out-task','mark-gap','ch-sd','low-r','ic-sd1','ic-sd2'}; +pie(cpie,explode,labels); colormap(jet); +title('Data Time Classification'); + +%% Components +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +fprintf('\nICA Components\n'); +fprintf('==================================================\n'); +disp(['Identified ' num2str(min(size(EEG.icaweights))) ' components from ' num2str(length(EEG.icachansind)) ' channels']); + +% PIE CHART +ca = length(find(EEG.reject.classtype == 1)); +c2 = length(find(EEG.reject.classtype == 2)); +c3 = length(find(EEG.reject.classtype == 3)); +c4 = length(find(EEG.reject.classtype == 4)); +c5 = length(find(EEG.reject.classtype == 5)); +c6 = length(find(EEG.reject.classtype == 6)); +cpie = [ca c2 c3 c4 c5 c6]; +figure; +%ax1 = subplot(1,2,1); +explode = [0 1 0 0 0 0]; +labels = { 'blink','neural','heart','lateye','muscle','mixed'}; +pie(cpie,explode,labels);colormap(jet); +title('Component Majority Classifications'); + +% BAR CHART +%ax2 = subplot(1,2,2); +figure; +bar(1:length(EEG.reject.probabilities),EEG.reject.probabilities(:,:),'stacked','Horizontal','on');colormap(jet); +title('Component Composistion'); xlabel('% Composition (Sum to 100%)'); ylabel('Component Number'); +axis([0 1 0 (length(EEG.reject.probabilities) + 1)]); + +% MULTI PIE CHART +% figure; +% for j = 1:size(EEG.reject.probabilities,1) +% s1 = EEG.reject.probabilities(j,:); +% explode = [0 0 0 0 0 0]; +% explode(find((EEG.reject.probabilities(4,:))==(max(EEG.reject.probabilities(4,:))))) = 1; +% labels = { 'B','N','H','LE','M','Mix'}; +% ax1 = subplot(10,10,j); +% pie(s1,explode,labels);colormap(jet); +% title(ax1,['Channel: ' num2str(j)]); +% end + +%ch_sd time +s='[sd_t_meth]'; +sd_t_meth=strtrim(batch_config(1).replace_string{find(strncmp(batch_config(1).replace_string,s,length(s)))}(length(s)+2:end)); +s='[sd_t_vals]'; +sd_t_vals=batch_config(1).replace_string{find(strncmp(batch_config(1).replace_string,s,length(s)))}(length(s)+2:end); +s='[sd_t_o]'; +sd_t_o=batch_config(1).replace_string{find(strncmp(batch_config(1).replace_string,s,length(s)))}(length(s)+2:end); +s='[sd_t_p]'; +sd_t_p=batch_config(1).replace_string{find(strncmp(batch_config(1).replace_string,s,length(s)))}(length(s)+2:end); + +data_sd_t = load([EEG.filepath base_name '_data_sd_t.mat']); +fieldname = fieldnames(data_sd_t); +fieldname = fieldname{1}; +data_sd_t = data_sd_t.(fieldname); + +if ~strcmp('na',sd_t_meth); + marks_array2flags(data_sd_t, ... + 'flag_dim','col', ... + 'init_method',sd_t_meth, ... + 'init_vals',str2num(sd_t_vals), ... + 'init_crit',str2num(sd_t_o), ... + 'flag_method','fixed', ... + 'flag_val',str2num(sd_t_p), ... + 'plot_figs','on', ... + 'title_prefix','SD time - '); +end + +%ch_sd channel +s='[sd_ch_meth]'; +sd_ch_meth=strtrim(batch_config(1).replace_string{find(strncmp(batch_config(1).replace_string,s,length(s)))}(length(s)+2:end)); +s='[sd_ch_vals]'; +sd_ch_vals=batch_config(1).replace_string{find(strncmp(batch_config(1).replace_string,s,length(s)))}(length(s)+2:end); +s='[sd_ch_o]'; +sd_ch_o=batch_config(1).replace_string{find(strncmp(batch_config(1).replace_string,s,length(s)))}(length(s)+2:end); +s='[sd_ch_p]'; +sd_ch_p=batch_config(1).replace_string{find(strncmp(batch_config(1).replace_string,s,length(s)))}(length(s)+2:end); + +data_sd_ch = load([EEG.filepath base_name '_data_sd_ch.mat']); +fieldname = fieldnames(data_sd_ch); +fieldname = fieldname{1}; +data_sd_ch = data_sd_ch.(fieldname); + +marks_array2flags(data_sd_ch, ... + 'flag_dim','row', ... + 'init_method',sd_ch_meth, ... + 'init_vals',str2num(sd_ch_vals), ... + 'init_crit',str2num(sd_ch_o), ... + 'flag_method','fixed', ... + 'flag_val',str2num(sd_ch_p), ... + 'plot_figs','on', ... + 'title_prefix','SD channel - '); + +%low_r channel +s='[r_ch_meth]'; +r_ch_meth=strtrim(batch_config(1).replace_string{find(strncmp(batch_config(1).replace_string,s,length(s)))}(length(s)+2:end)); +s='[r_ch_vals]'; +r_ch_vals=batch_config(1).replace_string{find(strncmp(batch_config(1).replace_string,s,length(s)))}(length(s)+2:end); +s='[r_ch_o]'; +r_ch_o=batch_config(1).replace_string{find(strncmp(batch_config(1).replace_string,s,length(s)))}(length(s)+2:end); +s='[r_ch_p]'; +r_ch_p=batch_config(1).replace_string{find(strncmp(batch_config(1).replace_string,s,length(s)))}(length(s)+2:end); + +data_r_ch = load([EEG.filepath base_name '_data_r_ch.mat']); +fieldname = fieldnames(data_r_ch); +fieldname = fieldname{1}; +data_r_ch = data_r_ch.(fieldname); + +marks_array2flags(data_r_ch, ... + 'flag_dim','row', ... + 'init_method',r_ch_meth, ... + 'init_vals',str2num(r_ch_vals), ... + 'init_crit',str2num(r_ch_o), ... + 'flag_method','fixed', ... + 'flag_val',str2num(r_ch_p), ... + 'plot_figs','on', ... + 'title_prefix','low R channel - '); + +%bridge channel +mr = median(data_r_ch,2); +iqrr = iqr(data_r_ch,2); +msr = mr./iqrr; +figure; +plot(msr); +hold on; +plot(ones(1,length(msr))*(ve_trimmean(msr,50)+ve_trimstd(msr,50)*6),'r') +view(270,90); +axis tight; +title('High R (low variant) bridge cutoff values') + +%low_r time +s='[r_t_meth]'; +r_t_meth=strtrim(batch_config(1).replace_string{find(strncmp(batch_config(1).replace_string,s,length(s)))}(length(s)+2:end)); +s='[r_t_vals]'; +r_t_vals=batch_config(1).replace_string{find(strncmp(batch_config(1).replace_string,s,length(s)))}(length(s)+2:end); +s='[r_t_o]'; +r_t_o=batch_config(1).replace_string{find(strncmp(batch_config(1).replace_string,s,length(s)))}(length(s)+2:end); +s='[r_t_p]'; +r_t_p=batch_config(1).replace_string{find(strncmp(batch_config(1).replace_string,s,length(s)))}(length(s)+2:end); + +data_r_t = load([EEG.filepath base_name '_data_r_t.mat']); +fieldname = fieldnames(data_r_t); +fieldname = fieldname{1}; +data_r_t = data_r_t.(fieldname); + +marks_array2flags(data_r_t, ... + 'flag_dim','col', ... + 'init_method',r_t_meth, ... + 'init_vals',str2num(r_t_vals), ... + 'init_crit',str2num(r_t_o), ... + 'flag_method','fixed', ... + 'flag_val',str2num(r_t_p), ... + 'plot_figs','on', ... + 'title_prefix','Low R time - '); + +%ic_sd1 time +s='[sd_t_meth]'; +sd_t_meth=strtrim(batch_config(3).replace_string{find(strncmp(batch_config(3).replace_string,s,length(s)))}(length(s)+2:end)); +s='[sd_t_vals]'; +sd_t_vals=batch_config(3).replace_string{find(strncmp(batch_config(3).replace_string,s,length(s)))}(length(s)+2:end); +s='[sd_t_o]'; +sd_t_o=batch_config(3).replace_string{find(strncmp(batch_config(3).replace_string,s,length(s)))}(length(s)+2:end); +s='[sd_t_p]'; +sd_t_p=batch_config(3).replace_string{find(strncmp(batch_config(3).replace_string,s,length(s)))}(length(s)+2:end); + +icaact_sd1_t = load([EEG.filepath base_name '_icaact_sd1_t.mat']); +fieldname = fieldnames(icaact_sd1_t); +fieldname = fieldname{1}; +icaact_sd1_t = icaact_sd1_t.(fieldname); + +marks_array2flags(icaact_sd1_t, ... + 'flag_dim','col', ... + 'init_method',sd_t_meth, ... + 'init_vals',str2num(sd_t_vals), ... + 'init_crit',str2num(sd_t_o), ... + 'flag_method','fixed', ... + 'flag_val',str2num(sd_t_p), ... + 'plot_figs','on', ... + 'title_prefix','SD ICA 1 - '); + +%ic_sd2 time +s='[sd_t_meth]'; +sd_t_meth=strtrim(batch_config(7).replace_string{find(strncmp(batch_config(7).replace_string,s,length(s)))}(length(s)+2:end)); +s='[sd_t_vals]'; +sd_t_vals=batch_config(7).replace_string{find(strncmp(batch_config(7).replace_string,s,length(s)))}(length(s)+2:end); +s='[sd_t_o]'; +sd_t_o=batch_config(7).replace_string{find(strncmp(batch_config(7).replace_string,s,length(s)))}(length(s)+2:end); +s='[sd_t_p]'; +sd_t_p=batch_config(7).replace_string{find(strncmp(batch_config(7).replace_string,s,length(s)))}(length(s)+2:end); + +if ~exist([EEG.filepath base_name '_icaact_sd2_t.mat']) + return; +end + +icaact_sd2_t = load([EEG.filepath base_name '_icaact_sd2_t.mat']); +fieldname = fieldnames(icaact_sd2_t); +fieldname = fieldname{1}; +icaact_sd2_t = icaact_sd2_t.(fieldname); + +marks_array2flags(icaact_sd2_t, ... + 'flag_dim','col', ... + 'init_method',sd_t_meth, ... + 'init_vals',str2num(sd_t_vals), ... + 'init_crit',str2num(sd_t_o), ... + 'flag_method','fixed', ... + 'flag_val',str2num(sd_t_p), ... + 'plot_figs','on', ... + 'title_prefix','SD ICA 2 - '); + + + diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/tools/diagnostics/init_diag_notes b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/tools/diagnostics/init_diag_notes new file mode 100644 index 000000000..8e8df31ed --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/tools/diagnostics/init_diag_notes @@ -0,0 +1,28 @@ +[x,y]=system('find . -maxdepth 3 -name "*.set"') +z=strsplit(y); +init_sd_param(z,'grp_diag'); + +load('grp_diag.mat'); + +figure;plot(grp_out_dist(1,:)) +hold on;plot(grp_out_dist(2,:),'g') +hold on;plot(grp_out_dist(3,:),'g') +hold on;plot(grp_out_dist(4,:),'r') +hold on;plot(grp_out_dist(5,:),'r') +%ml=ve_trimmean(grp_out_dist(4,:),20,2); +ml=median(grp_out_dist(4,:),2); +hold on;plot(ones(1,size(grp_out_dist,2))*ml,'k'); +%mu=ve_trimmean(grp_out_dist(5,:),20,2); +mu=median(grp_out_dist(5,:),2); +hold on;plot(ones(1,size(grp_out_dist,2))*mu,'k'); + +load('grp_diag_nch.mat'); + +set(gca,'xtick',nchans) +set(gca,'xgrid','on') + +for i=1:length(nchans); + th=text(nchans(i)-20,mu,z{i}); + set(th,'Rotation',90); +end + diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/tools/diagnostics/init_sd_param.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/tools/diagnostics/init_sd_param.m new file mode 100644 index 000000000..560088cbc --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/tools/diagnostics/init_sd_param.m @@ -0,0 +1,89 @@ +function grp_out_dist = init_sd_param(infnames,outfname) + +%% LOAD DATASET +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +grp_out_dist=[]; +for i=1:length(infnames); + infnames{i} + %Load the .set file (this will need to be BIDS data file format compliant) + EEG = pop_loadset('filename',infnames{i}); + EEG = eeg_checkset( EEG ); + + %% add channel coregistration procedure here. + %warp locations to standard head surface: + if ~isempty('[montage_info]'); + if isnumeric([-.14,-22,-48,-.07,0,-1.57,1080,1260,1240]) + EEG = warp_locs( EEG, ... + 'transform',[-.14,-22,-48,-.07,0,-1.57,1080,1260,1240], ... + 'manual','off'); + else + %if it is a BIDS channel tsv, load the tsv, + %else read the file that is assumed to be a transformation matrix. + end + end + + %% execute the event marks initiation script. + %if exist('[event_marks_script]'); + % run [event_marks_script] + %end + + EEG.marks = marks_init(size(EEG.data)); + % Apply trimmed average re-reference + chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); + trm_m = ve_trimmean(EEG.data(chan_inds,:),30,1); + trm_m_mat = repmat(trm_m,size(EEG.data,1),1); + EEG.data = EEG.data - trm_m_mat; + clear trm_m_mat; + + %% Window the continuous data + EEG = marks_continuous2epochs(EEG,'recurrence',[1],'limits',[0 1]); + + %% CALCULATE DATA SD + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % This flag calculates the standard deviation of the channels. Epochs are flagged if they + % are above the SD critical value. This flag identifies comically bad epochs. + + % Calculate standard deviation of activation on non-'manual' flagged channels and epochs... + chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); + epoch_inds = marks_label2index(EEG.marks.time_info,{'manual'},'indexes','invert','on'); + [EEG,data_sd_t]=chan_variance(EEG,'data_field','data', ... + 'chan_inds',chan_inds, ... + 'epoch_inds',epoch_inds, ... + 'plot_figs','off'); + + %% Create the window criteria vector for flagging ch_sd time_info... + if strcmp('q','na'); + flag_sd_t_inds=[]; + else + [~,~,out_dist]=marks_array2flags(data_sd_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',9, ... + 'flag_method','fixed', ... + 'flag_val',.2, ... + 'plot_figs','off'); + end + logging_log('INFO', 'CREATED EPOCH CRITERIA VECTOR...'); + + if isempty(grp_out_dist); + grp_out_dist=out_dist; + else + grp_out_dist=[grp_out_dist,out_dist]; + end + + %% save output + try OCTAVE_VERSION; + save('-mat7-binary',[outfname,'.mat'],'grp_out_dist'); + catch + save([outfname,'.mat'],'grp_out_dist'); + end + + nchans(i)=size(grp_out_dist,2); + try OCTAVE_VERSION; + save('-mat7-binary',[outfname,'_nch.mat'],'nchans'); + catch + save([outfname,'_nch.mat'],'nchans'); + end + +end \ No newline at end of file diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/tools/diagnostics/main_diagnostics.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/tools/diagnostics/main_diagnostics.m new file mode 100644 index 000000000..b212364db --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/tools/diagnostics/main_diagnostics.m @@ -0,0 +1,26 @@ +% Root diagnostics file. +% Argument should be a plaintext file listing the qcr's to be loaded +% newFolder=$(date +%Y_%m_%d_%H_%M_%S) +function main_diagnostics(filePath) + % Generate unique csv file + [~,fMod] = system('(date +%Y_%m_%d_%H_%M_%S)'); + fName = ['diag_',strtrim(fMod),'.csv']; + fOut = fopen(fName,'w'); + + % Update this eventually... + header = 'fname,nbchan,pnts,srate,manual,ch_s_sd_m,ch_s_sd_b_m,ch_sd_m,ch_sd_b_m,low_r_m,ic_sd1_m,ic_sd1_b_m,ic_sd2_m,mark_gap,ch_s_sd_b-ch_sd_b,ch_s_sd_b-ic_sd1_b,ch_sd_b-ic_sd1_b,low_r_m-ic_sd1_b,manual_only,non_manual,remaining,ch_s_sd,ch_sd,low_r,bridge,LtModelMean,LtModelStd,Lt_quant_0.05,Lt_quant_0.15,Lt_quant_0.25,Lt_quant_0.5,Lt_quant_0.75,Lt_quant_0.85,Lt_quant_0.95,manual_comp_1,ic_rt_1,linkpvaluesMean,linkpvaluesStd,lpv_quant_0.05,lpv_quant_0.15,lpv_quant_0.25,lpv_quant_0.5,lpv_quant_0.75,lpv_quant_0.85,lpv_quant_0.95,tvar_mean, tvar_std, tvar_quant_0.05,tvar_quant_0.15,tvar_quant_0.25,tvar_quant_0.5,tvar_quant_0.75,tvar_quant_0.85,tvar_quant_0.95, pvar_mean, pvar_std,pvar_quant_0.05,pvar_quant_0.15,pvar_quant_0.25,pvar_quant_0.5,pvar_quant_0.75,pvar_quant_0.85,pvar_quant_0.95'; + fprintf(fOut,'%s\n',header); + + % Read line by line of the specified diag list + fIn = fopen(filePath,'r'); + tline = fgetl(fIn); + while ischar(tline) + single_diagnostic(fOut,tline); + single_spect_diag(tline); + tline = fgetl(fIn); + end + + % Proper form! + fclose(fIn); + fclose(fOut); +end \ No newline at end of file diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/tools/diagnostics/rotate_ticks.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/tools/diagnostics/rotate_ticks.m new file mode 100644 index 000000000..fdceff494 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/tools/diagnostics/rotate_ticks.m @@ -0,0 +1,49 @@ +function[tl1,tl2]=rotatetl(h,rot,tb) + +%%%%%%%%%%%%%%%%%%%% Get current graph properties %%%%%%%%%%%%%%%%%%%%%%% +a=get(h,'XTickLabel'); +b=transpose(get(h,'XTick')); +c=get(h,'YLim'); +if strcmpi(tb,'t') || strcmpi(tb,'top'), + tb=2; +elseif strcmpi(tb,'b') || strcmpi(tb,'bottom'), + tb=1; +else + disp('The third argument is not recognized, try top or bottom'); +end + +%%%%%%%%%%%%%%%% Erase current tick labels from figure %%%%%%%%%%%%%%%%%% +set(h,'XTickLabel',[]); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Routine %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +if rot>=0 || rot<=360, +flag=1; + while flag + rot=mod(rot,360); + if rot>=0 && rot<=360, + flag=0; + end + end +end + +k=1; +while k<=size(a,1); + a1(k/2+0.5,:)=a(k,:); + b1(k/2+.5)=b(k); + if k+1<=size(a,2); + a2(k/2+.5,:)=a(:,k+1); + end + if k+1<=size(b,1); + b2(k/2+.5)=b(k+1); + end + k=k+2; +end + +c1=(c(1,tb)-(c(1,2)-c(1,1))*0.017).*ones(1,length(b1)); % upper ticks +c2=(c(1,tb)-(c(1,2)-c(1,1))*0.017).*ones(1,length(b2)); % lower ticks + +tl1=text(b1,c1,a1,'HorizontalAlignment','center','rotation',rot,'VerticalAlignment','cap'); +tl2=text(b2,c2,a2,'HorizontalAlignment','center','rotation',rot,'VerticalAlignment','cap'); + +end diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/tools/diagnostics/single_diagnostic.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/tools/diagnostics/single_diagnostic.m new file mode 100644 index 000000000..762f2839c --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/tools/diagnostics/single_diagnostic.m @@ -0,0 +1,114 @@ +% This function loads a single file and gathers all of the needed +% information. fOutID should be the output file handle from +% main_diagnostics. singleSetFile should reflect its name. +function single_diagnostic(fOutID, singleSetFile) + % Storing what's going to be written to the csv in a variable to act as + % a buffer. Line starts with filename. + outString = [singleSetFile, ',']; + + % Quantile breakdown used as a constant: + quantBreakdown = [0.05,0.15,0.25,0.5,0.75,0.85,0.95]; + + % Load: + EEG = pop_loadset('filepath','','filename',singleSetFile); + EEG = eeg_checkset( EEG ); + + % Basic row info: + outString = [outString, num2str(EEG.nbchan), ',']; + outString = [outString, num2str(EEG.pnts), ',']; + outString = [outString, num2str(EEG.srate), ',']; + + % All time_info flags in seconds: + % Manual + outString = [outString, num2str(length(find(EEG.marks.time_info(1).flags== 1)) / EEG.srate), ',']; + % ch_s_sd_m + outString = [outString, num2str(length(find(EEG.marks.time_info(1).flags == 1 & EEG.marks.time_info(3).flags == 1)) / EEG.srate), ',']; + % ch_s_sd_b_m (padding of ch_s_sd_m) + outString = [outString, num2str(length(find(EEG.marks.time_info(1).flags == 1 & EEG.marks.time_info(3).flags == 0.5)) / EEG.srate), ',']; + % ch_sd_m + outString = [outString, num2str(length(find(EEG.marks.time_info(1).flags == 1 & EEG.marks.time_info(4).flags == 1)) / EEG.srate), ',']; + % ch_sd_b_m (padding of ch_sd_m) + outString = [outString, num2str(length(find(EEG.marks.time_info(1).flags == 1 & EEG.marks.time_info(4).flags == 0.5)) / EEG.srate), ',']; + % low_r_m + outString = [outString, num2str(length(find(EEG.marks.time_info(1).flags == 1 & EEG.marks.time_info(5).flags == 1)) / EEG.srate), ',']; + % ic_sd1_m + outString = [outString, num2str(length(find(EEG.marks.time_info(1).flags == 1 & EEG.marks.time_info(8).flags == 1)) / EEG.srate), ',']; + % ic_sd1_b_m + outString = [outString, num2str(length(find(EEG.marks.time_info(1).flags == 1 & EEG.marks.time_info(8).flags == 0.5)) / EEG.srate), ',']; + % ic_sd2_m + outString = [outString, num2str(length(find(EEG.marks.time_info(1).flags == 1 & EEG.marks.time_info(12).flags == 1)) / EEG.srate), ',']; + % mark_gap + outString = [outString, num2str(length(find(EEG.marks.time_info(1).flags == 1 & EEG.marks.time_info(6).flags == 1)) / EEG.srate), ',']; + % overlap between ch_s_sd_b_m and ch_sd_b_m + outString = [outString, num2str(length(find(EEG.marks.time_info(1).flags == 1 & EEG.marks.time_info(3).flags == 0.5 & EEG.marks.time_info(4).flags == 0.5)) / EEG.srate), ',']; + % overlap between ch_s_sd_b_m and ic_sd1_b_m + outString = [outString, num2str(length(find(EEG.marks.time_info(1).flags == 1 & EEG.marks.time_info(3).flags == 0.5 & EEG.marks.time_info(8).flags == 0.5)) / EEG.srate), ',']; + % overlap between ch_sd_b_m and ic_sd1_b_m + outString = [outString, num2str(length(find(EEG.marks.time_info(1).flags == 1 & EEG.marks.time_info(4).flags == 0.5 & EEG.marks.time_info(8).flags == 0.5)) / EEG.srate), ',']; + % overlap between low_r_m and ic_sd1_b_m + outString = [outString, num2str(length(find(EEG.marks.time_info(1).flags == 1 & EEG.marks.time_info(5).flags == 1 & EEG.marks.time_info(8).flags == 0.5)) / EEG.srate), ',']; + % manual only + outString = [outString, num2str(length(find(EEG.marks.time_info(1).flags == 1 & EEG.marks.time_info(3).flags == 0 & EEG.marks.time_info(4).flags == 0 & ... + EEG.marks.time_info(5).flags == 0 & EEG.marks.time_info(6).flags == 0 & EEG.marks.time_info(8).flags == 0 & EEG.marks.time_info(12).flags == 0)) / EEG.srate), ',']; + % non-manual but flagged for other reason + outString = [outString, num2str(length(find(EEG.marks.time_info(1).flags == 0 & (EEG.marks.time_info(3).flags > 0 | EEG.marks.time_info(4).flags > 0 | EEG.marks.time_info(5).flags > 0 | EEG.marks.time_info(6).flags > 0 | EEG.marks.time_info(8).flags > 0 | EEG.marks.time_info(12).flags > 0))) / EEG.srate), ',']; + % all non-manual + outString = [outString, num2str(length(find(EEG.marks.time_info(1).flags == 0)) / EEG.srate), ',']; + + % Single chan_info flag enables in seconds: + for i=2:5 % [2,5] are ch_s_sd, ch_sd, low_r, and bridge respectively. + outString = [outString, num2str(length(find(EEG.marks.chan_info(i).flags==1))), ',']; + end + + % Amica diagnostic info: + outString = [outString, num2str(mean(EEG.amica(2).models.Lt)), ',']; + outString = [outString, num2str(std(EEG.amica(2).models.Lt)), ',']; + quants = quantile(EEG.amica(2).models.Lt,quantBreakdown); + for i=1:length(quants) + outString = [outString, num2str(quants(i)), ',']; + end + % Manual QC comp count: + outString = [outString, num2str(length(find(EEG.marks.comp_info(1).flags == 1))), ',']; + % Components marked as ic_rt + outString = [outString, num2str(length(find(EEG.marks.comp_info(2).flags == 1))), ',']; + + % ISCtest: + a = []; + for i=2:length(EEG.amica); + a(:,:,i-1)=EEG.amica(i).models(1).A; + end + [~,~,linkpvalues,~] = isctest(a,0.05,0.05,'mixing'); + outString = [outString, num2str(mean(linkpvalues)), ',']; + outString = [outString, num2str(std(linkpvalues)), ',']; + quants = quantile(linkpvalues,quantBreakdown); + for i=1:length(quants) + outString = [outString, num2str(quants(i)), ',']; + end + + % Varianc measures: + % Total data: + EEG = pop_marks_select_data(EEG,'channel marks',[],'labels',{'manual'},'remove','on'); + EEG = pop_marks_select_data(EEG,'time marks',[],'labels',{'manual'},'remove','on'); + tvar=std(EEG.data,[],1); + outString = [outString, num2str(mean(tvar)), ',']; + outString = [outString, num2str(std(tvar)), ',']; + quants = quantile(tvar,quantBreakdown); + for i=1:length(quants) + outString = [outString, num2str(quants(i)), ',']; + end + + % Projected data: + EEG = pop_marks_select_data(EEG,'component marks',[],'labels',{'manual'},'remove','on'); + pvar=std(EEG.data,[],1); + outString = [outString, num2str(mean(pvar)), ',']; + outString = [outString, num2str(std(pvar)), ',']; + quants = quantile(pvar,quantBreakdown); + for i=1:length(quants) + outString = [outString, num2str(quants(i)), ',']; + end + + % disp('hold'); + + % Final write - includes newline + fprintf(fOutID,'%s\n',outString); +end \ No newline at end of file diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/tools/diagnostics/single_spect_diag.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/tools/diagnostics/single_spect_diag.m new file mode 100644 index 000000000..694002f7c --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/tools/diagnostics/single_spect_diag.m @@ -0,0 +1,99 @@ +% This function loads a single file and gathers all of the needed +% information. fOutID should be the output file handle from +% main_diagnostics. singleSetFile should reflect its name. +function single_spect_diag(singleSetFile) + % Storing what's going to be written to the csv in a variable to act as + + % Load: + EEG = pop_loadset('filepath','','filename',singleSetFile); + EEG = eeg_checkset( EEG ); + + % Removed flagged channels and time segments + sprintf('%s','Purging flagged channels...\n'); + EEG = pop_marks_select_data(EEG,'channel marks',[],'labels',{'manual'},'remove','on'); + EEG = pop_marks_select_data(EEG,'time marks',[],'labels',{'manual'},'remove','on'); +% EEG = pop_marks_select_data(EEG,'component marks',[],'labels',{'manual'},'remove','on'); + EEG = eeg_checkset(EEG); + + EEG= warp_locs(EEG,'derivatives/lossless/code/misc/standard_1020_Fpz_FT7_FT8_Cz_PO7_PO8_Oz.elc','transform',[0,0,0,0,0,-1.57,1,1,1],'manual','off'); + EEG = interp_mont(EEG,'derivatives/lossless/code/misc/standard_1020_Fpz_FT7_FT8_Cz_PO7_PO8_Oz.elc','manual','off'); + EEG = eeg_checkset(EEG); + + EEG.icaact=[]; + EEG.icawinv=[]; + EEG.icasphere=[]; + EEG.icaweights=[]; + EEG.icachansind=[]; + + % Window the continuous data + logging_log('INFO', 'Windowing the continous data...'); + EEG = marks_continuous2epochs(EEG,'recurrence',0.5,'limits',[0,1],'keepboundary','off'); + EEG = eeg_checkset(EEG); + + if EEG.srate==500; + EEG.data=EEG.data(:,1:2:end,:); + %EEG.srate=250; + end + %EEG = eeg_checkset(EEG); + + h=hanning(250); + hr=repmat(h,1,EEG.trials); + + for i=1:size(EEG.data,1); + d=squeeze(EEG.data(i,:,:)); + dh=d.*hr; + f=fft(dh); + out.tfm(:,i)=mean(abs(f(1:125,:)),2); + end + + + + % Load: + EEG = pop_loadset('filepath','','filename',singleSetFile); + EEG = eeg_checkset( EEG ); + + % Removed flagged channels and time segments + sprintf('%s','Purging flagged channels...\n'); + EEG = pop_marks_select_data(EEG,'channel marks',[],'labels',{'manual'},'remove','on'); + EEG = pop_marks_select_data(EEG,'time marks',[],'labels',{'manual'},'remove','on'); + EEG = pop_marks_select_data(EEG,'component marks',[],'labels',{'manual'},'remove','on'); + EEG = eeg_checkset(EEG); + + EEG= warp_locs(EEG,'derivatives/lossless/code/misc/standard_1020_Fpz_FT7_FT8_Cz_PO7_PO8_Oz.elc','transform',[0,0,0,0,0,-1.57,1,1,1],'manual','off'); + EEG = interp_mont(EEG,'derivatives/lossless/code/misc/standard_1020_Fpz_FT7_FT8_Cz_PO7_PO8_Oz.elc','manual','off'); + EEG = eeg_checkset(EEG); + + EEG.icaact=[]; + EEG.icawinv=[]; + EEG.icasphere=[]; + EEG.icaweights=[]; + EEG.icachansind=[]; + + % Window the continuous data + logging_log('INFO', 'Windowing the continous data...'); + EEG = marks_continuous2epochs(EEG,'recurrence',0.5,'limits',[0,1],'keepboundary','off'); + EEG = eeg_checkset(EEG); + + if EEG.srate==500; + EEG.data=EEG.data(:,1:2:end,:); + %EEG.srate=250; + end + %EEG = eeg_checkset(EEG); + + h=hanning(250); + hr=repmat(h,1,EEG.trials); + + for i=1:size(EEG.data,1); + d=squeeze(EEG.data(i,:,:)); + dh=d.*hr; + f=fft(dh); + out.pfm(:,i)=mean(abs(f(1:125,:)),2); + end + + + [path, name, ~]= fileparts(singleSetFile); + outfname = [path,'/',name, '_spect_diag.mat']; + disp(['Saving ' outfname '...']); + save(outfname,'out'); +end + diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/tools/diagnostics/strsplit.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/tools/diagnostics/strsplit.m new file mode 100644 index 000000000..f452b8ed6 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/tools/diagnostics/strsplit.m @@ -0,0 +1,109 @@ +function terms = strsplit(s, delimiter) +%STRSPLIT Splits a string into multiple terms +% +% terms = strsplit(s) +% splits the string s into multiple terms that are separated by +% white spaces (white spaces also include tab and newline). +% +% The extracted terms are returned in form of a cell array of +% strings. +% +% terms = strsplit(s, delimiter) +% splits the string s into multiple terms that are separated by +% the specified delimiter. +% +% Remarks +% ------- +% - Note that the spaces surrounding the delimiter are considered +% part of the delimiter, and thus removed from the extracted +% terms. +% +% - If there are two consecutive non-whitespace delimiters, it is +% regarded that there is an empty-string term between them. +% +% Examples +% -------- +% % extract the words delimited by white spaces +% ts = strsplit('I am using MATLAB'); +% ts <- {'I', 'am', 'using', 'MATLAB'} +% +% % split operands delimited by '+' +% ts = strsplit('1+2+3+4', '+'); +% ts <- {'1', '2', '3', '4'} +% +% % It still works if there are spaces surrounding the delimiter +% ts = strsplit('1 + 2 + 3 + 4', '+'); +% ts <- {'1', '2', '3', '4'} +% +% % Consecutive delimiters results in empty terms +% ts = strsplit('C,Java, C++ ,, Python, MATLAB', ','); +% ts <- {'C', 'Java', 'C++', '', 'Python', 'MATLAB'} +% +% % When no delimiter is presented, the entire string is considered +% % as a single term +% ts = strsplit('YouAndMe'); +% ts <- {'YouAndMe'} +% + +% History +% ------- +% - Created by Dahua Lin, on Oct 9, 2008 +% + +%% parse and verify input arguments + +assert(ischar(s) && ndims(s) == 2 && size(s,1) <= 1, ... + 'strsplit:invalidarg', ... + 'The first input argument should be a char string.'); + +if nargin < 2 + by_space = true; +else + d = delimiter; + assert(ischar(d) && ndims(d) == 2 && size(d,1) == 1 && ~isempty(d), ... + 'strsplit:invalidarg', ... + 'The delimiter should be a non-empty char string.'); + + d = strtrim(d); + by_space = isempty(d); +end + +%% main + +s = strtrim(s); + +if by_space + w = isspace(s); + if any(w) + % decide the positions of terms + dw = diff(w); + sp = [1, find(dw == -1) + 1]; % start positions of terms + ep = [find(dw == 1), length(s)]; % end positions of terms + + % extract the terms + nt = numel(sp); + terms = cell(1, nt); + for i = 1 : nt + terms{i} = s(sp(i):ep(i)); + end + else + terms = {s}; + end + +else + p = strfind(s, d); + if ~isempty(p) + % extract the terms + nt = numel(p) + 1; + terms = cell(1, nt); + sp = 1; + dl = length(delimiter); + for i = 1 : nt-1 + terms{i} = strtrim(s(sp:p(i)-1)); + sp = p(i) + dl; + end + terms{nt} = strtrim(s(sp:end)); + else + terms = {s}; + end +end diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/tools/diagnostics/study_diagnostics.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/tools/diagnostics/study_diagnostics.m new file mode 100644 index 000000000..b81edf345 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/tools/diagnostics/study_diagnostics.m @@ -0,0 +1,398 @@ +function study_diagnostics(ALLEEG,batch_config) +% diagnostics(ALLEEG,type) - Creates figures to aid user in understanding +% the generated marks, and the overal quality of the data. +% +%Copyright (C) 2017 BUCANL +% +%Code originally written by James A. Desjardins with contributions from +%Allan Campopiano and Andrew Lofts, supported by NSERC funding to +%Sidney J. Segalowitz at the Jack and Nora Walker Canadian Centre for +%Lifespan Development Research (Brock University), and a Dedicated Programming +%award from SHARCNet, Compute Ontario. +% +%This program is free software; you can redistribute it and/or modify +%it under the terms of the GNU General Public License as published by +%the Free Software Foundation; either version 2 of the License, or +%(at your option) any later version. +% +%This program is distributed in the hope that it will be useful, +%but WITHOUT ANY WARRANTY; without even the implied warranty of +%MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +%GNU General Public License for more details. +% +%You should have received a copy of the GNU General Public License +%along with this program (LICENSE.txt file in the root directory); if not, +%write to the Free Software Foundation, Inc., 59 Temple Place, +%Suite 330, Boston, MA 02111-1307 USA + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%%%%% Beginning of Calculating Statistics +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +data_chans = []; +data_time = []; +ica_chans = []; +sub_names = []; + +sub_dir = dir('derivatives/lossless/sub-*'); +for i=1:length(sub_dir); + set_name = dir(['derivatives/lossless/' sub_dir(i).name '/eeg/*set']); + if ~isempty(set_name); + set_name = ['derivatives/lossless/' sub_dir(i).name '/eeg/' set_name.name]; + EEG = pop_loadset('filename',set_name,'loadmode','info'); + else + EEG =[]; + end + if ~isempty(EEG); + ALLEEG = horzcat(ALLEEG,EEG); + end +end + +%dat_ch_sd_all = ones(size(ALLEEG,1),longest_recording,ALLEEG(i).nbchan); % assuming all recordings have same number of channels +%dat_sd_t_all = ones(size(ALLEEG,1),longest_recording,ALLEEG(i).nbchan); +%icaact_sd1_t_all = ones(length(ALLEEG(1)),longest_recording,largest_ica_count); +%icaact_sd2_t_all = ones(length(ALLEEG(1)),longest_recording,largest_ica_count); + + +for i=1:length(ALLEEG); + + EEG = ALLEEG(i); + fprintf(['\nChecking marks for file ' EEG.filename '...\n']); + + fin_underscore = regexp(EEG.filename,'_'); + fin_underscore = fin_underscore(end); + base_name = EEG.filename(1:fin_underscore-1); + sub_names = horzcat(sub_names,base_name(5:end)); + + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + %% Channels + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + fprintf('\nChannels\n'); + fprintf('==================================================\n'); + for j = 1:length(EEG.marks.chan_info) + %just flags + x = length(find(EEG.marks.chan_info(j).flags)); + % All chans + y = length(EEG.marks.chan_info(j).flags); + disp(['Flagged: ' num2str(x) '/' num2str(y) ' Channels for ' EEG.marks.chan_info(j).label]); + end + + %%%%%%%%%%%%%%%%%% + % Pie Chart Info % + %%%%%%%%%%%%%%%%%% + + ca = length(EEG.marks.chan_info(1).flags); % All time + c2 = length(find(EEG.marks.chan_info(2).flags)); % 2 + c3 = length(find(EEG.marks.chan_info(3).flags)); % 3 + c4 = length(find(EEG.marks.chan_info(4).flags)); % 4 + cm = length(find(EEG.marks.chan_info(1).flags)); % 5 + ca = ca-cm; % Remaining All time + cpie = [ca c2 c3 c4]; + + data_chans = vertcat(data_chans,cpie); + + %%%%%%%%%%%%%%%%%%%%% + % Scatter Plot Info % + %%%%%%%%%%%%%%%%%%%%% + s='[sd_ch_meth]'; + sd_ch_meth=strtrim(batch_config(1).replace_string{find(strncmp(batch_config(1).replace_string,s,length(s)))}(length(s)+2:end)); + s='[sd_ch_vals]'; + sd_ch_vals=batch_config(1).replace_string{find(strncmp(batch_config(1).replace_string,s,length(s)))}(length(s)+2:end); + s='[sd_ch_o]'; + sd_ch_o=batch_config(1).replace_string{find(strncmp(batch_config(1).replace_string,s,length(s)))}(length(s)+2:end); + s='[sd_ch_p]'; + sd_ch_p=batch_config(1).replace_string{find(strncmp(batch_config(1).replace_string,s,length(s)))}(length(s)+2:end); + + %data_sd_ch = load([EEG.filepath '/' base_name '_data_sd_ch.mat']); + %fieldname = fieldnames(data_sd_ch); + %fieldname = fieldname{1}; + %data_sd_ch = data_sd_ch.(fieldname); + %dat_ch_sd_all(:,:,i) = data_sd_ch; + + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + %% Time + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + fprintf('\nTime\n'); + fprintf('==================================================\n'); + for j = 1:length(EEG.marks.time_info) + %just flags + x = length(find(EEG.marks.time_info(j).flags)); + % All time + y = length(EEG.marks.time_info(j).flags); + disp(['Flagged: ' num2str((x/y)*100) ' % of Time for ' EEG.marks.time_info(j).label]); + end + + %%%%%%%%%%%%%%%%%% + % Pie Chart Info % + %%%%%%%%%%%%%%%%%% + + % outtask vs manual vs time remaining piechart + ca = length(EEG.marks.time_info(1).flags); % All time + + co=length(marks_label2index(EEG.marks.time_info, ... + marks_match_label({'out_task','in_leadup'},{EEG.marks.time_info.label}), ... + 'indices')); + + mgap=length(marks_label2index(EEG.marks.time_info, ... + marks_match_label('mark_gap',{EEG.marks.time_info.label}), ... + 'indices')); + + chsd=length(marks_label2index(EEG.marks.time_info, ... + marks_match_label('ch_sd',{EEG.marks.time_info.label}), ... + 'indices')); + + lowr=length(marks_label2index(EEG.marks.time_info, ... + marks_match_label('low_r',{EEG.marks.time_info.label}), ... + 'indices')); + + icsd1=length(marks_label2index(EEG.marks.time_info, ... + marks_match_label('ic_sd1',{EEG.marks.time_info.label}), ... + 'indices')); + + icsd2=length(marks_label2index(EEG.marks.time_info, ... + marks_match_label('ic_sd2',{EEG.marks.time_info.label}), ... + 'indices')); + + cm=length(marks_label2index(EEG.marks.time_info, ... + marks_match_label('manual',{EEG.marks.time_info.label}), ... + 'indices')); + + cr = ca-cm; % Remaining All time + cpie = [cr co mgap chsd lowr icsd1 icsd2]; + + data_time = vertcat(data_time,cpie); + + + %%%%%%%%%%%%%%%%%%%%% + % Scatter Plot Info % + %%%%%%%%%%%%%%%%%%%%% + s='[sd_t_meth]'; + sd_t_meth=strtrim(batch_config(1).replace_string{find(strncmp(batch_config(1).replace_string,s,length(s)))}(length(s)+2:end)); + s='[sd_t_vals]'; + sd_t_vals=batch_config(1).replace_string{find(strncmp(batch_config(1).replace_string,s,length(s)))}(length(s)+2:end); + s='[sd_t_o]'; + sd_t_o=batch_config(1).replace_string{find(strncmp(batch_config(1).replace_string,s,length(s)))}(length(s)+2:end); + s='[sd_t_p]'; + sd_t_p=batch_config(1).replace_string{find(strncmp(batch_config(1).replace_string,s,length(s)))}(length(s)+2:end); + + %data_sd_t = load([EEG.filepath '/' base_name '_data_sd_t.mat']); + %fieldname = fieldnames(data_sd_t); + %fieldname = fieldname{1}; + %data_sd_t = data_sd_t.(fieldname); + %dat_sd_t_all(:,:,i) = data_sd_t; + + + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + %% Components + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + fprintf('\nICA Components\n'); + fprintf('==================================================\n'); + disp(['Identified ' num2str(min(size(EEG.icaweights))) ' components from ' num2str(length(EEG.icachansind)) ' channels']); + + %%%%%%%%%%%%%%%%%% + % Pie Chart Info % + %%%%%%%%%%%%%%%%%% + + ca = length(find(EEG.reject.classtype == 1)); + c2 = length(find(EEG.reject.classtype == 2)); + c3 = length(find(EEG.reject.classtype == 3)); + c4 = length(find(EEG.reject.classtype == 4)); + c5 = length(find(EEG.reject.classtype == 5)); + c6 = length(find(EEG.reject.classtype == 6)); + cpie = [ca c2 c3 c4 c5 c6]; + + ica_chans = vertcat(ica_chans,cpie); + + %%%%%%%%%%%%%%%%%%%%% + % Scatter Plot Info % + %%%%%%%%%%%%%%%%%%%%% + + %ic_sd1 time + %icaact_sd1_t = load([EEG.filepath '/' base_name '_icaact_sd1_t.mat']); + %fieldname = fieldnames(icaact_sd1_t); + %fieldname = fieldname{1}; + %icaact_sd1_t = icaact_sd1_t.(fieldname); + %icaact_sd1_t_all(:,:,i) = icaact_sd1_t; + + s='[sd_t_meth]'; + sd_t_meth=strtrim(batch_config(3).replace_string{find(strncmp(batch_config(3).replace_string,s,length(s)))}(length(s)+2:end)); + s='[sd_t_vals]'; + sd_t_vals=batch_config(3).replace_string{find(strncmp(batch_config(3).replace_string,s,length(s)))}(length(s)+2:end); + s='[sd_t_o]'; + sd_t_o=batch_config(3).replace_string{find(strncmp(batch_config(3).replace_string,s,length(s)))}(length(s)+2:end); + s='[sd_t_p]'; + sd_t_p=batch_config(3).replace_string{find(strncmp(batch_config(3).replace_string,s,length(s)))}(length(s)+2:end); + + %ic_sd2 time + if ~exist([EEG.filepath base_name '_icaact_sd2_t.mat']) + continue + end + %icaact_sd2_t = load([EEG.filepath '/' base_name '_icaact_sd2_t.mat']); + %fieldname = fieldnames(icaact_sd2_t); + %fieldname = fieldname{1}; + %icaact_sd2_t = icaact_sd2_t.(fieldname); + %icaact_sd2_t_all(:,:,i) = icaact_sd2_t; + + s='[sd_t_meth]'; + sd_t_meth=strtrim(batch_config(7).replace_string{find(strncmp(batch_config(7).replace_string,s,length(s)))}(length(s)+2:end)); + s='[sd_t_vals]'; + sd_t_vals=batch_config(7).replace_string{find(strncmp(batch_config(7).replace_string,s,length(s)))}(length(s)+2:end); + s='[sd_t_o]'; + sd_t_o=batch_config(7).replace_string{find(strncmp(batch_config(7).replace_string,s,length(s)))}(length(s)+2:end); + s= '[sd_t_p]'; + s_t_p=batch_config(7).replace_string{find(strncmp(batch_config(7).replace_string,s,length(s)))}(length(s)+2:end); + +end %% End of Calculating Statistics +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%%%%% Beginning of Plots +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + +%% Mean and Sum Channel Breakdown +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%%%%%%%%%%%% +% Pie Chart % +%%%%%%%%%%%%% + +data_chan_m = mean(data_chans); +figure; +explode = [1 0 0 0]; +labels = {'Remaining Channels','ch-sd','low-r','bridge'}; +pie(data_chan_m,explode,labels); colormap(jet); +title('Data Channel Classification'); + +%%%%%%%%%%%%% +% Histogram % +%%%%%%%%%%%%% + +figure; bar(data_chans,'stacked'); +title('Data Channel Classification'); +legend(labels); +set(gca,'XTick',[1:length(sub_names)]); +set(gca,'XTickLabel',sub_names); +rotatetl(gca,90,'b'); +xlabel('Subject #'); +ylabel('# of Channels'); +x_pos = get(get(gca, 'XLabel'), 'Position'); +set(get(gca, 'XLabel'), 'Position', x_pos + [0 -3 0]); + +%%%%%%%%%%%%%%%% +% Scatter Plot % +%%%%%%%%%%%%%%%% + +marks_array2flags(data_chans, ... + 'flag_dim','row', ... + 'init_method',sd_t_meth, ... + 'init_vals',str2num(sd_t_vals), ... + 'init_crit',str2num(sd_t_o), ... + 'flag_method','fixed', ... + 'flag_val',str2num(sd_t_p), ... + 'plot_figs','on', ... + 'title_prefix','SD time - '); + + +%% Mean and Sum Time Breakdown +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +mean_time = mean(data_time); + +%%%%%%%%%%%%% +% Pie Chart % +%%%%%%%%%%%%% + +figure; +explode = [1 0 0 0 0 0 0]; +labels = {'Remaining Time','out-task','mark-gap','ch-sd','low-r','ic-sd1','ic-sd2'}; +pie(mean_time,explode,labels); colormap(jet); +title('Mean Time Classification Breakdown across all Subjects'); + +%%%%%%%%%%%%% +% Histogram % +%%%%%%%%%%%%% + +figure; bar(data_time,'stacked'); +title('Mean Time Classification Breakdown for each Subject'); +legend(labels); +set(gca,'XTick',[1:length(sub_names)]); +set(gca,'XTickLabel',sub_names); +rotatetl(gca,90,'b'); +x_pos = get(get(gca, 'XLabel'), 'Position'); +set(get(gca, 'XLabel'), 'Position', x_pos + [0 -3 0]); +xlabel('Subject #'); +ylabel('# of Time Points'); +y_lab = get(gca,'YTick'); +set(gca,'YTickLabel',num2str(y_lab')); + +%%%%%%%%%%%%%%%% +% Scatter Plot % +%%%%%%%%%%%%%%%% + +marks_array2flags(data_time, ... + 'flag_dim','col', ... + 'init_method',sd_t_meth, ... + 'init_vals',str2num(sd_t_vals), ... + 'init_crit',str2num(sd_t_o), ... + 'flag_method','fixed', ... + 'flag_val',str2num(sd_t_p), ... + 'plot_figs','on', ... + 'title_prefix','SD time - '); + + +%% Mean Component Breakdown +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +ica_chan_m = mean(ica_chans); + +%%%%%%%%%%%%% +% Pie Chart % +%%%%%%%%%%%%% + +figure; +explode = [0 1 0 0 0 0]; +labels = {'blink','neural','heart','lateye','muscle','mixed'}; +pie(ica_chan_m,explode,labels); colormap(jet); +title('Component Classification Breakdown across all Subjects'); + +%%%%%%%%%%%%% +% Histogram % +%%%%%%%%%%%%% + +figure; bar(ica_chans,'stacked'); +title('Component Classification Breakdown for each Subject'); +legend(labels); +set(gca,'XTick',[1:length(sub_names)]); +set(gca,'XTickLabel',sub_names); +rotatetl(gca,90,'b'); +xlabel('Subject #'); +ylabel('# of Components'); +x_pos = get(get(gca, 'XLabel'), 'Position'); +set(get(gca, 'XLabel'), 'Position', x_pos + [0 -3 0]); + +%%%%%%%%%%%%%%%% +% Scatter Plot % +%%%%%%%%%%%%%%%% + +marks_array2flags(ica_chans, ... + 'flag_dim','col', ... + 'init_method',sd_t_meth, ... + 'init_vals',str2num(sd_t_vals), ... + 'init_crit',str2num(sd_t_o), ... + 'flag_method','fixed', ... + 'flag_val',str2num(sd_t_p), ... + 'plot_figs','on', ... + 'title_prefix','SD ICA 1 - '); + +%marks_array2flags(icaact_sd2_t_all, ... +% 'flag_dim','col', ... +% 'init_method',sd_t_meth, ... +% 'init_vals',str2num(sd_t_vals), ... +% 'init_crit',str2num(sd_t_o), ... +% 'flag_method','fixed', ... +% 'flag_val',str2num(sd_t_p), ... +% 'plot_figs','on', ... +% 'title_prefix','SD ICA 2 - '); + diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/tools/print_chan_sample.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/tools/print_chan_sample.m new file mode 100644 index 000000000..484b67f9c --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/tools/print_chan_sample.m @@ -0,0 +1,42 @@ +% print_chan_sample() - prints the number of channems and samples +% in the EEG file to the log file +% +% Usage: +% >> print_chan_sample(EEG_temp); +% +% Inputs: +% EEG_temp - input EEG dataset +% +% Outputs: +% None +%: + +% Copyright (C) 2017 Brock University Cognitive and Affective Neuroscience Lab +% +% Code written by Mike Cichonski +% +% This program is free software; you can redistribute it and/or modify +% it under the terms of the GNU General Public License as published by +% the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program (LICENSE.txt file in the root directory); if not, +% write to the Free Software Foundation, Inc., 59 Temple Place, +% Suite 330, Boston, MA 02111-1307 USA +function print_chan_sample(EEG_temp) + numChans = 0; + numSamps = 0; + if ~isempty(EEG_temp) + numChans = EEG_temp.nbchan; + numSamps = EEG_temp.pnts; + end + logging_log('INFO',sprintf('Number of Channels: %d', numChans)); + logging_log('INFO',sprintf('Number of Samples: %d', numSamps)); +end + diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/code/tools/wa_startsWith.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/tools/wa_startsWith.m new file mode 100644 index 000000000..f340addd3 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/code/tools/wa_startsWith.m @@ -0,0 +1,8 @@ +function retval = startsWith(s, prefix) + n = length(prefix); + if n == 0 # Empty prefix + retval = 1; # Every string starts with empty prefix + return + endif + retval = strncmp(s, prefix, n); +end diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/README.md b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/README.md new file mode 100644 index 000000000..79107c71e --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/README.md @@ -0,0 +1,20 @@ +# Face13 Segmentation +Script for segmentation of BIDS-Lossless-EEG derivative of Face13 dataset. + +This extension is currently in development. + +## Face and house, both upright and inverted +Copy this 'BIDS-Seg-Face13-EEGLAB' folder to the 'Face13/derivatives/BIDS-Lossless-EEG/derivatives' folder. + +Use EEGLAB with Matlab working directory set to: +Face13 + +Add needed structures and files to the path: +addpath derivatives/BIDS-Lossless-EEG/code/install +lossless_path +eeglab + +Start EEGLAB: +eeglab + +To run the segmentation use the batch_context EEGLAB extension to execute the "seg_face_house_up_inv.htb" script (located in 'derivatives/BIDS-Seg-Face13-EEGLAB/code/scripts') on the *.edf files in the BIDS-Lossless-EEG derivatives directory. diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/code/scripts/loadfnames.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/code/scripts/loadfnames.m new file mode 100644 index 000000000..d514e2ecb --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/code/scripts/loadfnames.m @@ -0,0 +1,29 @@ +%This function loads EEGLAB *.set files into the ALLEEG structure. This is +%useful for loading segmented files that are ready to create a STUDY +%structure. The fnamefile input is a text file containing the filenames +%with paths to each of the *.set files to be loaded (one file per line). +%From a bash terminal these file name text files can be created as follows: +%for example... +%'find . -type f -name "*-SEG*.set" > derivatives/seg_face_noise/code/misc/fnames.txt + +function [EEG,ALLEEG,CURRENTSET]=loadfnames(EEG,fnamefile) + +fid=fopen(fnamefile,'r'); +fnd=fread(fid); +fclose(fid); +cr_ind=find(fnd==10); +for i=1:length(cr_ind); + if i==1; + c_fname{i}=deblank(char(fnd(3:cr_ind(i))')); + %EEG=eeg_checkset(EEG); + %ALLEEG(i)=EEG; + else + c_fname{i}=deblank(char(fnd(cr_ind(i-1)+3:cr_ind(i))')); + %EEG=eeg_checkset(EEG); + %ALLEEG(i)=EEG; + end +end +ALLEEG = pop_loadset('filename',c_fname); +CURRENTSET=i; +EEG=eeg_checkset(EEG); +eeglab redraw diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/code/scripts/seg_face_house_up_inv.htb b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/code/scripts/seg_face_house_up_inv.htb new file mode 100644 index 000000000..e84d0e409 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/code/scripts/seg_face_house_up_inv.htb @@ -0,0 +1,120 @@ +%LOAD BIDS FILES +sphereLoc = '[batch_dfp]/[batch_dfn,_,-1]_icasphere.tsv' +weightsLoc = '[batch_dfp]/[batch_dfn,_,-1]_icaweights.tsv' +annoFile = '[batch_dfp]/[batch_dfn,_,-1]_desc-qc_annotations.tsv' + +elecFile = './[batch_dfn,_,-1]_electrodes.tsv' +eventsFile = './[batch_dfn,_,-1]_events.tsv' + +EEG = pop_bidsload('[batch_dfp]/[batch_dfn]','elecLoc',elecFile,'eventLoc',eventsFile,'icaSphere',sphereLoc,'icaWeights',weightsLoc,'annoLoc',annoFile); + +% add STUDY variables +% legacy mode for finding subject number. +myStr = '[batch_dfn]'; +indexStart = regexp(myStr,'\-(.*?)\/'); +EEG.subject = str2num(myStr(indexStart+1:indexStart+3)); +%session +EEG.session = 1; + +% Removed flagged time segments and Independent Components +EEG = pop_marks_select_data(EEG,'channel marks',[],'labels',{'manual', 'rank'},'remove','on'); +EEG = pop_marks_select_data(EEG,'time marks',[],'labels',{'manual'},'remove','on'); +EEG = pop_marks_select_data(EEG,'component marks',[],'labels',{'manual'},'remove','on'); +EEG = eeg_checkset(EEG); + +%average rereference. +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual','rank'},'indexes','invert','on'); +EEG = warp_locs( EEG,'[batch_dfp]/code/misc/standard_1020_ll_ref19.elc','transform',[0,0,0,0,0,-1.57,1,1,1],'manual','off'); +EEG = interp_ref(EEG,'[batch_dfp]/code/misc/standard_1020_ll_ref19.elc','chans',chan_inds); +EEG.chaninfo.nosedir='+Y'; +EEG = eeg_checkset(EEG); + +% Interpolate flagged channels +%manual_mark_ind=find(strcmp('manual',{EEG.marks.chan_info.label})); +%rank_mark_ind=find(strcmp('rank',{EEG.marks.chan_info.label})); +%flags=EEG.marks.chan_info(manual_mark_ind).flags+EEG.marks.chan_info(rank_mark_ind).flags; +%chan_inds=find(flags); +%EEG = pop_interp(EEG,chan_inds,'spherical'); + +%purge unnecessary fields... +for i=1:length(EEG.marks.time_info); + EEG.marks.time_info(i).flags=[]; +end +EEG.data_sd_ep=[]; +EEG.c_data_sd_ep=[]; +EEG.data_sd_ch=[]; +EEG.c_data_sd_ch=[]; +EEG.m_neigbr_r_ch=[]; +EEG.c_neigbr_r_ch=[]; +EEG.m_neigbr_r_lat=[]; +EEG.c_neigbr_r_lat=[]; +EEG.amica=[]; +EEG.icaact_sd1_lat=[]; +EEG.c_icaact1_sd_lat=[]; +EEG.icaact_sd2_lat=[]; +EEG.c_icaact_sd2_lat=[]; +EEG.icaact_ta_lat=[]; +EEG.c_icaact_ta_lat=[]; +EEG.icaact_b_lat=[]; +EEG.c_icaact_b_lat=[]; +% EEG.icaact = []; +% EEG.icawinv = []; +% EEG.icasphere = []; +% EEG.icaweights = []; +% EEG.icachansind = []; + +%lowpass filter +%EEG = pop_eegfiltnew(EEG,[], 1,[],1,[],0); +EEG = pop_eegfiltnew(EEG,[],30,[],0,[],0); + +% ICA information must be valid across all segmented files +%icaFileName = '[batch_dfp]/derivatives/BIDS-Seg-Face13-EEGLAB/[/,1,batch_dfn,_,-1]_icaweights.tsv'; +%[outpath outname outext] = fileparts(icaFileName); +%if ~exist(outpath,'dir') +% mkdir(outpath); +%end +%dlmwrite(icaFileName,EEG.icaweights,'\t'); +%dlmwrite(strrep(icaFileName,'_icaweights.tsv','_icasphere.tsv'),EEG.icasphere,'\t'); +%s = {}; +%s.algorithm = 'amica15'; +%s.icachansind = EEG.icachansind; +%s.intendedFor = '[batch_dfp]/derivatives/BIDS-Seg-Face13-EEGLAB/[/,1,batch_dfn,_,-1]'; +%savejson('',s,strrep(icaFileName,'_icaweights.tsv','_icaweights.json')); + +tmpEEG=EEG; + +% face-upright +outfname='[batch_dfp]/derivatives/BIDS-Seg-Face13-EEGLAB/[/,1,batch_dfn,_,-1]_desc-SEGfaceu_eeg.edf' +[outpath outname outext] = fileparts(outfname); +if ~exist(outpath,'dir') + mkdir(outpath); +end +try + EEG = pop_epoch( tmpEEG, { 'face-upright' }, ... + [-2 3], 'newname', 'direct', 'epochinfo', 'yes'); + EEG = pop_rmbase( EEG, [-1000 0]); + EEG.condition = 'faceu'; + EEG = pop_saveset( EEG, 'filename',outfname); + %pop_writeeeg(EEG, outfname, 'TYPE','EDF'); + %event_helper(outfname,EEG,{}); +catch + disp(['problem with ', outfname]); +end + +% house-upright +outfname='[batch_dfp]/derivatives/BIDS-Seg-Face13-EEGLAB/[/,1,batch_dfn,_,-1]_desc-SEGhousu_eeg.edf' +[outpath outname outext] = fileparts(outfname); +if ~exist(outpath,'dir') + mkdir(outpath); +end +try + EEG = pop_epoch( tmpEEG, { 'house-upright' }, ... + [-2 3], 'newname', 'direct', 'epochinfo', 'yes'); + EEG = pop_rmbase( EEG, [-1000 0]); + EEG.condition = 'houseu'; + EEG = pop_saveset( EEG, 'filename',outfname); + %pop_writeeeg(EEG, outfname, 'TYPE','EDF'); + %event_helper(outfname,EEG,{}); +catch + disp(['problem with ', outfname]); +end \ No newline at end of file diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_001_task_faceFO_eeg.log b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_001_task_faceFO_eeg.log new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_001_task_faceFO_eeg.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_001_task_faceFO_eeg.m new file mode 100644 index 000000000..10e2d04d0 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_001_task_faceFO_eeg.m @@ -0,0 +1,120 @@ +%LOAD BIDS FILES +sphereLoc = 'derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_icasphere.tsv' +weightsLoc = 'derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_icaweights.tsv' +annoFile = 'derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_desc-qc_annotations.tsv' + +elecFile = '././sub-001/eeg/sub-001_task-faceFO_electrodes.tsv' +eventsFile = '././sub-001/eeg/sub-001_task-faceFO_events.tsv' + +EEG = pop_bidsload('derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_eeg.edf','elecLoc',elecFile,'eventLoc',eventsFile,'icaSphere',sphereLoc,'icaWeights',weightsLoc,'annoLoc',annoFile); + +% add STUDY variables +% legacy mode for finding subject number. +myStr = './sub-001/eeg/sub-001_task-faceFO_eeg.edf'; +indexStart = regexp(myStr,'\-(.*?)\/'); +EEG.subject = str2num(myStr(indexStart+1:indexStart+3)); +%session +EEG.session = 1; + +% Removed flagged time segments and Independent Components +EEG = pop_marks_select_data(EEG,'channel marks',[],'labels',{'manual', 'rank'},'remove','on'); +EEG = pop_marks_select_data(EEG,'time marks',[],'labels',{'manual'},'remove','on'); +EEG = pop_marks_select_data(EEG,'component marks',[],'labels',{'manual'},'remove','on'); +EEG = eeg_checkset(EEG); + +%average rereference. +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual','rank'},'indexes','invert','on'); +EEG = warp_locs( EEG,'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc','transform',[0,0,0,0,0,-1.57,1,1,1],'manual','off'); +EEG = interp_ref(EEG,'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc','chans',chan_inds); +EEG.chaninfo.nosedir='+Y'; +EEG = eeg_checkset(EEG); + +% Interpolate flagged channels +%manual_mark_ind=find(strcmp('manual',{EEG.marks.chan_info.label})); +%rank_mark_ind=find(strcmp('rank',{EEG.marks.chan_info.label})); +%flags=EEG.marks.chan_info(manual_mark_ind).flags+EEG.marks.chan_info(rank_mark_ind).flags; +%chan_inds=find(flags); +%EEG = pop_interp(EEG,chan_inds,'spherical'); + +%purge unnecessary fields... +for i=1:length(EEG.marks.time_info); + EEG.marks.time_info(i).flags=[]; +end +EEG.data_sd_ep=[]; +EEG.c_data_sd_ep=[]; +EEG.data_sd_ch=[]; +EEG.c_data_sd_ch=[]; +EEG.m_neigbr_r_ch=[]; +EEG.c_neigbr_r_ch=[]; +EEG.m_neigbr_r_lat=[]; +EEG.c_neigbr_r_lat=[]; +EEG.amica=[]; +EEG.icaact_sd1_lat=[]; +EEG.c_icaact1_sd_lat=[]; +EEG.icaact_sd2_lat=[]; +EEG.c_icaact_sd2_lat=[]; +EEG.icaact_ta_lat=[]; +EEG.c_icaact_ta_lat=[]; +EEG.icaact_b_lat=[]; +EEG.c_icaact_b_lat=[]; +% EEG.icaact = []; +% EEG.icawinv = []; +% EEG.icasphere = []; +% EEG.icaweights = []; +% EEG.icachansind = []; + +%lowpass filter +%EEG = pop_eegfiltnew(EEG,[], 1,[],1,[],0); +EEG = pop_eegfiltnew(EEG,[],30,[],0,[],0); + +% ICA information must be valid across all segmented files +%icaFileName = 'derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-001/eeg/sub-001_task-faceFO_icaweights.tsv'; +%[outpath outname outext] = fileparts(icaFileName); +%if ~exist(outpath,'dir') +% mkdir(outpath); +%end +%dlmwrite(icaFileName,EEG.icaweights,'\t'); +%dlmwrite(strrep(icaFileName,'_icaweights.tsv','_icasphere.tsv'),EEG.icasphere,'\t'); +%s = {}; +%s.algorithm = 'amica15'; +%s.icachansind = EEG.icachansind; +%s.intendedFor = 'derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-001/eeg/sub-001_task-faceFO'; +%savejson('',s,strrep(icaFileName,'_icaweights.tsv','_icaweights.json')); + +tmpEEG=EEG; + +% face-upright +outfname='derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-001/eeg/sub-001_task-faceFO_desc-SEGfaceu_eeg.edf' +[outpath outname outext] = fileparts(outfname); +if ~exist(outpath,'dir') + mkdir(outpath); +end +try + EEG = pop_epoch( tmpEEG, { 'face-upright' }, ... + [-2 3], 'newname', 'direct', 'epochinfo', 'yes'); + EEG = pop_rmbase( EEG, [-1000 0]); + EEG.condition = 'faceu'; + EEG = pop_saveset( EEG, 'filename',outfname); + %pop_writeeeg(EEG, outfname, 'TYPE','EDF'); + %event_helper(outfname,EEG,{}); +catch + disp(['problem with ', outfname]); +end + +% house-upright +outfname='derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-001/eeg/sub-001_task-faceFO_desc-SEGhousu_eeg.edf' +[outpath outname outext] = fileparts(outfname); +if ~exist(outpath,'dir') + mkdir(outpath); +end +try + EEG = pop_epoch( tmpEEG, { 'house-upright' }, ... + [-2 3], 'newname', 'direct', 'epochinfo', 'yes'); + EEG = pop_rmbase( EEG, [-1000 0]); + EEG.condition = 'houseu'; + EEG = pop_saveset( EEG, 'filename',outfname); + %pop_writeeeg(EEG, outfname, 'TYPE','EDF'); + %event_helper(outfname,EEG,{}); +catch + disp(['problem with ', outfname]); +end \ No newline at end of file diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_002_task_faceFO_eeg.log b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_002_task_faceFO_eeg.log new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_002_task_faceFO_eeg.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_002_task_faceFO_eeg.m new file mode 100644 index 000000000..9b0195531 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_002_task_faceFO_eeg.m @@ -0,0 +1,120 @@ +%LOAD BIDS FILES +sphereLoc = 'derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_icasphere.tsv' +weightsLoc = 'derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_icaweights.tsv' +annoFile = 'derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_desc-qc_annotations.tsv' + +elecFile = '././sub-002/eeg/sub-002_task-faceFO_electrodes.tsv' +eventsFile = '././sub-002/eeg/sub-002_task-faceFO_events.tsv' + +EEG = pop_bidsload('derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_eeg.edf','elecLoc',elecFile,'eventLoc',eventsFile,'icaSphere',sphereLoc,'icaWeights',weightsLoc,'annoLoc',annoFile); + +% add STUDY variables +% legacy mode for finding subject number. +myStr = './sub-002/eeg/sub-002_task-faceFO_eeg.edf'; +indexStart = regexp(myStr,'\-(.*?)\/'); +EEG.subject = str2num(myStr(indexStart+1:indexStart+3)); +%session +EEG.session = 1; + +% Removed flagged time segments and Independent Components +EEG = pop_marks_select_data(EEG,'channel marks',[],'labels',{'manual', 'rank'},'remove','on'); +EEG = pop_marks_select_data(EEG,'time marks',[],'labels',{'manual'},'remove','on'); +EEG = pop_marks_select_data(EEG,'component marks',[],'labels',{'manual'},'remove','on'); +EEG = eeg_checkset(EEG); + +%average rereference. +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual','rank'},'indexes','invert','on'); +EEG = warp_locs( EEG,'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc','transform',[0,0,0,0,0,-1.57,1,1,1],'manual','off'); +EEG = interp_ref(EEG,'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc','chans',chan_inds); +EEG.chaninfo.nosedir='+Y'; +EEG = eeg_checkset(EEG); + +% Interpolate flagged channels +%manual_mark_ind=find(strcmp('manual',{EEG.marks.chan_info.label})); +%rank_mark_ind=find(strcmp('rank',{EEG.marks.chan_info.label})); +%flags=EEG.marks.chan_info(manual_mark_ind).flags+EEG.marks.chan_info(rank_mark_ind).flags; +%chan_inds=find(flags); +%EEG = pop_interp(EEG,chan_inds,'spherical'); + +%purge unnecessary fields... +for i=1:length(EEG.marks.time_info); + EEG.marks.time_info(i).flags=[]; +end +EEG.data_sd_ep=[]; +EEG.c_data_sd_ep=[]; +EEG.data_sd_ch=[]; +EEG.c_data_sd_ch=[]; +EEG.m_neigbr_r_ch=[]; +EEG.c_neigbr_r_ch=[]; +EEG.m_neigbr_r_lat=[]; +EEG.c_neigbr_r_lat=[]; +EEG.amica=[]; +EEG.icaact_sd1_lat=[]; +EEG.c_icaact1_sd_lat=[]; +EEG.icaact_sd2_lat=[]; +EEG.c_icaact_sd2_lat=[]; +EEG.icaact_ta_lat=[]; +EEG.c_icaact_ta_lat=[]; +EEG.icaact_b_lat=[]; +EEG.c_icaact_b_lat=[]; +% EEG.icaact = []; +% EEG.icawinv = []; +% EEG.icasphere = []; +% EEG.icaweights = []; +% EEG.icachansind = []; + +%lowpass filter +%EEG = pop_eegfiltnew(EEG,[], 1,[],1,[],0); +EEG = pop_eegfiltnew(EEG,[],30,[],0,[],0); + +% ICA information must be valid across all segmented files +%icaFileName = 'derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-002/eeg/sub-002_task-faceFO_icaweights.tsv'; +%[outpath outname outext] = fileparts(icaFileName); +%if ~exist(outpath,'dir') +% mkdir(outpath); +%end +%dlmwrite(icaFileName,EEG.icaweights,'\t'); +%dlmwrite(strrep(icaFileName,'_icaweights.tsv','_icasphere.tsv'),EEG.icasphere,'\t'); +%s = {}; +%s.algorithm = 'amica15'; +%s.icachansind = EEG.icachansind; +%s.intendedFor = 'derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-002/eeg/sub-002_task-faceFO'; +%savejson('',s,strrep(icaFileName,'_icaweights.tsv','_icaweights.json')); + +tmpEEG=EEG; + +% face-upright +outfname='derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-002/eeg/sub-002_task-faceFO_desc-SEGfaceu_eeg.edf' +[outpath outname outext] = fileparts(outfname); +if ~exist(outpath,'dir') + mkdir(outpath); +end +try + EEG = pop_epoch( tmpEEG, { 'face-upright' }, ... + [-2 3], 'newname', 'direct', 'epochinfo', 'yes'); + EEG = pop_rmbase( EEG, [-1000 0]); + EEG.condition = 'faceu'; + EEG = pop_saveset( EEG, 'filename',outfname); + %pop_writeeeg(EEG, outfname, 'TYPE','EDF'); + %event_helper(outfname,EEG,{}); +catch + disp(['problem with ', outfname]); +end + +% house-upright +outfname='derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-002/eeg/sub-002_task-faceFO_desc-SEGhousu_eeg.edf' +[outpath outname outext] = fileparts(outfname); +if ~exist(outpath,'dir') + mkdir(outpath); +end +try + EEG = pop_epoch( tmpEEG, { 'house-upright' }, ... + [-2 3], 'newname', 'direct', 'epochinfo', 'yes'); + EEG = pop_rmbase( EEG, [-1000 0]); + EEG.condition = 'houseu'; + EEG = pop_saveset( EEG, 'filename',outfname); + %pop_writeeeg(EEG, outfname, 'TYPE','EDF'); + %event_helper(outfname,EEG,{}); +catch + disp(['problem with ', outfname]); +end \ No newline at end of file diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_003_task_faceFO_eeg.log b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_003_task_faceFO_eeg.log new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_003_task_faceFO_eeg.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_003_task_faceFO_eeg.m new file mode 100644 index 000000000..9812b98eb --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_003_task_faceFO_eeg.m @@ -0,0 +1,120 @@ +%LOAD BIDS FILES +sphereLoc = 'derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_icasphere.tsv' +weightsLoc = 'derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_icaweights.tsv' +annoFile = 'derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_desc-qc_annotations.tsv' + +elecFile = '././sub-003/eeg/sub-003_task-faceFO_electrodes.tsv' +eventsFile = '././sub-003/eeg/sub-003_task-faceFO_events.tsv' + +EEG = pop_bidsload('derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_eeg.edf','elecLoc',elecFile,'eventLoc',eventsFile,'icaSphere',sphereLoc,'icaWeights',weightsLoc,'annoLoc',annoFile); + +% add STUDY variables +% legacy mode for finding subject number. +myStr = './sub-003/eeg/sub-003_task-faceFO_eeg.edf'; +indexStart = regexp(myStr,'\-(.*?)\/'); +EEG.subject = str2num(myStr(indexStart+1:indexStart+3)); +%session +EEG.session = 1; + +% Removed flagged time segments and Independent Components +EEG = pop_marks_select_data(EEG,'channel marks',[],'labels',{'manual', 'rank'},'remove','on'); +EEG = pop_marks_select_data(EEG,'time marks',[],'labels',{'manual'},'remove','on'); +EEG = pop_marks_select_data(EEG,'component marks',[],'labels',{'manual'},'remove','on'); +EEG = eeg_checkset(EEG); + +%average rereference. +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual','rank'},'indexes','invert','on'); +EEG = warp_locs( EEG,'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc','transform',[0,0,0,0,0,-1.57,1,1,1],'manual','off'); +EEG = interp_ref(EEG,'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc','chans',chan_inds); +EEG.chaninfo.nosedir='+Y'; +EEG = eeg_checkset(EEG); + +% Interpolate flagged channels +%manual_mark_ind=find(strcmp('manual',{EEG.marks.chan_info.label})); +%rank_mark_ind=find(strcmp('rank',{EEG.marks.chan_info.label})); +%flags=EEG.marks.chan_info(manual_mark_ind).flags+EEG.marks.chan_info(rank_mark_ind).flags; +%chan_inds=find(flags); +%EEG = pop_interp(EEG,chan_inds,'spherical'); + +%purge unnecessary fields... +for i=1:length(EEG.marks.time_info); + EEG.marks.time_info(i).flags=[]; +end +EEG.data_sd_ep=[]; +EEG.c_data_sd_ep=[]; +EEG.data_sd_ch=[]; +EEG.c_data_sd_ch=[]; +EEG.m_neigbr_r_ch=[]; +EEG.c_neigbr_r_ch=[]; +EEG.m_neigbr_r_lat=[]; +EEG.c_neigbr_r_lat=[]; +EEG.amica=[]; +EEG.icaact_sd1_lat=[]; +EEG.c_icaact1_sd_lat=[]; +EEG.icaact_sd2_lat=[]; +EEG.c_icaact_sd2_lat=[]; +EEG.icaact_ta_lat=[]; +EEG.c_icaact_ta_lat=[]; +EEG.icaact_b_lat=[]; +EEG.c_icaact_b_lat=[]; +% EEG.icaact = []; +% EEG.icawinv = []; +% EEG.icasphere = []; +% EEG.icaweights = []; +% EEG.icachansind = []; + +%lowpass filter +%EEG = pop_eegfiltnew(EEG,[], 1,[],1,[],0); +EEG = pop_eegfiltnew(EEG,[],30,[],0,[],0); + +% ICA information must be valid across all segmented files +%icaFileName = 'derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-003/eeg/sub-003_task-faceFO_icaweights.tsv'; +%[outpath outname outext] = fileparts(icaFileName); +%if ~exist(outpath,'dir') +% mkdir(outpath); +%end +%dlmwrite(icaFileName,EEG.icaweights,'\t'); +%dlmwrite(strrep(icaFileName,'_icaweights.tsv','_icasphere.tsv'),EEG.icasphere,'\t'); +%s = {}; +%s.algorithm = 'amica15'; +%s.icachansind = EEG.icachansind; +%s.intendedFor = 'derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-003/eeg/sub-003_task-faceFO'; +%savejson('',s,strrep(icaFileName,'_icaweights.tsv','_icaweights.json')); + +tmpEEG=EEG; + +% face-upright +outfname='derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-003/eeg/sub-003_task-faceFO_desc-SEGfaceu_eeg.edf' +[outpath outname outext] = fileparts(outfname); +if ~exist(outpath,'dir') + mkdir(outpath); +end +try + EEG = pop_epoch( tmpEEG, { 'face-upright' }, ... + [-2 3], 'newname', 'direct', 'epochinfo', 'yes'); + EEG = pop_rmbase( EEG, [-1000 0]); + EEG.condition = 'faceu'; + EEG = pop_saveset( EEG, 'filename',outfname); + %pop_writeeeg(EEG, outfname, 'TYPE','EDF'); + %event_helper(outfname,EEG,{}); +catch + disp(['problem with ', outfname]); +end + +% house-upright +outfname='derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-003/eeg/sub-003_task-faceFO_desc-SEGhousu_eeg.edf' +[outpath outname outext] = fileparts(outfname); +if ~exist(outpath,'dir') + mkdir(outpath); +end +try + EEG = pop_epoch( tmpEEG, { 'house-upright' }, ... + [-2 3], 'newname', 'direct', 'epochinfo', 'yes'); + EEG = pop_rmbase( EEG, [-1000 0]); + EEG.condition = 'houseu'; + EEG = pop_saveset( EEG, 'filename',outfname); + %pop_writeeeg(EEG, outfname, 'TYPE','EDF'); + %event_helper(outfname,EEG,{}); +catch + disp(['problem with ', outfname]); +end \ No newline at end of file diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_004_task_faceFO_eeg.log b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_004_task_faceFO_eeg.log new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_004_task_faceFO_eeg.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_004_task_faceFO_eeg.m new file mode 100644 index 000000000..8446a207d --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_004_task_faceFO_eeg.m @@ -0,0 +1,120 @@ +%LOAD BIDS FILES +sphereLoc = 'derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_icasphere.tsv' +weightsLoc = 'derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_icaweights.tsv' +annoFile = 'derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_desc-qc_annotations.tsv' + +elecFile = '././sub-004/eeg/sub-004_task-faceFO_electrodes.tsv' +eventsFile = '././sub-004/eeg/sub-004_task-faceFO_events.tsv' + +EEG = pop_bidsload('derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_eeg.edf','elecLoc',elecFile,'eventLoc',eventsFile,'icaSphere',sphereLoc,'icaWeights',weightsLoc,'annoLoc',annoFile); + +% add STUDY variables +% legacy mode for finding subject number. +myStr = './sub-004/eeg/sub-004_task-faceFO_eeg.edf'; +indexStart = regexp(myStr,'\-(.*?)\/'); +EEG.subject = str2num(myStr(indexStart+1:indexStart+3)); +%session +EEG.session = 1; + +% Removed flagged time segments and Independent Components +EEG = pop_marks_select_data(EEG,'channel marks',[],'labels',{'manual', 'rank'},'remove','on'); +EEG = pop_marks_select_data(EEG,'time marks',[],'labels',{'manual'},'remove','on'); +EEG = pop_marks_select_data(EEG,'component marks',[],'labels',{'manual'},'remove','on'); +EEG = eeg_checkset(EEG); + +%average rereference. +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual','rank'},'indexes','invert','on'); +EEG = warp_locs( EEG,'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc','transform',[0,0,0,0,0,-1.57,1,1,1],'manual','off'); +EEG = interp_ref(EEG,'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc','chans',chan_inds); +EEG.chaninfo.nosedir='+Y'; +EEG = eeg_checkset(EEG); + +% Interpolate flagged channels +%manual_mark_ind=find(strcmp('manual',{EEG.marks.chan_info.label})); +%rank_mark_ind=find(strcmp('rank',{EEG.marks.chan_info.label})); +%flags=EEG.marks.chan_info(manual_mark_ind).flags+EEG.marks.chan_info(rank_mark_ind).flags; +%chan_inds=find(flags); +%EEG = pop_interp(EEG,chan_inds,'spherical'); + +%purge unnecessary fields... +for i=1:length(EEG.marks.time_info); + EEG.marks.time_info(i).flags=[]; +end +EEG.data_sd_ep=[]; +EEG.c_data_sd_ep=[]; +EEG.data_sd_ch=[]; +EEG.c_data_sd_ch=[]; +EEG.m_neigbr_r_ch=[]; +EEG.c_neigbr_r_ch=[]; +EEG.m_neigbr_r_lat=[]; +EEG.c_neigbr_r_lat=[]; +EEG.amica=[]; +EEG.icaact_sd1_lat=[]; +EEG.c_icaact1_sd_lat=[]; +EEG.icaact_sd2_lat=[]; +EEG.c_icaact_sd2_lat=[]; +EEG.icaact_ta_lat=[]; +EEG.c_icaact_ta_lat=[]; +EEG.icaact_b_lat=[]; +EEG.c_icaact_b_lat=[]; +% EEG.icaact = []; +% EEG.icawinv = []; +% EEG.icasphere = []; +% EEG.icaweights = []; +% EEG.icachansind = []; + +%lowpass filter +%EEG = pop_eegfiltnew(EEG,[], 1,[],1,[],0); +EEG = pop_eegfiltnew(EEG,[],30,[],0,[],0); + +% ICA information must be valid across all segmented files +%icaFileName = 'derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-004/eeg/sub-004_task-faceFO_icaweights.tsv'; +%[outpath outname outext] = fileparts(icaFileName); +%if ~exist(outpath,'dir') +% mkdir(outpath); +%end +%dlmwrite(icaFileName,EEG.icaweights,'\t'); +%dlmwrite(strrep(icaFileName,'_icaweights.tsv','_icasphere.tsv'),EEG.icasphere,'\t'); +%s = {}; +%s.algorithm = 'amica15'; +%s.icachansind = EEG.icachansind; +%s.intendedFor = 'derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-004/eeg/sub-004_task-faceFO'; +%savejson('',s,strrep(icaFileName,'_icaweights.tsv','_icaweights.json')); + +tmpEEG=EEG; + +% face-upright +outfname='derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-004/eeg/sub-004_task-faceFO_desc-SEGfaceu_eeg.edf' +[outpath outname outext] = fileparts(outfname); +if ~exist(outpath,'dir') + mkdir(outpath); +end +try + EEG = pop_epoch( tmpEEG, { 'face-upright' }, ... + [-2 3], 'newname', 'direct', 'epochinfo', 'yes'); + EEG = pop_rmbase( EEG, [-1000 0]); + EEG.condition = 'faceu'; + EEG = pop_saveset( EEG, 'filename',outfname); + %pop_writeeeg(EEG, outfname, 'TYPE','EDF'); + %event_helper(outfname,EEG,{}); +catch + disp(['problem with ', outfname]); +end + +% house-upright +outfname='derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-004/eeg/sub-004_task-faceFO_desc-SEGhousu_eeg.edf' +[outpath outname outext] = fileparts(outfname); +if ~exist(outpath,'dir') + mkdir(outpath); +end +try + EEG = pop_epoch( tmpEEG, { 'house-upright' }, ... + [-2 3], 'newname', 'direct', 'epochinfo', 'yes'); + EEG = pop_rmbase( EEG, [-1000 0]); + EEG.condition = 'houseu'; + EEG = pop_saveset( EEG, 'filename',outfname); + %pop_writeeeg(EEG, outfname, 'TYPE','EDF'); + %event_helper(outfname,EEG,{}); +catch + disp(['problem with ', outfname]); +end \ No newline at end of file diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_005_task_faceFO_eeg.log b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_005_task_faceFO_eeg.log new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_005_task_faceFO_eeg.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_005_task_faceFO_eeg.m new file mode 100644 index 000000000..ddcd78c0e --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_005_task_faceFO_eeg.m @@ -0,0 +1,120 @@ +%LOAD BIDS FILES +sphereLoc = 'derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_icasphere.tsv' +weightsLoc = 'derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_icaweights.tsv' +annoFile = 'derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_desc-qc_annotations.tsv' + +elecFile = '././sub-005/eeg/sub-005_task-faceFO_electrodes.tsv' +eventsFile = '././sub-005/eeg/sub-005_task-faceFO_events.tsv' + +EEG = pop_bidsload('derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_eeg.edf','elecLoc',elecFile,'eventLoc',eventsFile,'icaSphere',sphereLoc,'icaWeights',weightsLoc,'annoLoc',annoFile); + +% add STUDY variables +% legacy mode for finding subject number. +myStr = './sub-005/eeg/sub-005_task-faceFO_eeg.edf'; +indexStart = regexp(myStr,'\-(.*?)\/'); +EEG.subject = str2num(myStr(indexStart+1:indexStart+3)); +%session +EEG.session = 1; + +% Removed flagged time segments and Independent Components +EEG = pop_marks_select_data(EEG,'channel marks',[],'labels',{'manual', 'rank'},'remove','on'); +EEG = pop_marks_select_data(EEG,'time marks',[],'labels',{'manual'},'remove','on'); +EEG = pop_marks_select_data(EEG,'component marks',[],'labels',{'manual'},'remove','on'); +EEG = eeg_checkset(EEG); + +%average rereference. +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual','rank'},'indexes','invert','on'); +EEG = warp_locs( EEG,'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc','transform',[0,0,0,0,0,-1.57,1,1,1],'manual','off'); +EEG = interp_ref(EEG,'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc','chans',chan_inds); +EEG.chaninfo.nosedir='+Y'; +EEG = eeg_checkset(EEG); + +% Interpolate flagged channels +%manual_mark_ind=find(strcmp('manual',{EEG.marks.chan_info.label})); +%rank_mark_ind=find(strcmp('rank',{EEG.marks.chan_info.label})); +%flags=EEG.marks.chan_info(manual_mark_ind).flags+EEG.marks.chan_info(rank_mark_ind).flags; +%chan_inds=find(flags); +%EEG = pop_interp(EEG,chan_inds,'spherical'); + +%purge unnecessary fields... +for i=1:length(EEG.marks.time_info); + EEG.marks.time_info(i).flags=[]; +end +EEG.data_sd_ep=[]; +EEG.c_data_sd_ep=[]; +EEG.data_sd_ch=[]; +EEG.c_data_sd_ch=[]; +EEG.m_neigbr_r_ch=[]; +EEG.c_neigbr_r_ch=[]; +EEG.m_neigbr_r_lat=[]; +EEG.c_neigbr_r_lat=[]; +EEG.amica=[]; +EEG.icaact_sd1_lat=[]; +EEG.c_icaact1_sd_lat=[]; +EEG.icaact_sd2_lat=[]; +EEG.c_icaact_sd2_lat=[]; +EEG.icaact_ta_lat=[]; +EEG.c_icaact_ta_lat=[]; +EEG.icaact_b_lat=[]; +EEG.c_icaact_b_lat=[]; +% EEG.icaact = []; +% EEG.icawinv = []; +% EEG.icasphere = []; +% EEG.icaweights = []; +% EEG.icachansind = []; + +%lowpass filter +%EEG = pop_eegfiltnew(EEG,[], 1,[],1,[],0); +EEG = pop_eegfiltnew(EEG,[],30,[],0,[],0); + +% ICA information must be valid across all segmented files +%icaFileName = 'derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-005/eeg/sub-005_task-faceFO_icaweights.tsv'; +%[outpath outname outext] = fileparts(icaFileName); +%if ~exist(outpath,'dir') +% mkdir(outpath); +%end +%dlmwrite(icaFileName,EEG.icaweights,'\t'); +%dlmwrite(strrep(icaFileName,'_icaweights.tsv','_icasphere.tsv'),EEG.icasphere,'\t'); +%s = {}; +%s.algorithm = 'amica15'; +%s.icachansind = EEG.icachansind; +%s.intendedFor = 'derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-005/eeg/sub-005_task-faceFO'; +%savejson('',s,strrep(icaFileName,'_icaweights.tsv','_icaweights.json')); + +tmpEEG=EEG; + +% face-upright +outfname='derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-005/eeg/sub-005_task-faceFO_desc-SEGfaceu_eeg.edf' +[outpath outname outext] = fileparts(outfname); +if ~exist(outpath,'dir') + mkdir(outpath); +end +try + EEG = pop_epoch( tmpEEG, { 'face-upright' }, ... + [-2 3], 'newname', 'direct', 'epochinfo', 'yes'); + EEG = pop_rmbase( EEG, [-1000 0]); + EEG.condition = 'faceu'; + EEG = pop_saveset( EEG, 'filename',outfname); + %pop_writeeeg(EEG, outfname, 'TYPE','EDF'); + %event_helper(outfname,EEG,{}); +catch + disp(['problem with ', outfname]); +end + +% house-upright +outfname='derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-005/eeg/sub-005_task-faceFO_desc-SEGhousu_eeg.edf' +[outpath outname outext] = fileparts(outfname); +if ~exist(outpath,'dir') + mkdir(outpath); +end +try + EEG = pop_epoch( tmpEEG, { 'house-upright' }, ... + [-2 3], 'newname', 'direct', 'epochinfo', 'yes'); + EEG = pop_rmbase( EEG, [-1000 0]); + EEG.condition = 'houseu'; + EEG = pop_saveset( EEG, 'filename',outfname); + %pop_writeeeg(EEG, outfname, 'TYPE','EDF'); + %event_helper(outfname,EEG,{}); +catch + disp(['problem with ', outfname]); +end \ No newline at end of file diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_006_task_faceFO_eeg.log b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_006_task_faceFO_eeg.log new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_006_task_faceFO_eeg.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_006_task_faceFO_eeg.m new file mode 100644 index 000000000..46e2babcb --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_006_task_faceFO_eeg.m @@ -0,0 +1,120 @@ +%LOAD BIDS FILES +sphereLoc = 'derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_icasphere.tsv' +weightsLoc = 'derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_icaweights.tsv' +annoFile = 'derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_desc-qc_annotations.tsv' + +elecFile = '././sub-006/eeg/sub-006_task-faceFO_electrodes.tsv' +eventsFile = '././sub-006/eeg/sub-006_task-faceFO_events.tsv' + +EEG = pop_bidsload('derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_eeg.edf','elecLoc',elecFile,'eventLoc',eventsFile,'icaSphere',sphereLoc,'icaWeights',weightsLoc,'annoLoc',annoFile); + +% add STUDY variables +% legacy mode for finding subject number. +myStr = './sub-006/eeg/sub-006_task-faceFO_eeg.edf'; +indexStart = regexp(myStr,'\-(.*?)\/'); +EEG.subject = str2num(myStr(indexStart+1:indexStart+3)); +%session +EEG.session = 1; + +% Removed flagged time segments and Independent Components +EEG = pop_marks_select_data(EEG,'channel marks',[],'labels',{'manual', 'rank'},'remove','on'); +EEG = pop_marks_select_data(EEG,'time marks',[],'labels',{'manual'},'remove','on'); +EEG = pop_marks_select_data(EEG,'component marks',[],'labels',{'manual'},'remove','on'); +EEG = eeg_checkset(EEG); + +%average rereference. +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual','rank'},'indexes','invert','on'); +EEG = warp_locs( EEG,'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc','transform',[0,0,0,0,0,-1.57,1,1,1],'manual','off'); +EEG = interp_ref(EEG,'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc','chans',chan_inds); +EEG.chaninfo.nosedir='+Y'; +EEG = eeg_checkset(EEG); + +% Interpolate flagged channels +%manual_mark_ind=find(strcmp('manual',{EEG.marks.chan_info.label})); +%rank_mark_ind=find(strcmp('rank',{EEG.marks.chan_info.label})); +%flags=EEG.marks.chan_info(manual_mark_ind).flags+EEG.marks.chan_info(rank_mark_ind).flags; +%chan_inds=find(flags); +%EEG = pop_interp(EEG,chan_inds,'spherical'); + +%purge unnecessary fields... +for i=1:length(EEG.marks.time_info); + EEG.marks.time_info(i).flags=[]; +end +EEG.data_sd_ep=[]; +EEG.c_data_sd_ep=[]; +EEG.data_sd_ch=[]; +EEG.c_data_sd_ch=[]; +EEG.m_neigbr_r_ch=[]; +EEG.c_neigbr_r_ch=[]; +EEG.m_neigbr_r_lat=[]; +EEG.c_neigbr_r_lat=[]; +EEG.amica=[]; +EEG.icaact_sd1_lat=[]; +EEG.c_icaact1_sd_lat=[]; +EEG.icaact_sd2_lat=[]; +EEG.c_icaact_sd2_lat=[]; +EEG.icaact_ta_lat=[]; +EEG.c_icaact_ta_lat=[]; +EEG.icaact_b_lat=[]; +EEG.c_icaact_b_lat=[]; +% EEG.icaact = []; +% EEG.icawinv = []; +% EEG.icasphere = []; +% EEG.icaweights = []; +% EEG.icachansind = []; + +%lowpass filter +%EEG = pop_eegfiltnew(EEG,[], 1,[],1,[],0); +EEG = pop_eegfiltnew(EEG,[],30,[],0,[],0); + +% ICA information must be valid across all segmented files +%icaFileName = 'derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-006/eeg/sub-006_task-faceFO_icaweights.tsv'; +%[outpath outname outext] = fileparts(icaFileName); +%if ~exist(outpath,'dir') +% mkdir(outpath); +%end +%dlmwrite(icaFileName,EEG.icaweights,'\t'); +%dlmwrite(strrep(icaFileName,'_icaweights.tsv','_icasphere.tsv'),EEG.icasphere,'\t'); +%s = {}; +%s.algorithm = 'amica15'; +%s.icachansind = EEG.icachansind; +%s.intendedFor = 'derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-006/eeg/sub-006_task-faceFO'; +%savejson('',s,strrep(icaFileName,'_icaweights.tsv','_icaweights.json')); + +tmpEEG=EEG; + +% face-upright +outfname='derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-006/eeg/sub-006_task-faceFO_desc-SEGfaceu_eeg.edf' +[outpath outname outext] = fileparts(outfname); +if ~exist(outpath,'dir') + mkdir(outpath); +end +try + EEG = pop_epoch( tmpEEG, { 'face-upright' }, ... + [-2 3], 'newname', 'direct', 'epochinfo', 'yes'); + EEG = pop_rmbase( EEG, [-1000 0]); + EEG.condition = 'faceu'; + EEG = pop_saveset( EEG, 'filename',outfname); + %pop_writeeeg(EEG, outfname, 'TYPE','EDF'); + %event_helper(outfname,EEG,{}); +catch + disp(['problem with ', outfname]); +end + +% house-upright +outfname='derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-006/eeg/sub-006_task-faceFO_desc-SEGhousu_eeg.edf' +[outpath outname outext] = fileparts(outfname); +if ~exist(outpath,'dir') + mkdir(outpath); +end +try + EEG = pop_epoch( tmpEEG, { 'house-upright' }, ... + [-2 3], 'newname', 'direct', 'epochinfo', 'yes'); + EEG = pop_rmbase( EEG, [-1000 0]); + EEG.condition = 'houseu'; + EEG = pop_saveset( EEG, 'filename',outfname); + %pop_writeeeg(EEG, outfname, 'TYPE','EDF'); + %event_helper(outfname,EEG,{}); +catch + disp(['problem with ', outfname]); +end \ No newline at end of file diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_007_task_faceFO_eeg.log b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_007_task_faceFO_eeg.log new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_007_task_faceFO_eeg.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_007_task_faceFO_eeg.m new file mode 100644 index 000000000..03aa7257e --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_007_task_faceFO_eeg.m @@ -0,0 +1,120 @@ +%LOAD BIDS FILES +sphereLoc = 'derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_icasphere.tsv' +weightsLoc = 'derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_icaweights.tsv' +annoFile = 'derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_desc-qc_annotations.tsv' + +elecFile = '././sub-007/eeg/sub-007_task-faceFO_electrodes.tsv' +eventsFile = '././sub-007/eeg/sub-007_task-faceFO_events.tsv' + +EEG = pop_bidsload('derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_eeg.edf','elecLoc',elecFile,'eventLoc',eventsFile,'icaSphere',sphereLoc,'icaWeights',weightsLoc,'annoLoc',annoFile); + +% add STUDY variables +% legacy mode for finding subject number. +myStr = './sub-007/eeg/sub-007_task-faceFO_eeg.edf'; +indexStart = regexp(myStr,'\-(.*?)\/'); +EEG.subject = str2num(myStr(indexStart+1:indexStart+3)); +%session +EEG.session = 1; + +% Removed flagged time segments and Independent Components +EEG = pop_marks_select_data(EEG,'channel marks',[],'labels',{'manual', 'rank'},'remove','on'); +EEG = pop_marks_select_data(EEG,'time marks',[],'labels',{'manual'},'remove','on'); +EEG = pop_marks_select_data(EEG,'component marks',[],'labels',{'manual'},'remove','on'); +EEG = eeg_checkset(EEG); + +%average rereference. +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual','rank'},'indexes','invert','on'); +EEG = warp_locs( EEG,'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc','transform',[0,0,0,0,0,-1.57,1,1,1],'manual','off'); +EEG = interp_ref(EEG,'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc','chans',chan_inds); +EEG.chaninfo.nosedir='+Y'; +EEG = eeg_checkset(EEG); + +% Interpolate flagged channels +%manual_mark_ind=find(strcmp('manual',{EEG.marks.chan_info.label})); +%rank_mark_ind=find(strcmp('rank',{EEG.marks.chan_info.label})); +%flags=EEG.marks.chan_info(manual_mark_ind).flags+EEG.marks.chan_info(rank_mark_ind).flags; +%chan_inds=find(flags); +%EEG = pop_interp(EEG,chan_inds,'spherical'); + +%purge unnecessary fields... +for i=1:length(EEG.marks.time_info); + EEG.marks.time_info(i).flags=[]; +end +EEG.data_sd_ep=[]; +EEG.c_data_sd_ep=[]; +EEG.data_sd_ch=[]; +EEG.c_data_sd_ch=[]; +EEG.m_neigbr_r_ch=[]; +EEG.c_neigbr_r_ch=[]; +EEG.m_neigbr_r_lat=[]; +EEG.c_neigbr_r_lat=[]; +EEG.amica=[]; +EEG.icaact_sd1_lat=[]; +EEG.c_icaact1_sd_lat=[]; +EEG.icaact_sd2_lat=[]; +EEG.c_icaact_sd2_lat=[]; +EEG.icaact_ta_lat=[]; +EEG.c_icaact_ta_lat=[]; +EEG.icaact_b_lat=[]; +EEG.c_icaact_b_lat=[]; +% EEG.icaact = []; +% EEG.icawinv = []; +% EEG.icasphere = []; +% EEG.icaweights = []; +% EEG.icachansind = []; + +%lowpass filter +%EEG = pop_eegfiltnew(EEG,[], 1,[],1,[],0); +EEG = pop_eegfiltnew(EEG,[],30,[],0,[],0); + +% ICA information must be valid across all segmented files +%icaFileName = 'derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-007/eeg/sub-007_task-faceFO_icaweights.tsv'; +%[outpath outname outext] = fileparts(icaFileName); +%if ~exist(outpath,'dir') +% mkdir(outpath); +%end +%dlmwrite(icaFileName,EEG.icaweights,'\t'); +%dlmwrite(strrep(icaFileName,'_icaweights.tsv','_icasphere.tsv'),EEG.icasphere,'\t'); +%s = {}; +%s.algorithm = 'amica15'; +%s.icachansind = EEG.icachansind; +%s.intendedFor = 'derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-007/eeg/sub-007_task-faceFO'; +%savejson('',s,strrep(icaFileName,'_icaweights.tsv','_icaweights.json')); + +tmpEEG=EEG; + +% face-upright +outfname='derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-007/eeg/sub-007_task-faceFO_desc-SEGfaceu_eeg.edf' +[outpath outname outext] = fileparts(outfname); +if ~exist(outpath,'dir') + mkdir(outpath); +end +try + EEG = pop_epoch( tmpEEG, { 'face-upright' }, ... + [-2 3], 'newname', 'direct', 'epochinfo', 'yes'); + EEG = pop_rmbase( EEG, [-1000 0]); + EEG.condition = 'faceu'; + EEG = pop_saveset( EEG, 'filename',outfname); + %pop_writeeeg(EEG, outfname, 'TYPE','EDF'); + %event_helper(outfname,EEG,{}); +catch + disp(['problem with ', outfname]); +end + +% house-upright +outfname='derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-007/eeg/sub-007_task-faceFO_desc-SEGhousu_eeg.edf' +[outpath outname outext] = fileparts(outfname); +if ~exist(outpath,'dir') + mkdir(outpath); +end +try + EEG = pop_epoch( tmpEEG, { 'house-upright' }, ... + [-2 3], 'newname', 'direct', 'epochinfo', 'yes'); + EEG = pop_rmbase( EEG, [-1000 0]); + EEG.condition = 'houseu'; + EEG = pop_saveset( EEG, 'filename',outfname); + %pop_writeeeg(EEG, outfname, 'TYPE','EDF'); + %event_helper(outfname,EEG,{}); +catch + disp(['problem with ', outfname]); +end \ No newline at end of file diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_008_task_faceFO_eeg.log b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_008_task_faceFO_eeg.log new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_008_task_faceFO_eeg.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_008_task_faceFO_eeg.m new file mode 100644 index 000000000..6024f2caa --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_008_task_faceFO_eeg.m @@ -0,0 +1,120 @@ +%LOAD BIDS FILES +sphereLoc = 'derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_icasphere.tsv' +weightsLoc = 'derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_icaweights.tsv' +annoFile = 'derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_desc-qc_annotations.tsv' + +elecFile = '././sub-008/eeg/sub-008_task-faceFO_electrodes.tsv' +eventsFile = '././sub-008/eeg/sub-008_task-faceFO_events.tsv' + +EEG = pop_bidsload('derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_eeg.edf','elecLoc',elecFile,'eventLoc',eventsFile,'icaSphere',sphereLoc,'icaWeights',weightsLoc,'annoLoc',annoFile); + +% add STUDY variables +% legacy mode for finding subject number. +myStr = './sub-008/eeg/sub-008_task-faceFO_eeg.edf'; +indexStart = regexp(myStr,'\-(.*?)\/'); +EEG.subject = str2num(myStr(indexStart+1:indexStart+3)); +%session +EEG.session = 1; + +% Removed flagged time segments and Independent Components +EEG = pop_marks_select_data(EEG,'channel marks',[],'labels',{'manual', 'rank'},'remove','on'); +EEG = pop_marks_select_data(EEG,'time marks',[],'labels',{'manual'},'remove','on'); +EEG = pop_marks_select_data(EEG,'component marks',[],'labels',{'manual'},'remove','on'); +EEG = eeg_checkset(EEG); + +%average rereference. +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual','rank'},'indexes','invert','on'); +EEG = warp_locs( EEG,'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc','transform',[0,0,0,0,0,-1.57,1,1,1],'manual','off'); +EEG = interp_ref(EEG,'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc','chans',chan_inds); +EEG.chaninfo.nosedir='+Y'; +EEG = eeg_checkset(EEG); + +% Interpolate flagged channels +%manual_mark_ind=find(strcmp('manual',{EEG.marks.chan_info.label})); +%rank_mark_ind=find(strcmp('rank',{EEG.marks.chan_info.label})); +%flags=EEG.marks.chan_info(manual_mark_ind).flags+EEG.marks.chan_info(rank_mark_ind).flags; +%chan_inds=find(flags); +%EEG = pop_interp(EEG,chan_inds,'spherical'); + +%purge unnecessary fields... +for i=1:length(EEG.marks.time_info); + EEG.marks.time_info(i).flags=[]; +end +EEG.data_sd_ep=[]; +EEG.c_data_sd_ep=[]; +EEG.data_sd_ch=[]; +EEG.c_data_sd_ch=[]; +EEG.m_neigbr_r_ch=[]; +EEG.c_neigbr_r_ch=[]; +EEG.m_neigbr_r_lat=[]; +EEG.c_neigbr_r_lat=[]; +EEG.amica=[]; +EEG.icaact_sd1_lat=[]; +EEG.c_icaact1_sd_lat=[]; +EEG.icaact_sd2_lat=[]; +EEG.c_icaact_sd2_lat=[]; +EEG.icaact_ta_lat=[]; +EEG.c_icaact_ta_lat=[]; +EEG.icaact_b_lat=[]; +EEG.c_icaact_b_lat=[]; +% EEG.icaact = []; +% EEG.icawinv = []; +% EEG.icasphere = []; +% EEG.icaweights = []; +% EEG.icachansind = []; + +%lowpass filter +%EEG = pop_eegfiltnew(EEG,[], 1,[],1,[],0); +EEG = pop_eegfiltnew(EEG,[],30,[],0,[],0); + +% ICA information must be valid across all segmented files +%icaFileName = 'derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-008/eeg/sub-008_task-faceFO_icaweights.tsv'; +%[outpath outname outext] = fileparts(icaFileName); +%if ~exist(outpath,'dir') +% mkdir(outpath); +%end +%dlmwrite(icaFileName,EEG.icaweights,'\t'); +%dlmwrite(strrep(icaFileName,'_icaweights.tsv','_icasphere.tsv'),EEG.icasphere,'\t'); +%s = {}; +%s.algorithm = 'amica15'; +%s.icachansind = EEG.icachansind; +%s.intendedFor = 'derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-008/eeg/sub-008_task-faceFO'; +%savejson('',s,strrep(icaFileName,'_icaweights.tsv','_icaweights.json')); + +tmpEEG=EEG; + +% face-upright +outfname='derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-008/eeg/sub-008_task-faceFO_desc-SEGfaceu_eeg.edf' +[outpath outname outext] = fileparts(outfname); +if ~exist(outpath,'dir') + mkdir(outpath); +end +try + EEG = pop_epoch( tmpEEG, { 'face-upright' }, ... + [-2 3], 'newname', 'direct', 'epochinfo', 'yes'); + EEG = pop_rmbase( EEG, [-1000 0]); + EEG.condition = 'faceu'; + EEG = pop_saveset( EEG, 'filename',outfname); + %pop_writeeeg(EEG, outfname, 'TYPE','EDF'); + %event_helper(outfname,EEG,{}); +catch + disp(['problem with ', outfname]); +end + +% house-upright +outfname='derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-008/eeg/sub-008_task-faceFO_desc-SEGhousu_eeg.edf' +[outpath outname outext] = fileparts(outfname); +if ~exist(outpath,'dir') + mkdir(outpath); +end +try + EEG = pop_epoch( tmpEEG, { 'house-upright' }, ... + [-2 3], 'newname', 'direct', 'epochinfo', 'yes'); + EEG = pop_rmbase( EEG, [-1000 0]); + EEG.condition = 'houseu'; + EEG = pop_saveset( EEG, 'filename',outfname); + %pop_writeeeg(EEG, outfname, 'TYPE','EDF'); + %event_helper(outfname,EEG,{}); +catch + disp(['problem with ', outfname]); +end \ No newline at end of file diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_009_task_faceFO_eeg.log b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_009_task_faceFO_eeg.log new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_009_task_faceFO_eeg.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_009_task_faceFO_eeg.m new file mode 100644 index 000000000..773a9d32f --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_009_task_faceFO_eeg.m @@ -0,0 +1,120 @@ +%LOAD BIDS FILES +sphereLoc = 'derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_icasphere.tsv' +weightsLoc = 'derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_icaweights.tsv' +annoFile = 'derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_desc-qc_annotations.tsv' + +elecFile = '././sub-009/eeg/sub-009_task-faceFO_electrodes.tsv' +eventsFile = '././sub-009/eeg/sub-009_task-faceFO_events.tsv' + +EEG = pop_bidsload('derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_eeg.edf','elecLoc',elecFile,'eventLoc',eventsFile,'icaSphere',sphereLoc,'icaWeights',weightsLoc,'annoLoc',annoFile); + +% add STUDY variables +% legacy mode for finding subject number. +myStr = './sub-009/eeg/sub-009_task-faceFO_eeg.edf'; +indexStart = regexp(myStr,'\-(.*?)\/'); +EEG.subject = str2num(myStr(indexStart+1:indexStart+3)); +%session +EEG.session = 1; + +% Removed flagged time segments and Independent Components +EEG = pop_marks_select_data(EEG,'channel marks',[],'labels',{'manual', 'rank'},'remove','on'); +EEG = pop_marks_select_data(EEG,'time marks',[],'labels',{'manual'},'remove','on'); +EEG = pop_marks_select_data(EEG,'component marks',[],'labels',{'manual'},'remove','on'); +EEG = eeg_checkset(EEG); + +%average rereference. +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual','rank'},'indexes','invert','on'); +EEG = warp_locs( EEG,'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc','transform',[0,0,0,0,0,-1.57,1,1,1],'manual','off'); +EEG = interp_ref(EEG,'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc','chans',chan_inds); +EEG.chaninfo.nosedir='+Y'; +EEG = eeg_checkset(EEG); + +% Interpolate flagged channels +%manual_mark_ind=find(strcmp('manual',{EEG.marks.chan_info.label})); +%rank_mark_ind=find(strcmp('rank',{EEG.marks.chan_info.label})); +%flags=EEG.marks.chan_info(manual_mark_ind).flags+EEG.marks.chan_info(rank_mark_ind).flags; +%chan_inds=find(flags); +%EEG = pop_interp(EEG,chan_inds,'spherical'); + +%purge unnecessary fields... +for i=1:length(EEG.marks.time_info); + EEG.marks.time_info(i).flags=[]; +end +EEG.data_sd_ep=[]; +EEG.c_data_sd_ep=[]; +EEG.data_sd_ch=[]; +EEG.c_data_sd_ch=[]; +EEG.m_neigbr_r_ch=[]; +EEG.c_neigbr_r_ch=[]; +EEG.m_neigbr_r_lat=[]; +EEG.c_neigbr_r_lat=[]; +EEG.amica=[]; +EEG.icaact_sd1_lat=[]; +EEG.c_icaact1_sd_lat=[]; +EEG.icaact_sd2_lat=[]; +EEG.c_icaact_sd2_lat=[]; +EEG.icaact_ta_lat=[]; +EEG.c_icaact_ta_lat=[]; +EEG.icaact_b_lat=[]; +EEG.c_icaact_b_lat=[]; +% EEG.icaact = []; +% EEG.icawinv = []; +% EEG.icasphere = []; +% EEG.icaweights = []; +% EEG.icachansind = []; + +%lowpass filter +%EEG = pop_eegfiltnew(EEG,[], 1,[],1,[],0); +EEG = pop_eegfiltnew(EEG,[],30,[],0,[],0); + +% ICA information must be valid across all segmented files +%icaFileName = 'derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-009/eeg/sub-009_task-faceFO_icaweights.tsv'; +%[outpath outname outext] = fileparts(icaFileName); +%if ~exist(outpath,'dir') +% mkdir(outpath); +%end +%dlmwrite(icaFileName,EEG.icaweights,'\t'); +%dlmwrite(strrep(icaFileName,'_icaweights.tsv','_icasphere.tsv'),EEG.icasphere,'\t'); +%s = {}; +%s.algorithm = 'amica15'; +%s.icachansind = EEG.icachansind; +%s.intendedFor = 'derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-009/eeg/sub-009_task-faceFO'; +%savejson('',s,strrep(icaFileName,'_icaweights.tsv','_icaweights.json')); + +tmpEEG=EEG; + +% face-upright +outfname='derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-009/eeg/sub-009_task-faceFO_desc-SEGfaceu_eeg.edf' +[outpath outname outext] = fileparts(outfname); +if ~exist(outpath,'dir') + mkdir(outpath); +end +try + EEG = pop_epoch( tmpEEG, { 'face-upright' }, ... + [-2 3], 'newname', 'direct', 'epochinfo', 'yes'); + EEG = pop_rmbase( EEG, [-1000 0]); + EEG.condition = 'faceu'; + EEG = pop_saveset( EEG, 'filename',outfname); + %pop_writeeeg(EEG, outfname, 'TYPE','EDF'); + %event_helper(outfname,EEG,{}); +catch + disp(['problem with ', outfname]); +end + +% house-upright +outfname='derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-009/eeg/sub-009_task-faceFO_desc-SEGhousu_eeg.edf' +[outpath outname outext] = fileparts(outfname); +if ~exist(outpath,'dir') + mkdir(outpath); +end +try + EEG = pop_epoch( tmpEEG, { 'house-upright' }, ... + [-2 3], 'newname', 'direct', 'epochinfo', 'yes'); + EEG = pop_rmbase( EEG, [-1000 0]); + EEG.condition = 'houseu'; + EEG = pop_saveset( EEG, 'filename',outfname); + %pop_writeeeg(EEG, outfname, 'TYPE','EDF'); + %event_helper(outfname,EEG,{}); +catch + disp(['problem with ', outfname]); +end \ No newline at end of file diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_010_task_faceFO_eeg.log b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_010_task_faceFO_eeg.log new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_010_task_faceFO_eeg.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_010_task_faceFO_eeg.m new file mode 100644 index 000000000..ad67ea3b7 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/log/seg_face_house_up_inv-2019-10-04T10-32-52/seg_face_house_up_inv_sub_010_task_faceFO_eeg.m @@ -0,0 +1,120 @@ +%LOAD BIDS FILES +sphereLoc = 'derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_icasphere.tsv' +weightsLoc = 'derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_icaweights.tsv' +annoFile = 'derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_desc-qc_annotations.tsv' + +elecFile = '././sub-010/eeg/sub-010_task-faceFO_electrodes.tsv' +eventsFile = '././sub-010/eeg/sub-010_task-faceFO_events.tsv' + +EEG = pop_bidsload('derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_eeg.edf','elecLoc',elecFile,'eventLoc',eventsFile,'icaSphere',sphereLoc,'icaWeights',weightsLoc,'annoLoc',annoFile); + +% add STUDY variables +% legacy mode for finding subject number. +myStr = './sub-010/eeg/sub-010_task-faceFO_eeg.edf'; +indexStart = regexp(myStr,'\-(.*?)\/'); +EEG.subject = str2num(myStr(indexStart+1:indexStart+3)); +%session +EEG.session = 1; + +% Removed flagged time segments and Independent Components +EEG = pop_marks_select_data(EEG,'channel marks',[],'labels',{'manual', 'rank'},'remove','on'); +EEG = pop_marks_select_data(EEG,'time marks',[],'labels',{'manual'},'remove','on'); +EEG = pop_marks_select_data(EEG,'component marks',[],'labels',{'manual'},'remove','on'); +EEG = eeg_checkset(EEG); + +%average rereference. +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual','rank'},'indexes','invert','on'); +EEG = warp_locs( EEG,'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc','transform',[0,0,0,0,0,-1.57,1,1,1],'manual','off'); +EEG = interp_ref(EEG,'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc','chans',chan_inds); +EEG.chaninfo.nosedir='+Y'; +EEG = eeg_checkset(EEG); + +% Interpolate flagged channels +%manual_mark_ind=find(strcmp('manual',{EEG.marks.chan_info.label})); +%rank_mark_ind=find(strcmp('rank',{EEG.marks.chan_info.label})); +%flags=EEG.marks.chan_info(manual_mark_ind).flags+EEG.marks.chan_info(rank_mark_ind).flags; +%chan_inds=find(flags); +%EEG = pop_interp(EEG,chan_inds,'spherical'); + +%purge unnecessary fields... +for i=1:length(EEG.marks.time_info); + EEG.marks.time_info(i).flags=[]; +end +EEG.data_sd_ep=[]; +EEG.c_data_sd_ep=[]; +EEG.data_sd_ch=[]; +EEG.c_data_sd_ch=[]; +EEG.m_neigbr_r_ch=[]; +EEG.c_neigbr_r_ch=[]; +EEG.m_neigbr_r_lat=[]; +EEG.c_neigbr_r_lat=[]; +EEG.amica=[]; +EEG.icaact_sd1_lat=[]; +EEG.c_icaact1_sd_lat=[]; +EEG.icaact_sd2_lat=[]; +EEG.c_icaact_sd2_lat=[]; +EEG.icaact_ta_lat=[]; +EEG.c_icaact_ta_lat=[]; +EEG.icaact_b_lat=[]; +EEG.c_icaact_b_lat=[]; +% EEG.icaact = []; +% EEG.icawinv = []; +% EEG.icasphere = []; +% EEG.icaweights = []; +% EEG.icachansind = []; + +%lowpass filter +%EEG = pop_eegfiltnew(EEG,[], 1,[],1,[],0); +EEG = pop_eegfiltnew(EEG,[],30,[],0,[],0); + +% ICA information must be valid across all segmented files +%icaFileName = 'derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-010/eeg/sub-010_task-faceFO_icaweights.tsv'; +%[outpath outname outext] = fileparts(icaFileName); +%if ~exist(outpath,'dir') +% mkdir(outpath); +%end +%dlmwrite(icaFileName,EEG.icaweights,'\t'); +%dlmwrite(strrep(icaFileName,'_icaweights.tsv','_icasphere.tsv'),EEG.icasphere,'\t'); +%s = {}; +%s.algorithm = 'amica15'; +%s.icachansind = EEG.icachansind; +%s.intendedFor = 'derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-010/eeg/sub-010_task-faceFO'; +%savejson('',s,strrep(icaFileName,'_icaweights.tsv','_icaweights.json')); + +tmpEEG=EEG; + +% face-upright +outfname='derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-010/eeg/sub-010_task-faceFO_desc-SEGfaceu_eeg.edf' +[outpath outname outext] = fileparts(outfname); +if ~exist(outpath,'dir') + mkdir(outpath); +end +try + EEG = pop_epoch( tmpEEG, { 'face-upright' }, ... + [-2 3], 'newname', 'direct', 'epochinfo', 'yes'); + EEG = pop_rmbase( EEG, [-1000 0]); + EEG.condition = 'faceu'; + EEG = pop_saveset( EEG, 'filename',outfname); + %pop_writeeeg(EEG, outfname, 'TYPE','EDF'); + %event_helper(outfname,EEG,{}); +catch + disp(['problem with ', outfname]); +end + +% house-upright +outfname='derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-010/eeg/sub-010_task-faceFO_desc-SEGhousu_eeg.edf' +[outpath outname outext] = fileparts(outfname); +if ~exist(outpath,'dir') + mkdir(outpath); +end +try + EEG = pop_epoch( tmpEEG, { 'house-upright' }, ... + [-2 3], 'newname', 'direct', 'epochinfo', 'yes'); + EEG = pop_rmbase( EEG, [-1000 0]); + EEG.condition = 'houseu'; + EEG = pop_saveset( EEG, 'filename',outfname); + %pop_writeeeg(EEG, outfname, 'TYPE','EDF'); + %event_helper(outfname,EEG,{}); +catch + disp(['problem with ', outfname]); +end \ No newline at end of file diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-001/eeg/sub-001_task-faceFO_desc-SEGfaceu_eeg.fdt b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-001/eeg/sub-001_task-faceFO_desc-SEGfaceu_eeg.fdt new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-001/eeg/sub-001_task-faceFO_desc-SEGfaceu_eeg.set b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-001/eeg/sub-001_task-faceFO_desc-SEGfaceu_eeg.set new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-001/eeg/sub-001_task-faceFO_desc-SEGhousu_eeg.fdt b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-001/eeg/sub-001_task-faceFO_desc-SEGhousu_eeg.fdt new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-001/eeg/sub-001_task-faceFO_desc-SEGhousu_eeg.set b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-001/eeg/sub-001_task-faceFO_desc-SEGhousu_eeg.set new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-002/eeg/sub-002_task-faceFO_desc-SEGfaceu_eeg.fdt b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-002/eeg/sub-002_task-faceFO_desc-SEGfaceu_eeg.fdt new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-002/eeg/sub-002_task-faceFO_desc-SEGfaceu_eeg.set b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-002/eeg/sub-002_task-faceFO_desc-SEGfaceu_eeg.set new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-002/eeg/sub-002_task-faceFO_desc-SEGhousu_eeg.fdt b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-002/eeg/sub-002_task-faceFO_desc-SEGhousu_eeg.fdt new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-002/eeg/sub-002_task-faceFO_desc-SEGhousu_eeg.set b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-002/eeg/sub-002_task-faceFO_desc-SEGhousu_eeg.set new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-003/eeg/sub-003_task-faceFO_desc-SEGfaceu_eeg.fdt b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-003/eeg/sub-003_task-faceFO_desc-SEGfaceu_eeg.fdt new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-003/eeg/sub-003_task-faceFO_desc-SEGfaceu_eeg.set b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-003/eeg/sub-003_task-faceFO_desc-SEGfaceu_eeg.set new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-003/eeg/sub-003_task-faceFO_desc-SEGhousu_eeg.fdt b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-003/eeg/sub-003_task-faceFO_desc-SEGhousu_eeg.fdt new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-003/eeg/sub-003_task-faceFO_desc-SEGhousu_eeg.set b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-003/eeg/sub-003_task-faceFO_desc-SEGhousu_eeg.set new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-004/eeg/sub-004_task-faceFO_desc-SEGfaceu_eeg.fdt b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-004/eeg/sub-004_task-faceFO_desc-SEGfaceu_eeg.fdt new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-004/eeg/sub-004_task-faceFO_desc-SEGfaceu_eeg.set b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-004/eeg/sub-004_task-faceFO_desc-SEGfaceu_eeg.set new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-004/eeg/sub-004_task-faceFO_desc-SEGhousu_eeg.fdt b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-004/eeg/sub-004_task-faceFO_desc-SEGhousu_eeg.fdt new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-004/eeg/sub-004_task-faceFO_desc-SEGhousu_eeg.set b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-004/eeg/sub-004_task-faceFO_desc-SEGhousu_eeg.set new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-005/eeg/sub-005_task-faceFO_desc-SEGfaceu_eeg.fdt b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-005/eeg/sub-005_task-faceFO_desc-SEGfaceu_eeg.fdt new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-005/eeg/sub-005_task-faceFO_desc-SEGfaceu_eeg.set b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-005/eeg/sub-005_task-faceFO_desc-SEGfaceu_eeg.set new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-005/eeg/sub-005_task-faceFO_desc-SEGhousu_eeg.fdt b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-005/eeg/sub-005_task-faceFO_desc-SEGhousu_eeg.fdt new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-005/eeg/sub-005_task-faceFO_desc-SEGhousu_eeg.set b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-005/eeg/sub-005_task-faceFO_desc-SEGhousu_eeg.set new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-006/eeg/sub-006_task-faceFO_desc-SEGfaceu_eeg.fdt b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-006/eeg/sub-006_task-faceFO_desc-SEGfaceu_eeg.fdt new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-006/eeg/sub-006_task-faceFO_desc-SEGfaceu_eeg.set b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-006/eeg/sub-006_task-faceFO_desc-SEGfaceu_eeg.set new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-006/eeg/sub-006_task-faceFO_desc-SEGhousu_eeg.fdt b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-006/eeg/sub-006_task-faceFO_desc-SEGhousu_eeg.fdt new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-006/eeg/sub-006_task-faceFO_desc-SEGhousu_eeg.set b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-006/eeg/sub-006_task-faceFO_desc-SEGhousu_eeg.set new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-007/eeg/sub-007_task-faceFO_desc-SEGfaceu_eeg.fdt b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-007/eeg/sub-007_task-faceFO_desc-SEGfaceu_eeg.fdt new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-007/eeg/sub-007_task-faceFO_desc-SEGfaceu_eeg.set b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-007/eeg/sub-007_task-faceFO_desc-SEGfaceu_eeg.set new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-007/eeg/sub-007_task-faceFO_desc-SEGhousu_eeg.fdt b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-007/eeg/sub-007_task-faceFO_desc-SEGhousu_eeg.fdt new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-007/eeg/sub-007_task-faceFO_desc-SEGhousu_eeg.set b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-007/eeg/sub-007_task-faceFO_desc-SEGhousu_eeg.set new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-008/eeg/sub-008_task-faceFO_desc-SEGfaceu_eeg.fdt b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-008/eeg/sub-008_task-faceFO_desc-SEGfaceu_eeg.fdt new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-008/eeg/sub-008_task-faceFO_desc-SEGfaceu_eeg.set b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-008/eeg/sub-008_task-faceFO_desc-SEGfaceu_eeg.set new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-008/eeg/sub-008_task-faceFO_desc-SEGhousu_eeg.fdt b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-008/eeg/sub-008_task-faceFO_desc-SEGhousu_eeg.fdt new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-008/eeg/sub-008_task-faceFO_desc-SEGhousu_eeg.set b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-008/eeg/sub-008_task-faceFO_desc-SEGhousu_eeg.set new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-009/eeg/sub-009_task-faceFO_desc-SEGfaceu_eeg.fdt b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-009/eeg/sub-009_task-faceFO_desc-SEGfaceu_eeg.fdt new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-009/eeg/sub-009_task-faceFO_desc-SEGfaceu_eeg.set b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-009/eeg/sub-009_task-faceFO_desc-SEGfaceu_eeg.set new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-009/eeg/sub-009_task-faceFO_desc-SEGhousu_eeg.fdt b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-009/eeg/sub-009_task-faceFO_desc-SEGhousu_eeg.fdt new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-009/eeg/sub-009_task-faceFO_desc-SEGhousu_eeg.set b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-009/eeg/sub-009_task-faceFO_desc-SEGhousu_eeg.set new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-010/eeg/sub-010_task-faceFO_desc-SEGfaceu_eeg.fdt b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-010/eeg/sub-010_task-faceFO_desc-SEGfaceu_eeg.fdt new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-010/eeg/sub-010_task-faceFO_desc-SEGfaceu_eeg.set b/eeg_face13/derivatives/BIDS-Lossless-EEG/derivatives/BIDS-Seg-Face13-EEGLAB/sub-010/eeg/sub-010_task-faceFO_desc-SEGfaceu_eeg.set new file mode 100644 index 000000000..e69de29bb diff --git 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z_!)Kga9R&U^MR8_YWHcqjws*^{uuFj;Z#?4N!%}TA!?S_N^q3U@}_p(rmsZz5)h9k z1UYPyY2YqT>7KV)Ejdbxc480jAivymap(g^MJDs%}1Cze@ zAPS8js%oWzUJ-*3}D1gfS{w1JS3-$#}YZq97*Fvk~fn?}k5aB<-!m?295 zZb_aWF-tO#!~ZGiFk_Z>8a{-1#vyd+%-_p6yP3i+Vr@I$6FYNoJL-u86Sg<=)s0?p z&dfq6N2coDOs_S2Th7ezOB|VMdox*gpJsn|TL+Fz^}U&_TVJpc&KxChWO4}bVr4Sd3$d0Q*_jn}9GM(e5A9~M&Uft0m;p{q qPNmD;OxAylIaBU5d)) as we were doing prior +if ~exist('EEG', 'var') + eeglab; +end +% Use matlog disp function override +logging_override_disp(true); +% Use matlog INFO level debugging +logging_set_log_level('INFO') + +% Don't run with traditional but enable some of the options +save_default_options ('-mat-binary'); +confirm_recursive_rmdir(false); + +% This makes | and & || and && in earlier version of Octave where +% | and & dont short curcuit by default +if exist('do_braindead_shortcircuit_evaluation', 'builtin') + do_braindead_shortcircuit_evaluation(true); +end + +% Options +pop_editoptions('option_savetwofiles', 1,'option_single',0); + +%% SCRIPT DESCRIPTION +% This script features the main preprocessing and cleaning of the data before the ICA process begin. Data is flagged for high standard deveiavtion +% by channel to remove comically bad channels. The data is then rereferenced to the average site, and high/low pass filtered. +% Channel neighbor correlations are then calculated to find odd channels and bridged channels. The outermost channel is also flagged to reduce the rank of the data. +% After another rereference channel correlations are calculated by time epoch and flagged. The file is saved containing the, marks structure, then saved again +% with all of the mentioned flags puregd from the data in order to prime it for ICA. +% +%% From Config key_strswap Description +%----------------------------------------------------------------------------------------------------------------------------- +% in_path = [] Relative path to input data files assuming cd = work_path +% montage_info = [0.500 -22.000 -48.000 -0.065 0.000 -1.580 1060.000 1260.000 1220.000] Montage information to fit channel locations on a standard surface. +% This can be a transformations matrix (e.g. [-.14,-22,-48,-.07,0,-1.57,1080,1260,1240]) +% ... or a a file containing a transformation matrix (e.g. ./sub-001/eeg/sub-001_task-faceFO_eeg.edf_transmat.mat). +% ... or a BIDS channel location tsv file (assuming that during BIDS init the channels were coregistered to the standard surface). +% staging_script = derivatives/BIDS-Lossless-EEG/code/scripts/face13_staging.m Script file path/name that creates marks based on events in the raw data file. +% recur_sec = 1 Recurrence (sec) for artifact detection epoching (e.g. .5, Default 1) +% limit_sec = [0 1] Limits (sec) for artifact detection epoching (e.g. [-.5 0], Default [0 recur_sec]) +% sd_t_meth = na Method used for flagging epochs (e.g. 'q' (quantiles), or 'na' (default)) +% sd_t_vals = [.3 .7] Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7]) +% sd_t_o = 16 z threshold for flagging epochs during standard deviation criteria (e.g. 6) +% sd_t_f_meth = fixed Fixed method used for flagging epochs (should be 'fixed') +% sd_t_f_vals = Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% sd_t_f_o = .2 z threshold for flagging epochs during fixed standard deviation criteria (e.g. .2) +% sd_t_pad = 1 Number of windows to pad onto each side of the ch_sd time flag +% sd_ch_meth = q Method used for flagging channels (e.g. 'q' (quantiles), or 'na' (default)) +% sd_ch_vals = [.3 .7] Percentage trim for confidence intervals during channel standard deviation criteria (e.g. [.3 .7]) +% sd_ch_o = 16 z threshold for flagging channels during standard deviation criteria (e.g. 6) +% sd_ch_f_meth = fixed Fixed method used for flagging channels (should be 'fixed') +% sd_ch_f_vals = Percentage trim for confidence intervals during channel standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% sd_ch_f_o = .2 z threshold for flagging channel during fixed standard deviation criteria (e.g. 6) +% ref_loc_file = derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc Name of file containing the reference locations (including the relative path) +% low_bound_hz = 1 Lower bound of the filter bass-band +% high_bound_hz = 100 Upper bound of the filter bass-band +% save_f_res = 1 1 if you want to save the filter residuals (Default: 1) +% n_nbr_ch = 3 Number of channels to use in nearest neighbour r calculation (for channel criteria) +% r_ch_meth = q Method used for flagging low r channels (e.g. 'q' (quantiles), or 'na' (default)) +% r_ch_vals = [.3,.7] Percentage trim for confidence intervals during low r channel standard deviation criteria (e.g. [.3 .7]) +% r_ch_o = 16 z threshold for flagging channel during neighbour r criteria (e.g. 3) +% r_ch_f_meth = fixed Fixed method used for flagging low r channels (should be 'fixed') +% r_ch_f_vals = Percentage trim for confidence intervals during low r channel standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% r_ch_f_o = .2 z threshold for flagging channel during fixed neighbour r criteria (e.g. 3) +% bridge_trim = 40 Percentage trim for z calculation of bridged channels (e.g. 40 = 20% top and bottom) +% bridge_z = 6 z threshold for flagging channel during bridging criteria (e.g. 8) +% n_nbr_t = 3 Number of channels to use in nearest neighbour r calculation (for epoch criteria) +% r_t_meth = q Method used for flagging low r epochs (e.g. 'q' (quantiles), or 'na' (default)) +% r_t_vals = [.3 .7] Percentage trim for confidence intervals during low r epoch standard deviation criteria (e.g. [.3 .7]) +% r_t_o = 16 z threshold for flagging epochs during neighbour r criteria (e.g. 3) +% r_t_f_meth = fixed Fixed method used for flagging low r epochs (should be 'fixed') +% r_t_f_vals = Percentage trim for confidence intervals during low r epoch standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% r_t_f_o = .2 z threshold for flagging epochs during fixed neighbour r criteria (e.g. 3) +% min_gap_ms = 2000 Minimum time (ms) to allow between periods marked for rejection +% out_path = derivatives/BIDS-Lossless-EEG Relative path to output data files assuming cd = work_path +% amica_param_file = derivatives/BIDS-Lossless-EEG/code/misc/amica15_default.param template amicadefs.param file to modify +% amica_threads_s02 = 8 number of threads to use for running s02 amica script (Default: 8) + +%% CHECK FOR OUTPUT PATH AND CRETAE IF NECESSARY +if ~exist('derivatives/BIDS-Lossless-EEG/./sub-001/eeg','dir'); + disp('Making directory derivatives/BIDS-Lossless-EEG/./sub-001/eeg'); + mkdir derivatives/BIDS-Lossless-EEG/./sub-001/eeg +end + +%% LOAD DATASET +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +EEG = pop_bidsload('./sub-001/eeg/sub-001_task-faceFO_eeg.edf','gui',0); + +if ~isfield(EEG,'marks'); + if isempty(EEG.icaweights) + EEG.marks=marks_init(size(EEG.data)); + else + EEG.marks=marks_init(size(EEG.data),min(size(EEG.icaweights))); + end +end + +EEG.marks = marks_add_label(EEG.marks,'time_info', ... +{'init_ind',[0,0,1],[1:EEG.pnts]}); + +%execute the staging script if specified. +if exist('derivatives/BIDS-Lossless-EEG/code/scripts/face13_staging.m'); + [ssp,ssn,sse]=fileparts('derivatives/BIDS-Lossless-EEG/code/scripts/face13_staging.m'); + addpath(ssp); + eval(ssn); +end + +%add channel coregistration procedure here. +%warp locations to standard head surface: +if ~isempty('[0.500 -22.000 -48.000 -0.065 0.000 -1.580 1060.000 1260.000 1220.000]'); + if isempty(str2num('[0.500 -22.000 -48.000 -0.065 0.000 -1.580 1060.000 1260.000 1220.000]')); + %if it is a BIDS channel tsv, load the tsv,sd_t_f_vals + %else read the file that is assumed to be a transformation matrix. + else + EEG = warp_locs( EEG,'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc', ... + 'transform',[[0.500 -22.000 -48.000 -0.065 0.000 -1.580 1060.000 1260.000 1220.000]], ... + 'manual','off'); + end +end + +% Apply average re-reference +%chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +%chan_m = mean(EEG.data(chan_inds,:),1); +%chan_m_mat = repmat(chan_m,size(EEG.data,1),1); +%EEG.data = EEG.data - chan_m_mat; +%clear chan_m_mat; + +% Window the continuous data +logging_log('INFO', 'Windowing the continous data...'); +EEG = marks_continuous2epochs(EEG,'recurrence',[1],'limits',[[0 1]]); +logging_log('INFO', 'LOADED DATASET...'); + +% rereference to selected channels.. +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +epoch_inds = marks_label2index(EEG.marks.time_info,{'manual'},'indexes','invert','on'); +[EEG,trim_ch_sd]=chan_variance(EEG,'data_field','data', ... + 'chan_inds',chan_inds, ... + 'epoch_inds',epoch_inds, ... + 'plot_figs','off'); + +chan_dist=zeros(size(trim_ch_sd)); +for i=1:size(trim_ch_sd,2); + chan_dist(:,i)=(trim_ch_sd(:,i)-median(trim_ch_sd(:,i)))/diff(quantile(trim_ch_sd(:,i),[.3,.7])); +end +mean_chan_dist=mean(chan_dist,2); +m=median(mean_chan_dist); +q=quantile(mean_chan_dist,[.3,.7]); + +refchans=find(mean_chan_distve_trimmean(msr,40,1)+ve_trimstd(msr,40,1)*6); +logging_log('INFO', 'IDENTIFIED BRIDGED CHANNELS...'); + +% Edit the channel flag info structure +logging_log('INFO', 'Updating chflaginfo structure...'); +lnkflags = zeros(EEG.nbchan,1); +lnkflags(chan_inds(flag_b_chan_inds))=1; +EEG.marks = marks_add_label(EEG.marks,'chan_info', ... + {'bridge',[.7,1,.7],[.2,1,.2],-1,lnkflags}); +logging_log('INFO', 'EDITED CHANFLAGINFO STRUCT...'); + +% Combine low_rand bridge chan_info flags into 'manual'... +EEG = pop_marks_merge_labels(EEG,'chan_info',{'low_r','bridge'},'target_label','manual'); +logging_log('INFO', 'COMBINED MARKS STRUCTURE INTO MANUAL FLAGS...'); + +%% FLAG RANK CHAN +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Flags the channel that is the least unique (the best channel to remove prior +% to ICA in order to account for the rereference rank deficiency. + +logging_log('INFO', 'Updating chflaginfo structure...'); +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +[r_max,rank_ind] = max(data_r_ch(chan_inds)); +rankflags = zeros(EEG.nbchan,1); +rankflags(chan_inds(rank_ind))=1; +EEG.marks = marks_add_label(EEG.marks,'chan_info', ... + {'rank',[.1,.1,.24],[.1,.1,.24],-1,rankflags}); +logging_log('INFO', 'EDITED CHANFLAGINFO STRUCT...'); + +%% REREFERENCE TO INTERPOLATED AVERAGE SITE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Rereference the data to an average interpolated site containined 19 channels +logging_log('INFO', 'Rereferencing to averaged interpolated site...'); +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +EEG = interp_ref(EEG,'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc','chans',chan_inds); +EEG = eeg_checkset(EEG); +logging_log('INFO', 'REREFERENCED TO INTERPOLATED AVERAGE SITE...'); + +%% CALCULATE NEAREST NEIGHBOUR R VALUES +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Similarly to the neighbor r calculation done between channels this section looks +% at the correlation, but between all channels and for epochs of time. Time segmenents +% are flagged for removal. + +logging_log('INFO', 'Calculating nearest neighbour r array for window criteria...'); +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +epoch_inds = marks_label2index(EEG.marks.time_info,{'manual'},'indexes','invert','on'); +[EEG,data_r_t,~,~,~] = chan_neighbour_r(EEG, ... + 3,'max', ... + 'chan_inds',chan_inds, ... + 'epoch_inds',epoch_inds, ... + 'plotfigs','off'); +logging_log('INFO', 'CALCULATED NEAREST NEIGHBOUR R VALUES...'); + +% Create the window criteria vector for flagging low_r time_info... +logging_log('INFO', 'Assessing epoch r distributions criteria...') +[~,flag_r_t_inds]=marks_array2flags(data_r_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_dir','neg', ... + 'init_crit',16, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); +logging_log('INFO', 'CREATED EPOCH CRITERIA VECTOR...'); + +% Edit the time flag info structure +logging_log('INFO', 'Updating latflaginfo structure...'); +lowr_epoch_flags = zeros(size(EEG.data(1,:,:))); +lowr_epoch_flags(1,:,epoch_inds(flag_r_t_inds))=1; +EEG.marks = marks_add_label(EEG.marks,'time_info', ... + {'low_r',[0,1,0],lowr_epoch_flags}); +clear lowr_epoch_flags; +logging_log('INFO', 'TIME TO: UPDATE REJECTION STRUCTURE...'); + +% Combine low_r time_info flags into 'manual'... +EEG = pop_marks_merge_labels(EEG,'time_info',{'low_r'},'target_label','manual'); +logging_log('INFO', 'COMBINED MARKS STRUCTURE INTO MANUAL FLAGS...'); + +% Concatenate epoched data back to continuous data +logging_log('INFO', 'Concatenating windowed data...'); +EEG = marks_epochs2continuous(EEG); +EEG = eeg_checkset(EEG,'eventconsistency'); +logging_log('INFO', 'CONCATENATED THE WINDOWED DATA INTO CONTINUOUS DATA...'); + +EEG=pop_marks_flag_gap(EEG,{'manual'},2000,'mark_gap',[.8,.8,.8],'offsets',[0 0],'ref_point','both'); + +% Combine mark_gap time_info flags into 'manual'... +EEG = pop_marks_merge_labels(EEG,'time_info',{'mark_gap'},'target_label','manual'); +logging_log('INFO', 'COMBINED MARKS STRUCTURE INTO MANUAL FLAGS...'); + +%%%----------------------------------------------------------------------- +%% SAVE sa.set FILE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +logging_log('INFO', 'Saving file: ./sub-001/eeg/sub-001_task-faceFO_sa.set...'); +if ~exist('derivatives/BIDS-Lossless-EEG/./sub-001/eeg','dir'); + mkdir('derivatives/BIDS-Lossless-EEG/./sub-001/eeg'); +end +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_sa.set'); +logging_log('INFO', 'SAVED sa FILE...'); + +%%%----------------------------------------------------------------------- +%% PREPARE FILE FOR AMICA +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Purges the flagged data to make sure it is primed for ICA and creates the amica paramaeter file. + +% Removed flagged channels and time segments +sprintf('%s','Purging flagged channels...\n'); +EEG = pop_marks_select_data(EEG,'channel marks',[],'labels',{'manual','rank'},'remove','on'); +EEG = pop_marks_select_data(EEG,'time marks',[],'labels',{'manual'},'remove','on'); +EEG = eeg_checkset(EEG); +logging_log('INFO', 'TIME TO: PURGE DATA...'); + +% Make sure EEG.data is doubles for use in amica +EEG.data = double(EEG.data); + +% Save the SA purge file +logging_log('INFO',sprintf('%s','Saving file: derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_sa_purge.set...')); +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_sa_purge.set'); + +% Save diagnostic arrays +try OCTAVE_VERSION; + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_data_sd_t.mat','data_sd_t'); + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_data_sd_ch.mat','data_sd_ch'); + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_data_r_ch.mat','data_r_ch'); + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_data_r_t.mat','data_r_t'); +catch + save('derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_data_sd_t.mat','data_sd_t'); + save('derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_data_sd_ch.mat','data_sd_ch'); + save('derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_data_r_ch.mat','data_r_ch'); + save('derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_data_r_t.mat','data_r_t'); +end + +% Save the AMICA parameter file +swapstr = file_strswap('derivatives/BIDS-Lossless-EEG/code/misc/amica15_default.param', ... + '[repstr_fdt_fname]','derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_sa_purge.fdt', ... + '[repstr_outpath]','derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_amicaout_init', ... + '[repstr_nbchan]', num2str(EEG.nbchan), ... + '[repstr_pnts]', sprintf('%12.0f',EEG.pnts), ... + '[repstr_tpp]', '8', ... + '[repstr_pca]', num2str(EEG.nbchan)); +fidparam = fopen('derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_init.param','w'); +fprintf(fidparam,'%s',swapstr); +fclose(fidparam); + +% CREATE THE AMICA OUTPUT FOLDER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Needed for MPI jobs on cluster only in order to prevent a race effect bug. + +[~] = rmdir('derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_amicaout_init','s'); +mkdir derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_amicaout_init; +logging_log('INFO', 'Created Amica Output Folder'); + +logging_log('INFO', 'Scalpart complete'); +logging_log('INFO', 'Scheduler: sbatch'); +print_chan_sample(EEG); diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s01_scalpart-2019-10-01T14-35-11/s01_scalpart_sub_002_task_faceFO_eeg.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s01_scalpart-2019-10-01T14-35-11/s01_scalpart_sub_002_task_faceFO_eeg.m new file mode 100644 index 000000000..c1ee21695 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s01_scalpart-2019-10-01T14-35-11/s01_scalpart_sub_002_task_faceFO_eeg.m @@ -0,0 +1,529 @@ +warning off; + +pkg load signal; +pkg load parallel; +pkg load io; + +file_path = fileparts(mfilename('fullpath')); + +addpath([file_path '/../../code/dependencies/eeglab_lossless'], ... + [file_path '/../../code/dependencies/matlog'], ... + [file_path '/../../code/tools']); +cd '/scratch/tk11br/Face13/'; + +% eeglab auto loads the path correctly instead of shotgun blasting with +% addpath(genpath(<>)) as we were doing prior +if ~exist('EEG', 'var') + eeglab; +end +% Use matlog disp function override +logging_override_disp(true); +% Use matlog INFO level debugging +logging_set_log_level('INFO') + +% Don't run with traditional but enable some of the options +save_default_options ('-mat-binary'); +confirm_recursive_rmdir(false); + +% This makes | and & || and && in earlier version of Octave where +% | and & dont short curcuit by default +if exist('do_braindead_shortcircuit_evaluation', 'builtin') + do_braindead_shortcircuit_evaluation(true); +end + +% Options +pop_editoptions('option_savetwofiles', 1,'option_single',0); + +%% SCRIPT DESCRIPTION +% This script features the main preprocessing and cleaning of the data before the ICA process begin. Data is flagged for high standard deveiavtion +% by channel to remove comically bad channels. The data is then rereferenced to the average site, and high/low pass filtered. +% Channel neighbor correlations are then calculated to find odd channels and bridged channels. The outermost channel is also flagged to reduce the rank of the data. +% After another rereference channel correlations are calculated by time epoch and flagged. The file is saved containing the, marks structure, then saved again +% with all of the mentioned flags puregd from the data in order to prime it for ICA. +% +%% From Config key_strswap Description +%----------------------------------------------------------------------------------------------------------------------------- +% in_path = [] Relative path to input data files assuming cd = work_path +% montage_info = [0.500 -22.000 -48.000 -0.065 0.000 -1.580 1060.000 1260.000 1220.000] Montage information to fit channel locations on a standard surface. +% This can be a transformations matrix (e.g. [-.14,-22,-48,-.07,0,-1.57,1080,1260,1240]) +% ... or a a file containing a transformation matrix (e.g. ./sub-002/eeg/sub-002_task-faceFO_eeg.edf_transmat.mat). +% ... or a BIDS channel location tsv file (assuming that during BIDS init the channels were coregistered to the standard surface). +% staging_script = derivatives/BIDS-Lossless-EEG/code/scripts/face13_staging.m Script file path/name that creates marks based on events in the raw data file. +% recur_sec = 1 Recurrence (sec) for artifact detection epoching (e.g. .5, Default 1) +% limit_sec = [0 1] Limits (sec) for artifact detection epoching (e.g. [-.5 0], Default [0 recur_sec]) +% sd_t_meth = na Method used for flagging epochs (e.g. 'q' (quantiles), or 'na' (default)) +% sd_t_vals = [.3 .7] Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7]) +% sd_t_o = 16 z threshold for flagging epochs during standard deviation criteria (e.g. 6) +% sd_t_f_meth = fixed Fixed method used for flagging epochs (should be 'fixed') +% sd_t_f_vals = Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% sd_t_f_o = .2 z threshold for flagging epochs during fixed standard deviation criteria (e.g. .2) +% sd_t_pad = 1 Number of windows to pad onto each side of the ch_sd time flag +% sd_ch_meth = q Method used for flagging channels (e.g. 'q' (quantiles), or 'na' (default)) +% sd_ch_vals = [.3 .7] Percentage trim for confidence intervals during channel standard deviation criteria (e.g. [.3 .7]) +% sd_ch_o = 16 z threshold for flagging channels during standard deviation criteria (e.g. 6) +% sd_ch_f_meth = fixed Fixed method used for flagging channels (should be 'fixed') +% sd_ch_f_vals = Percentage trim for confidence intervals during channel standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% sd_ch_f_o = .2 z threshold for flagging channel during fixed standard deviation criteria (e.g. 6) +% ref_loc_file = derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc Name of file containing the reference locations (including the relative path) +% low_bound_hz = 1 Lower bound of the filter bass-band +% high_bound_hz = 100 Upper bound of the filter bass-band +% save_f_res = 1 1 if you want to save the filter residuals (Default: 1) +% n_nbr_ch = 3 Number of channels to use in nearest neighbour r calculation (for channel criteria) +% r_ch_meth = q Method used for flagging low r channels (e.g. 'q' (quantiles), or 'na' (default)) +% r_ch_vals = [.3,.7] Percentage trim for confidence intervals during low r channel standard deviation criteria (e.g. [.3 .7]) +% r_ch_o = 16 z threshold for flagging channel during neighbour r criteria (e.g. 3) +% r_ch_f_meth = fixed Fixed method used for flagging low r channels (should be 'fixed') +% r_ch_f_vals = Percentage trim for confidence intervals during low r channel standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% r_ch_f_o = .2 z threshold for flagging channel during fixed neighbour r criteria (e.g. 3) +% bridge_trim = 40 Percentage trim for z calculation of bridged channels (e.g. 40 = 20% top and bottom) +% bridge_z = 6 z threshold for flagging channel during bridging criteria (e.g. 8) +% n_nbr_t = 3 Number of channels to use in nearest neighbour r calculation (for epoch criteria) +% r_t_meth = q Method used for flagging low r epochs (e.g. 'q' (quantiles), or 'na' (default)) +% r_t_vals = [.3 .7] Percentage trim for confidence intervals during low r epoch standard deviation criteria (e.g. [.3 .7]) +% r_t_o = 16 z threshold for flagging epochs during neighbour r criteria (e.g. 3) +% r_t_f_meth = fixed Fixed method used for flagging low r epochs (should be 'fixed') +% r_t_f_vals = Percentage trim for confidence intervals during low r epoch standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% r_t_f_o = .2 z threshold for flagging epochs during fixed neighbour r criteria (e.g. 3) +% min_gap_ms = 2000 Minimum time (ms) to allow between periods marked for rejection +% out_path = derivatives/BIDS-Lossless-EEG Relative path to output data files assuming cd = work_path +% amica_param_file = derivatives/BIDS-Lossless-EEG/code/misc/amica15_default.param template amicadefs.param file to modify +% amica_threads_s02 = 8 number of threads to use for running s02 amica script (Default: 8) + +%% CHECK FOR OUTPUT PATH AND CRETAE IF NECESSARY +if ~exist('derivatives/BIDS-Lossless-EEG/./sub-002/eeg','dir'); + disp('Making directory derivatives/BIDS-Lossless-EEG/./sub-002/eeg'); + mkdir derivatives/BIDS-Lossless-EEG/./sub-002/eeg +end + +%% LOAD DATASET +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +EEG = pop_bidsload('./sub-002/eeg/sub-002_task-faceFO_eeg.edf','gui',0); + +if ~isfield(EEG,'marks'); + if isempty(EEG.icaweights) + EEG.marks=marks_init(size(EEG.data)); + else + EEG.marks=marks_init(size(EEG.data),min(size(EEG.icaweights))); + end +end + +EEG.marks = marks_add_label(EEG.marks,'time_info', ... +{'init_ind',[0,0,1],[1:EEG.pnts]}); + +%execute the staging script if specified. +if exist('derivatives/BIDS-Lossless-EEG/code/scripts/face13_staging.m'); + [ssp,ssn,sse]=fileparts('derivatives/BIDS-Lossless-EEG/code/scripts/face13_staging.m'); + addpath(ssp); + eval(ssn); +end + +%add channel coregistration procedure here. +%warp locations to standard head surface: +if ~isempty('[0.500 -22.000 -48.000 -0.065 0.000 -1.580 1060.000 1260.000 1220.000]'); + if isempty(str2num('[0.500 -22.000 -48.000 -0.065 0.000 -1.580 1060.000 1260.000 1220.000]')); + %if it is a BIDS channel tsv, load the tsv,sd_t_f_vals + %else read the file that is assumed to be a transformation matrix. + else + EEG = warp_locs( EEG,'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc', ... + 'transform',[[0.500 -22.000 -48.000 -0.065 0.000 -1.580 1060.000 1260.000 1220.000]], ... + 'manual','off'); + end +end + +% Apply average re-reference +%chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +%chan_m = mean(EEG.data(chan_inds,:),1); +%chan_m_mat = repmat(chan_m,size(EEG.data,1),1); +%EEG.data = EEG.data - chan_m_mat; +%clear chan_m_mat; + +% Window the continuous data +logging_log('INFO', 'Windowing the continous data...'); +EEG = marks_continuous2epochs(EEG,'recurrence',[1],'limits',[[0 1]]); +logging_log('INFO', 'LOADED DATASET...'); + +% rereference to selected channels.. +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +epoch_inds = marks_label2index(EEG.marks.time_info,{'manual'},'indexes','invert','on'); +[EEG,trim_ch_sd]=chan_variance(EEG,'data_field','data', ... + 'chan_inds',chan_inds, ... + 'epoch_inds',epoch_inds, ... + 'plot_figs','off'); + +chan_dist=zeros(size(trim_ch_sd)); +for i=1:size(trim_ch_sd,2); + chan_dist(:,i)=(trim_ch_sd(:,i)-median(trim_ch_sd(:,i)))/diff(quantile(trim_ch_sd(:,i),[.3,.7])); +end +mean_chan_dist=mean(chan_dist,2); +m=median(mean_chan_dist); +q=quantile(mean_chan_dist,[.3,.7]); + +refchans=find(mean_chan_distve_trimmean(msr,40,1)+ve_trimstd(msr,40,1)*6); +logging_log('INFO', 'IDENTIFIED BRIDGED CHANNELS...'); + +% Edit the channel flag info structure +logging_log('INFO', 'Updating chflaginfo structure...'); +lnkflags = zeros(EEG.nbchan,1); +lnkflags(chan_inds(flag_b_chan_inds))=1; +EEG.marks = marks_add_label(EEG.marks,'chan_info', ... + {'bridge',[.7,1,.7],[.2,1,.2],-1,lnkflags}); +logging_log('INFO', 'EDITED CHANFLAGINFO STRUCT...'); + +% Combine low_rand bridge chan_info flags into 'manual'... +EEG = pop_marks_merge_labels(EEG,'chan_info',{'low_r','bridge'},'target_label','manual'); +logging_log('INFO', 'COMBINED MARKS STRUCTURE INTO MANUAL FLAGS...'); + +%% FLAG RANK CHAN +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Flags the channel that is the least unique (the best channel to remove prior +% to ICA in order to account for the rereference rank deficiency. + +logging_log('INFO', 'Updating chflaginfo structure...'); +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +[r_max,rank_ind] = max(data_r_ch(chan_inds)); +rankflags = zeros(EEG.nbchan,1); +rankflags(chan_inds(rank_ind))=1; +EEG.marks = marks_add_label(EEG.marks,'chan_info', ... + {'rank',[.1,.1,.24],[.1,.1,.24],-1,rankflags}); +logging_log('INFO', 'EDITED CHANFLAGINFO STRUCT...'); + +%% REREFERENCE TO INTERPOLATED AVERAGE SITE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Rereference the data to an average interpolated site containined 19 channels +logging_log('INFO', 'Rereferencing to averaged interpolated site...'); +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +EEG = interp_ref(EEG,'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc','chans',chan_inds); +EEG = eeg_checkset(EEG); +logging_log('INFO', 'REREFERENCED TO INTERPOLATED AVERAGE SITE...'); + +%% CALCULATE NEAREST NEIGHBOUR R VALUES +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Similarly to the neighbor r calculation done between channels this section looks +% at the correlation, but between all channels and for epochs of time. Time segmenents +% are flagged for removal. + +logging_log('INFO', 'Calculating nearest neighbour r array for window criteria...'); +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +epoch_inds = marks_label2index(EEG.marks.time_info,{'manual'},'indexes','invert','on'); +[EEG,data_r_t,~,~,~] = chan_neighbour_r(EEG, ... + 3,'max', ... + 'chan_inds',chan_inds, ... + 'epoch_inds',epoch_inds, ... + 'plotfigs','off'); +logging_log('INFO', 'CALCULATED NEAREST NEIGHBOUR R VALUES...'); + +% Create the window criteria vector for flagging low_r time_info... +logging_log('INFO', 'Assessing epoch r distributions criteria...') +[~,flag_r_t_inds]=marks_array2flags(data_r_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_dir','neg', ... + 'init_crit',16, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); +logging_log('INFO', 'CREATED EPOCH CRITERIA VECTOR...'); + +% Edit the time flag info structure +logging_log('INFO', 'Updating latflaginfo structure...'); +lowr_epoch_flags = zeros(size(EEG.data(1,:,:))); +lowr_epoch_flags(1,:,epoch_inds(flag_r_t_inds))=1; +EEG.marks = marks_add_label(EEG.marks,'time_info', ... + {'low_r',[0,1,0],lowr_epoch_flags}); +clear lowr_epoch_flags; +logging_log('INFO', 'TIME TO: UPDATE REJECTION STRUCTURE...'); + +% Combine low_r time_info flags into 'manual'... +EEG = pop_marks_merge_labels(EEG,'time_info',{'low_r'},'target_label','manual'); +logging_log('INFO', 'COMBINED MARKS STRUCTURE INTO MANUAL FLAGS...'); + +% Concatenate epoched data back to continuous data +logging_log('INFO', 'Concatenating windowed data...'); +EEG = marks_epochs2continuous(EEG); +EEG = eeg_checkset(EEG,'eventconsistency'); +logging_log('INFO', 'CONCATENATED THE WINDOWED DATA INTO CONTINUOUS DATA...'); + +EEG=pop_marks_flag_gap(EEG,{'manual'},2000,'mark_gap',[.8,.8,.8],'offsets',[0 0],'ref_point','both'); + +% Combine mark_gap time_info flags into 'manual'... +EEG = pop_marks_merge_labels(EEG,'time_info',{'mark_gap'},'target_label','manual'); +logging_log('INFO', 'COMBINED MARKS STRUCTURE INTO MANUAL FLAGS...'); + +%%%----------------------------------------------------------------------- +%% SAVE sa.set FILE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +logging_log('INFO', 'Saving file: ./sub-002/eeg/sub-002_task-faceFO_sa.set...'); +if ~exist('derivatives/BIDS-Lossless-EEG/./sub-002/eeg','dir'); + mkdir('derivatives/BIDS-Lossless-EEG/./sub-002/eeg'); +end +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_sa.set'); +logging_log('INFO', 'SAVED sa FILE...'); + +%%%----------------------------------------------------------------------- +%% PREPARE FILE FOR AMICA +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Purges the flagged data to make sure it is primed for ICA and creates the amica paramaeter file. + +% Removed flagged channels and time segments +sprintf('%s','Purging flagged channels...\n'); +EEG = pop_marks_select_data(EEG,'channel marks',[],'labels',{'manual','rank'},'remove','on'); +EEG = pop_marks_select_data(EEG,'time marks',[],'labels',{'manual'},'remove','on'); +EEG = eeg_checkset(EEG); +logging_log('INFO', 'TIME TO: PURGE DATA...'); + +% Make sure EEG.data is doubles for use in amica +EEG.data = double(EEG.data); + +% Save the SA purge file +logging_log('INFO',sprintf('%s','Saving file: derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_sa_purge.set...')); +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_sa_purge.set'); + +% Save diagnostic arrays +try OCTAVE_VERSION; + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_data_sd_t.mat','data_sd_t'); + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_data_sd_ch.mat','data_sd_ch'); + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_data_r_ch.mat','data_r_ch'); + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_data_r_t.mat','data_r_t'); +catch + save('derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_data_sd_t.mat','data_sd_t'); + save('derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_data_sd_ch.mat','data_sd_ch'); + save('derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_data_r_ch.mat','data_r_ch'); + save('derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_data_r_t.mat','data_r_t'); +end + +% Save the AMICA parameter file +swapstr = file_strswap('derivatives/BIDS-Lossless-EEG/code/misc/amica15_default.param', ... + '[repstr_fdt_fname]','derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_sa_purge.fdt', ... + '[repstr_outpath]','derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_amicaout_init', ... + '[repstr_nbchan]', num2str(EEG.nbchan), ... + '[repstr_pnts]', sprintf('%12.0f',EEG.pnts), ... + '[repstr_tpp]', '8', ... + '[repstr_pca]', num2str(EEG.nbchan)); +fidparam = fopen('derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_init.param','w'); +fprintf(fidparam,'%s',swapstr); +fclose(fidparam); + +% CREATE THE AMICA OUTPUT FOLDER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Needed for MPI jobs on cluster only in order to prevent a race effect bug. + +[~] = rmdir('derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_amicaout_init','s'); +mkdir derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_amicaout_init; +logging_log('INFO', 'Created Amica Output Folder'); + +logging_log('INFO', 'Scalpart complete'); +logging_log('INFO', 'Scheduler: sbatch'); +print_chan_sample(EEG); diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s01_scalpart-2019-10-01T14-35-11/s01_scalpart_sub_003_task_faceFO_eeg.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s01_scalpart-2019-10-01T14-35-11/s01_scalpart_sub_003_task_faceFO_eeg.m new file mode 100644 index 000000000..02922e51e --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s01_scalpart-2019-10-01T14-35-11/s01_scalpart_sub_003_task_faceFO_eeg.m @@ -0,0 +1,529 @@ +warning off; + +pkg load signal; +pkg load parallel; +pkg load io; + +file_path = fileparts(mfilename('fullpath')); + +addpath([file_path '/../../code/dependencies/eeglab_lossless'], ... + [file_path '/../../code/dependencies/matlog'], ... + [file_path '/../../code/tools']); +cd '/scratch/tk11br/Face13/'; + +% eeglab auto loads the path correctly instead of shotgun blasting with +% addpath(genpath(<>)) as we were doing prior +if ~exist('EEG', 'var') + eeglab; +end +% Use matlog disp function override +logging_override_disp(true); +% Use matlog INFO level debugging +logging_set_log_level('INFO') + +% Don't run with traditional but enable some of the options +save_default_options ('-mat-binary'); +confirm_recursive_rmdir(false); + +% This makes | and & || and && in earlier version of Octave where +% | and & dont short curcuit by default +if exist('do_braindead_shortcircuit_evaluation', 'builtin') + do_braindead_shortcircuit_evaluation(true); +end + +% Options +pop_editoptions('option_savetwofiles', 1,'option_single',0); + +%% SCRIPT DESCRIPTION +% This script features the main preprocessing and cleaning of the data before the ICA process begin. Data is flagged for high standard deveiavtion +% by channel to remove comically bad channels. The data is then rereferenced to the average site, and high/low pass filtered. +% Channel neighbor correlations are then calculated to find odd channels and bridged channels. The outermost channel is also flagged to reduce the rank of the data. +% After another rereference channel correlations are calculated by time epoch and flagged. The file is saved containing the, marks structure, then saved again +% with all of the mentioned flags puregd from the data in order to prime it for ICA. +% +%% From Config key_strswap Description +%----------------------------------------------------------------------------------------------------------------------------- +% in_path = [] Relative path to input data files assuming cd = work_path +% montage_info = [0.500 -22.000 -48.000 -0.065 0.000 -1.580 1060.000 1260.000 1220.000] Montage information to fit channel locations on a standard surface. +% This can be a transformations matrix (e.g. [-.14,-22,-48,-.07,0,-1.57,1080,1260,1240]) +% ... or a a file containing a transformation matrix (e.g. ./sub-003/eeg/sub-003_task-faceFO_eeg.edf_transmat.mat). +% ... or a BIDS channel location tsv file (assuming that during BIDS init the channels were coregistered to the standard surface). +% staging_script = derivatives/BIDS-Lossless-EEG/code/scripts/face13_staging.m Script file path/name that creates marks based on events in the raw data file. +% recur_sec = 1 Recurrence (sec) for artifact detection epoching (e.g. .5, Default 1) +% limit_sec = [0 1] Limits (sec) for artifact detection epoching (e.g. [-.5 0], Default [0 recur_sec]) +% sd_t_meth = na Method used for flagging epochs (e.g. 'q' (quantiles), or 'na' (default)) +% sd_t_vals = [.3 .7] Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7]) +% sd_t_o = 16 z threshold for flagging epochs during standard deviation criteria (e.g. 6) +% sd_t_f_meth = fixed Fixed method used for flagging epochs (should be 'fixed') +% sd_t_f_vals = Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% sd_t_f_o = .2 z threshold for flagging epochs during fixed standard deviation criteria (e.g. .2) +% sd_t_pad = 1 Number of windows to pad onto each side of the ch_sd time flag +% sd_ch_meth = q Method used for flagging channels (e.g. 'q' (quantiles), or 'na' (default)) +% sd_ch_vals = [.3 .7] Percentage trim for confidence intervals during channel standard deviation criteria (e.g. [.3 .7]) +% sd_ch_o = 16 z threshold for flagging channels during standard deviation criteria (e.g. 6) +% sd_ch_f_meth = fixed Fixed method used for flagging channels (should be 'fixed') +% sd_ch_f_vals = Percentage trim for confidence intervals during channel standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% sd_ch_f_o = .2 z threshold for flagging channel during fixed standard deviation criteria (e.g. 6) +% ref_loc_file = derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc Name of file containing the reference locations (including the relative path) +% low_bound_hz = 1 Lower bound of the filter bass-band +% high_bound_hz = 100 Upper bound of the filter bass-band +% save_f_res = 1 1 if you want to save the filter residuals (Default: 1) +% n_nbr_ch = 3 Number of channels to use in nearest neighbour r calculation (for channel criteria) +% r_ch_meth = q Method used for flagging low r channels (e.g. 'q' (quantiles), or 'na' (default)) +% r_ch_vals = [.3,.7] Percentage trim for confidence intervals during low r channel standard deviation criteria (e.g. [.3 .7]) +% r_ch_o = 16 z threshold for flagging channel during neighbour r criteria (e.g. 3) +% r_ch_f_meth = fixed Fixed method used for flagging low r channels (should be 'fixed') +% r_ch_f_vals = Percentage trim for confidence intervals during low r channel standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% r_ch_f_o = .2 z threshold for flagging channel during fixed neighbour r criteria (e.g. 3) +% bridge_trim = 40 Percentage trim for z calculation of bridged channels (e.g. 40 = 20% top and bottom) +% bridge_z = 6 z threshold for flagging channel during bridging criteria (e.g. 8) +% n_nbr_t = 3 Number of channels to use in nearest neighbour r calculation (for epoch criteria) +% r_t_meth = q Method used for flagging low r epochs (e.g. 'q' (quantiles), or 'na' (default)) +% r_t_vals = [.3 .7] Percentage trim for confidence intervals during low r epoch standard deviation criteria (e.g. [.3 .7]) +% r_t_o = 16 z threshold for flagging epochs during neighbour r criteria (e.g. 3) +% r_t_f_meth = fixed Fixed method used for flagging low r epochs (should be 'fixed') +% r_t_f_vals = Percentage trim for confidence intervals during low r epoch standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% r_t_f_o = .2 z threshold for flagging epochs during fixed neighbour r criteria (e.g. 3) +% min_gap_ms = 2000 Minimum time (ms) to allow between periods marked for rejection +% out_path = derivatives/BIDS-Lossless-EEG Relative path to output data files assuming cd = work_path +% amica_param_file = derivatives/BIDS-Lossless-EEG/code/misc/amica15_default.param template amicadefs.param file to modify +% amica_threads_s02 = 8 number of threads to use for running s02 amica script (Default: 8) + +%% CHECK FOR OUTPUT PATH AND CRETAE IF NECESSARY +if ~exist('derivatives/BIDS-Lossless-EEG/./sub-003/eeg','dir'); + disp('Making directory derivatives/BIDS-Lossless-EEG/./sub-003/eeg'); + mkdir derivatives/BIDS-Lossless-EEG/./sub-003/eeg +end + +%% LOAD DATASET +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +EEG = pop_bidsload('./sub-003/eeg/sub-003_task-faceFO_eeg.edf','gui',0); + +if ~isfield(EEG,'marks'); + if isempty(EEG.icaweights) + EEG.marks=marks_init(size(EEG.data)); + else + EEG.marks=marks_init(size(EEG.data),min(size(EEG.icaweights))); + end +end + +EEG.marks = marks_add_label(EEG.marks,'time_info', ... +{'init_ind',[0,0,1],[1:EEG.pnts]}); + +%execute the staging script if specified. +if exist('derivatives/BIDS-Lossless-EEG/code/scripts/face13_staging.m'); + [ssp,ssn,sse]=fileparts('derivatives/BIDS-Lossless-EEG/code/scripts/face13_staging.m'); + addpath(ssp); + eval(ssn); +end + +%add channel coregistration procedure here. +%warp locations to standard head surface: +if ~isempty('[0.500 -22.000 -48.000 -0.065 0.000 -1.580 1060.000 1260.000 1220.000]'); + if isempty(str2num('[0.500 -22.000 -48.000 -0.065 0.000 -1.580 1060.000 1260.000 1220.000]')); + %if it is a BIDS channel tsv, load the tsv,sd_t_f_vals + %else read the file that is assumed to be a transformation matrix. + else + EEG = warp_locs( EEG,'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc', ... + 'transform',[[0.500 -22.000 -48.000 -0.065 0.000 -1.580 1060.000 1260.000 1220.000]], ... + 'manual','off'); + end +end + +% Apply average re-reference +%chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +%chan_m = mean(EEG.data(chan_inds,:),1); +%chan_m_mat = repmat(chan_m,size(EEG.data,1),1); +%EEG.data = EEG.data - chan_m_mat; +%clear chan_m_mat; + +% Window the continuous data +logging_log('INFO', 'Windowing the continous data...'); +EEG = marks_continuous2epochs(EEG,'recurrence',[1],'limits',[[0 1]]); +logging_log('INFO', 'LOADED DATASET...'); + +% rereference to selected channels.. +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +epoch_inds = marks_label2index(EEG.marks.time_info,{'manual'},'indexes','invert','on'); +[EEG,trim_ch_sd]=chan_variance(EEG,'data_field','data', ... + 'chan_inds',chan_inds, ... + 'epoch_inds',epoch_inds, ... + 'plot_figs','off'); + +chan_dist=zeros(size(trim_ch_sd)); +for i=1:size(trim_ch_sd,2); + chan_dist(:,i)=(trim_ch_sd(:,i)-median(trim_ch_sd(:,i)))/diff(quantile(trim_ch_sd(:,i),[.3,.7])); +end +mean_chan_dist=mean(chan_dist,2); +m=median(mean_chan_dist); +q=quantile(mean_chan_dist,[.3,.7]); + +refchans=find(mean_chan_distve_trimmean(msr,40,1)+ve_trimstd(msr,40,1)*6); +logging_log('INFO', 'IDENTIFIED BRIDGED CHANNELS...'); + +% Edit the channel flag info structure +logging_log('INFO', 'Updating chflaginfo structure...'); +lnkflags = zeros(EEG.nbchan,1); +lnkflags(chan_inds(flag_b_chan_inds))=1; +EEG.marks = marks_add_label(EEG.marks,'chan_info', ... + {'bridge',[.7,1,.7],[.2,1,.2],-1,lnkflags}); +logging_log('INFO', 'EDITED CHANFLAGINFO STRUCT...'); + +% Combine low_rand bridge chan_info flags into 'manual'... +EEG = pop_marks_merge_labels(EEG,'chan_info',{'low_r','bridge'},'target_label','manual'); +logging_log('INFO', 'COMBINED MARKS STRUCTURE INTO MANUAL FLAGS...'); + +%% FLAG RANK CHAN +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Flags the channel that is the least unique (the best channel to remove prior +% to ICA in order to account for the rereference rank deficiency. + +logging_log('INFO', 'Updating chflaginfo structure...'); +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +[r_max,rank_ind] = max(data_r_ch(chan_inds)); +rankflags = zeros(EEG.nbchan,1); +rankflags(chan_inds(rank_ind))=1; +EEG.marks = marks_add_label(EEG.marks,'chan_info', ... + {'rank',[.1,.1,.24],[.1,.1,.24],-1,rankflags}); +logging_log('INFO', 'EDITED CHANFLAGINFO STRUCT...'); + +%% REREFERENCE TO INTERPOLATED AVERAGE SITE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Rereference the data to an average interpolated site containined 19 channels +logging_log('INFO', 'Rereferencing to averaged interpolated site...'); +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +EEG = interp_ref(EEG,'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc','chans',chan_inds); +EEG = eeg_checkset(EEG); +logging_log('INFO', 'REREFERENCED TO INTERPOLATED AVERAGE SITE...'); + +%% CALCULATE NEAREST NEIGHBOUR R VALUES +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Similarly to the neighbor r calculation done between channels this section looks +% at the correlation, but between all channels and for epochs of time. Time segmenents +% are flagged for removal. + +logging_log('INFO', 'Calculating nearest neighbour r array for window criteria...'); +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +epoch_inds = marks_label2index(EEG.marks.time_info,{'manual'},'indexes','invert','on'); +[EEG,data_r_t,~,~,~] = chan_neighbour_r(EEG, ... + 3,'max', ... + 'chan_inds',chan_inds, ... + 'epoch_inds',epoch_inds, ... + 'plotfigs','off'); +logging_log('INFO', 'CALCULATED NEAREST NEIGHBOUR R VALUES...'); + +% Create the window criteria vector for flagging low_r time_info... +logging_log('INFO', 'Assessing epoch r distributions criteria...') +[~,flag_r_t_inds]=marks_array2flags(data_r_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_dir','neg', ... + 'init_crit',16, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); +logging_log('INFO', 'CREATED EPOCH CRITERIA VECTOR...'); + +% Edit the time flag info structure +logging_log('INFO', 'Updating latflaginfo structure...'); +lowr_epoch_flags = zeros(size(EEG.data(1,:,:))); +lowr_epoch_flags(1,:,epoch_inds(flag_r_t_inds))=1; +EEG.marks = marks_add_label(EEG.marks,'time_info', ... + {'low_r',[0,1,0],lowr_epoch_flags}); +clear lowr_epoch_flags; +logging_log('INFO', 'TIME TO: UPDATE REJECTION STRUCTURE...'); + +% Combine low_r time_info flags into 'manual'... +EEG = pop_marks_merge_labels(EEG,'time_info',{'low_r'},'target_label','manual'); +logging_log('INFO', 'COMBINED MARKS STRUCTURE INTO MANUAL FLAGS...'); + +% Concatenate epoched data back to continuous data +logging_log('INFO', 'Concatenating windowed data...'); +EEG = marks_epochs2continuous(EEG); +EEG = eeg_checkset(EEG,'eventconsistency'); +logging_log('INFO', 'CONCATENATED THE WINDOWED DATA INTO CONTINUOUS DATA...'); + +EEG=pop_marks_flag_gap(EEG,{'manual'},2000,'mark_gap',[.8,.8,.8],'offsets',[0 0],'ref_point','both'); + +% Combine mark_gap time_info flags into 'manual'... +EEG = pop_marks_merge_labels(EEG,'time_info',{'mark_gap'},'target_label','manual'); +logging_log('INFO', 'COMBINED MARKS STRUCTURE INTO MANUAL FLAGS...'); + +%%%----------------------------------------------------------------------- +%% SAVE sa.set FILE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +logging_log('INFO', 'Saving file: ./sub-003/eeg/sub-003_task-faceFO_sa.set...'); +if ~exist('derivatives/BIDS-Lossless-EEG/./sub-003/eeg','dir'); + mkdir('derivatives/BIDS-Lossless-EEG/./sub-003/eeg'); +end +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_sa.set'); +logging_log('INFO', 'SAVED sa FILE...'); + +%%%----------------------------------------------------------------------- +%% PREPARE FILE FOR AMICA +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Purges the flagged data to make sure it is primed for ICA and creates the amica paramaeter file. + +% Removed flagged channels and time segments +sprintf('%s','Purging flagged channels...\n'); +EEG = pop_marks_select_data(EEG,'channel marks',[],'labels',{'manual','rank'},'remove','on'); +EEG = pop_marks_select_data(EEG,'time marks',[],'labels',{'manual'},'remove','on'); +EEG = eeg_checkset(EEG); +logging_log('INFO', 'TIME TO: PURGE DATA...'); + +% Make sure EEG.data is doubles for use in amica +EEG.data = double(EEG.data); + +% Save the SA purge file +logging_log('INFO',sprintf('%s','Saving file: derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_sa_purge.set...')); +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_sa_purge.set'); + +% Save diagnostic arrays +try OCTAVE_VERSION; + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_data_sd_t.mat','data_sd_t'); + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_data_sd_ch.mat','data_sd_ch'); + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_data_r_ch.mat','data_r_ch'); + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_data_r_t.mat','data_r_t'); +catch + save('derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_data_sd_t.mat','data_sd_t'); + save('derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_data_sd_ch.mat','data_sd_ch'); + save('derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_data_r_ch.mat','data_r_ch'); + save('derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_data_r_t.mat','data_r_t'); +end + +% Save the AMICA parameter file +swapstr = file_strswap('derivatives/BIDS-Lossless-EEG/code/misc/amica15_default.param', ... + '[repstr_fdt_fname]','derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_sa_purge.fdt', ... + '[repstr_outpath]','derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_amicaout_init', ... + '[repstr_nbchan]', num2str(EEG.nbchan), ... + '[repstr_pnts]', sprintf('%12.0f',EEG.pnts), ... + '[repstr_tpp]', '8', ... + '[repstr_pca]', num2str(EEG.nbchan)); +fidparam = fopen('derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_init.param','w'); +fprintf(fidparam,'%s',swapstr); +fclose(fidparam); + +% CREATE THE AMICA OUTPUT FOLDER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Needed for MPI jobs on cluster only in order to prevent a race effect bug. + +[~] = rmdir('derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_amicaout_init','s'); +mkdir derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_amicaout_init; +logging_log('INFO', 'Created Amica Output Folder'); + +logging_log('INFO', 'Scalpart complete'); +logging_log('INFO', 'Scheduler: sbatch'); +print_chan_sample(EEG); diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s01_scalpart-2019-10-01T14-35-11/s01_scalpart_sub_004_task_faceFO_eeg.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s01_scalpart-2019-10-01T14-35-11/s01_scalpart_sub_004_task_faceFO_eeg.m new file mode 100644 index 000000000..ad3823342 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s01_scalpart-2019-10-01T14-35-11/s01_scalpart_sub_004_task_faceFO_eeg.m @@ -0,0 +1,529 @@ +warning off; + +pkg load signal; +pkg load parallel; +pkg load io; + +file_path = fileparts(mfilename('fullpath')); + +addpath([file_path '/../../code/dependencies/eeglab_lossless'], ... + [file_path '/../../code/dependencies/matlog'], ... + [file_path '/../../code/tools']); +cd '/scratch/tk11br/Face13/'; + +% eeglab auto loads the path correctly instead of shotgun blasting with +% addpath(genpath(<>)) as we were doing prior +if ~exist('EEG', 'var') + eeglab; +end +% Use matlog disp function override +logging_override_disp(true); +% Use matlog INFO level debugging +logging_set_log_level('INFO') + +% Don't run with traditional but enable some of the options +save_default_options ('-mat-binary'); +confirm_recursive_rmdir(false); + +% This makes | and & || and && in earlier version of Octave where +% | and & dont short curcuit by default +if exist('do_braindead_shortcircuit_evaluation', 'builtin') + do_braindead_shortcircuit_evaluation(true); +end + +% Options +pop_editoptions('option_savetwofiles', 1,'option_single',0); + +%% SCRIPT DESCRIPTION +% This script features the main preprocessing and cleaning of the data before the ICA process begin. Data is flagged for high standard deveiavtion +% by channel to remove comically bad channels. The data is then rereferenced to the average site, and high/low pass filtered. +% Channel neighbor correlations are then calculated to find odd channels and bridged channels. The outermost channel is also flagged to reduce the rank of the data. +% After another rereference channel correlations are calculated by time epoch and flagged. The file is saved containing the, marks structure, then saved again +% with all of the mentioned flags puregd from the data in order to prime it for ICA. +% +%% From Config key_strswap Description +%----------------------------------------------------------------------------------------------------------------------------- +% in_path = [] Relative path to input data files assuming cd = work_path +% montage_info = [0.500 -22.000 -48.000 -0.065 0.000 -1.580 1060.000 1260.000 1220.000] Montage information to fit channel locations on a standard surface. +% This can be a transformations matrix (e.g. [-.14,-22,-48,-.07,0,-1.57,1080,1260,1240]) +% ... or a a file containing a transformation matrix (e.g. ./sub-004/eeg/sub-004_task-faceFO_eeg.edf_transmat.mat). +% ... or a BIDS channel location tsv file (assuming that during BIDS init the channels were coregistered to the standard surface). +% staging_script = derivatives/BIDS-Lossless-EEG/code/scripts/face13_staging.m Script file path/name that creates marks based on events in the raw data file. +% recur_sec = 1 Recurrence (sec) for artifact detection epoching (e.g. .5, Default 1) +% limit_sec = [0 1] Limits (sec) for artifact detection epoching (e.g. [-.5 0], Default [0 recur_sec]) +% sd_t_meth = na Method used for flagging epochs (e.g. 'q' (quantiles), or 'na' (default)) +% sd_t_vals = [.3 .7] Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7]) +% sd_t_o = 16 z threshold for flagging epochs during standard deviation criteria (e.g. 6) +% sd_t_f_meth = fixed Fixed method used for flagging epochs (should be 'fixed') +% sd_t_f_vals = Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% sd_t_f_o = .2 z threshold for flagging epochs during fixed standard deviation criteria (e.g. .2) +% sd_t_pad = 1 Number of windows to pad onto each side of the ch_sd time flag +% sd_ch_meth = q Method used for flagging channels (e.g. 'q' (quantiles), or 'na' (default)) +% sd_ch_vals = [.3 .7] Percentage trim for confidence intervals during channel standard deviation criteria (e.g. [.3 .7]) +% sd_ch_o = 16 z threshold for flagging channels during standard deviation criteria (e.g. 6) +% sd_ch_f_meth = fixed Fixed method used for flagging channels (should be 'fixed') +% sd_ch_f_vals = Percentage trim for confidence intervals during channel standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% sd_ch_f_o = .2 z threshold for flagging channel during fixed standard deviation criteria (e.g. 6) +% ref_loc_file = derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc Name of file containing the reference locations (including the relative path) +% low_bound_hz = 1 Lower bound of the filter bass-band +% high_bound_hz = 100 Upper bound of the filter bass-band +% save_f_res = 1 1 if you want to save the filter residuals (Default: 1) +% n_nbr_ch = 3 Number of channels to use in nearest neighbour r calculation (for channel criteria) +% r_ch_meth = q Method used for flagging low r channels (e.g. 'q' (quantiles), or 'na' (default)) +% r_ch_vals = [.3,.7] Percentage trim for confidence intervals during low r channel standard deviation criteria (e.g. [.3 .7]) +% r_ch_o = 16 z threshold for flagging channel during neighbour r criteria (e.g. 3) +% r_ch_f_meth = fixed Fixed method used for flagging low r channels (should be 'fixed') +% r_ch_f_vals = Percentage trim for confidence intervals during low r channel standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% r_ch_f_o = .2 z threshold for flagging channel during fixed neighbour r criteria (e.g. 3) +% bridge_trim = 40 Percentage trim for z calculation of bridged channels (e.g. 40 = 20% top and bottom) +% bridge_z = 6 z threshold for flagging channel during bridging criteria (e.g. 8) +% n_nbr_t = 3 Number of channels to use in nearest neighbour r calculation (for epoch criteria) +% r_t_meth = q Method used for flagging low r epochs (e.g. 'q' (quantiles), or 'na' (default)) +% r_t_vals = [.3 .7] Percentage trim for confidence intervals during low r epoch standard deviation criteria (e.g. [.3 .7]) +% r_t_o = 16 z threshold for flagging epochs during neighbour r criteria (e.g. 3) +% r_t_f_meth = fixed Fixed method used for flagging low r epochs (should be 'fixed') +% r_t_f_vals = Percentage trim for confidence intervals during low r epoch standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% r_t_f_o = .2 z threshold for flagging epochs during fixed neighbour r criteria (e.g. 3) +% min_gap_ms = 2000 Minimum time (ms) to allow between periods marked for rejection +% out_path = derivatives/BIDS-Lossless-EEG Relative path to output data files assuming cd = work_path +% amica_param_file = derivatives/BIDS-Lossless-EEG/code/misc/amica15_default.param template amicadefs.param file to modify +% amica_threads_s02 = 8 number of threads to use for running s02 amica script (Default: 8) + +%% CHECK FOR OUTPUT PATH AND CRETAE IF NECESSARY +if ~exist('derivatives/BIDS-Lossless-EEG/./sub-004/eeg','dir'); + disp('Making directory derivatives/BIDS-Lossless-EEG/./sub-004/eeg'); + mkdir derivatives/BIDS-Lossless-EEG/./sub-004/eeg +end + +%% LOAD DATASET +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +EEG = pop_bidsload('./sub-004/eeg/sub-004_task-faceFO_eeg.edf','gui',0); + +if ~isfield(EEG,'marks'); + if isempty(EEG.icaweights) + EEG.marks=marks_init(size(EEG.data)); + else + EEG.marks=marks_init(size(EEG.data),min(size(EEG.icaweights))); + end +end + +EEG.marks = marks_add_label(EEG.marks,'time_info', ... +{'init_ind',[0,0,1],[1:EEG.pnts]}); + +%execute the staging script if specified. +if exist('derivatives/BIDS-Lossless-EEG/code/scripts/face13_staging.m'); + [ssp,ssn,sse]=fileparts('derivatives/BIDS-Lossless-EEG/code/scripts/face13_staging.m'); + addpath(ssp); + eval(ssn); +end + +%add channel coregistration procedure here. +%warp locations to standard head surface: +if ~isempty('[0.500 -22.000 -48.000 -0.065 0.000 -1.580 1060.000 1260.000 1220.000]'); + if isempty(str2num('[0.500 -22.000 -48.000 -0.065 0.000 -1.580 1060.000 1260.000 1220.000]')); + %if it is a BIDS channel tsv, load the tsv,sd_t_f_vals + %else read the file that is assumed to be a transformation matrix. + else + EEG = warp_locs( EEG,'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc', ... + 'transform',[[0.500 -22.000 -48.000 -0.065 0.000 -1.580 1060.000 1260.000 1220.000]], ... + 'manual','off'); + end +end + +% Apply average re-reference +%chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +%chan_m = mean(EEG.data(chan_inds,:),1); +%chan_m_mat = repmat(chan_m,size(EEG.data,1),1); +%EEG.data = EEG.data - chan_m_mat; +%clear chan_m_mat; + +% Window the continuous data +logging_log('INFO', 'Windowing the continous data...'); +EEG = marks_continuous2epochs(EEG,'recurrence',[1],'limits',[[0 1]]); +logging_log('INFO', 'LOADED DATASET...'); + +% rereference to selected channels.. +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +epoch_inds = marks_label2index(EEG.marks.time_info,{'manual'},'indexes','invert','on'); +[EEG,trim_ch_sd]=chan_variance(EEG,'data_field','data', ... + 'chan_inds',chan_inds, ... + 'epoch_inds',epoch_inds, ... + 'plot_figs','off'); + +chan_dist=zeros(size(trim_ch_sd)); +for i=1:size(trim_ch_sd,2); + chan_dist(:,i)=(trim_ch_sd(:,i)-median(trim_ch_sd(:,i)))/diff(quantile(trim_ch_sd(:,i),[.3,.7])); +end +mean_chan_dist=mean(chan_dist,2); +m=median(mean_chan_dist); +q=quantile(mean_chan_dist,[.3,.7]); + +refchans=find(mean_chan_distve_trimmean(msr,40,1)+ve_trimstd(msr,40,1)*6); +logging_log('INFO', 'IDENTIFIED BRIDGED CHANNELS...'); + +% Edit the channel flag info structure +logging_log('INFO', 'Updating chflaginfo structure...'); +lnkflags = zeros(EEG.nbchan,1); +lnkflags(chan_inds(flag_b_chan_inds))=1; +EEG.marks = marks_add_label(EEG.marks,'chan_info', ... + {'bridge',[.7,1,.7],[.2,1,.2],-1,lnkflags}); +logging_log('INFO', 'EDITED CHANFLAGINFO STRUCT...'); + +% Combine low_rand bridge chan_info flags into 'manual'... +EEG = pop_marks_merge_labels(EEG,'chan_info',{'low_r','bridge'},'target_label','manual'); +logging_log('INFO', 'COMBINED MARKS STRUCTURE INTO MANUAL FLAGS...'); + +%% FLAG RANK CHAN +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Flags the channel that is the least unique (the best channel to remove prior +% to ICA in order to account for the rereference rank deficiency. + +logging_log('INFO', 'Updating chflaginfo structure...'); +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +[r_max,rank_ind] = max(data_r_ch(chan_inds)); +rankflags = zeros(EEG.nbchan,1); +rankflags(chan_inds(rank_ind))=1; +EEG.marks = marks_add_label(EEG.marks,'chan_info', ... + {'rank',[.1,.1,.24],[.1,.1,.24],-1,rankflags}); +logging_log('INFO', 'EDITED CHANFLAGINFO STRUCT...'); + +%% REREFERENCE TO INTERPOLATED AVERAGE SITE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Rereference the data to an average interpolated site containined 19 channels +logging_log('INFO', 'Rereferencing to averaged interpolated site...'); +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +EEG = interp_ref(EEG,'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc','chans',chan_inds); +EEG = eeg_checkset(EEG); +logging_log('INFO', 'REREFERENCED TO INTERPOLATED AVERAGE SITE...'); + +%% CALCULATE NEAREST NEIGHBOUR R VALUES +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Similarly to the neighbor r calculation done between channels this section looks +% at the correlation, but between all channels and for epochs of time. Time segmenents +% are flagged for removal. + +logging_log('INFO', 'Calculating nearest neighbour r array for window criteria...'); +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +epoch_inds = marks_label2index(EEG.marks.time_info,{'manual'},'indexes','invert','on'); +[EEG,data_r_t,~,~,~] = chan_neighbour_r(EEG, ... + 3,'max', ... + 'chan_inds',chan_inds, ... + 'epoch_inds',epoch_inds, ... + 'plotfigs','off'); +logging_log('INFO', 'CALCULATED NEAREST NEIGHBOUR R VALUES...'); + +% Create the window criteria vector for flagging low_r time_info... +logging_log('INFO', 'Assessing epoch r distributions criteria...') +[~,flag_r_t_inds]=marks_array2flags(data_r_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_dir','neg', ... + 'init_crit',16, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); +logging_log('INFO', 'CREATED EPOCH CRITERIA VECTOR...'); + +% Edit the time flag info structure +logging_log('INFO', 'Updating latflaginfo structure...'); +lowr_epoch_flags = zeros(size(EEG.data(1,:,:))); +lowr_epoch_flags(1,:,epoch_inds(flag_r_t_inds))=1; +EEG.marks = marks_add_label(EEG.marks,'time_info', ... + {'low_r',[0,1,0],lowr_epoch_flags}); +clear lowr_epoch_flags; +logging_log('INFO', 'TIME TO: UPDATE REJECTION STRUCTURE...'); + +% Combine low_r time_info flags into 'manual'... +EEG = pop_marks_merge_labels(EEG,'time_info',{'low_r'},'target_label','manual'); +logging_log('INFO', 'COMBINED MARKS STRUCTURE INTO MANUAL FLAGS...'); + +% Concatenate epoched data back to continuous data +logging_log('INFO', 'Concatenating windowed data...'); +EEG = marks_epochs2continuous(EEG); +EEG = eeg_checkset(EEG,'eventconsistency'); +logging_log('INFO', 'CONCATENATED THE WINDOWED DATA INTO CONTINUOUS DATA...'); + +EEG=pop_marks_flag_gap(EEG,{'manual'},2000,'mark_gap',[.8,.8,.8],'offsets',[0 0],'ref_point','both'); + +% Combine mark_gap time_info flags into 'manual'... +EEG = pop_marks_merge_labels(EEG,'time_info',{'mark_gap'},'target_label','manual'); +logging_log('INFO', 'COMBINED MARKS STRUCTURE INTO MANUAL FLAGS...'); + +%%%----------------------------------------------------------------------- +%% SAVE sa.set FILE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +logging_log('INFO', 'Saving file: ./sub-004/eeg/sub-004_task-faceFO_sa.set...'); +if ~exist('derivatives/BIDS-Lossless-EEG/./sub-004/eeg','dir'); + mkdir('derivatives/BIDS-Lossless-EEG/./sub-004/eeg'); +end +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_sa.set'); +logging_log('INFO', 'SAVED sa FILE...'); + +%%%----------------------------------------------------------------------- +%% PREPARE FILE FOR AMICA +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Purges the flagged data to make sure it is primed for ICA and creates the amica paramaeter file. + +% Removed flagged channels and time segments +sprintf('%s','Purging flagged channels...\n'); +EEG = pop_marks_select_data(EEG,'channel marks',[],'labels',{'manual','rank'},'remove','on'); +EEG = pop_marks_select_data(EEG,'time marks',[],'labels',{'manual'},'remove','on'); +EEG = eeg_checkset(EEG); +logging_log('INFO', 'TIME TO: PURGE DATA...'); + +% Make sure EEG.data is doubles for use in amica +EEG.data = double(EEG.data); + +% Save the SA purge file +logging_log('INFO',sprintf('%s','Saving file: derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_sa_purge.set...')); +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_sa_purge.set'); + +% Save diagnostic arrays +try OCTAVE_VERSION; + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_data_sd_t.mat','data_sd_t'); + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_data_sd_ch.mat','data_sd_ch'); + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_data_r_ch.mat','data_r_ch'); + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_data_r_t.mat','data_r_t'); +catch + save('derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_data_sd_t.mat','data_sd_t'); + save('derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_data_sd_ch.mat','data_sd_ch'); + save('derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_data_r_ch.mat','data_r_ch'); + save('derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_data_r_t.mat','data_r_t'); +end + +% Save the AMICA parameter file +swapstr = file_strswap('derivatives/BIDS-Lossless-EEG/code/misc/amica15_default.param', ... + '[repstr_fdt_fname]','derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_sa_purge.fdt', ... + '[repstr_outpath]','derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_amicaout_init', ... + '[repstr_nbchan]', num2str(EEG.nbchan), ... + '[repstr_pnts]', sprintf('%12.0f',EEG.pnts), ... + '[repstr_tpp]', '8', ... + '[repstr_pca]', num2str(EEG.nbchan)); +fidparam = fopen('derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_init.param','w'); +fprintf(fidparam,'%s',swapstr); +fclose(fidparam); + +% CREATE THE AMICA OUTPUT FOLDER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Needed for MPI jobs on cluster only in order to prevent a race effect bug. + +[~] = rmdir('derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_amicaout_init','s'); +mkdir derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_amicaout_init; +logging_log('INFO', 'Created Amica Output Folder'); + +logging_log('INFO', 'Scalpart complete'); +logging_log('INFO', 'Scheduler: sbatch'); +print_chan_sample(EEG); diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s01_scalpart-2019-10-01T14-35-11/s01_scalpart_sub_005_task_faceFO_eeg.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s01_scalpart-2019-10-01T14-35-11/s01_scalpart_sub_005_task_faceFO_eeg.m new file mode 100644 index 000000000..d4321b064 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s01_scalpart-2019-10-01T14-35-11/s01_scalpart_sub_005_task_faceFO_eeg.m @@ -0,0 +1,529 @@ +warning off; + +pkg load signal; +pkg load parallel; +pkg load io; + +file_path = fileparts(mfilename('fullpath')); + +addpath([file_path '/../../code/dependencies/eeglab_lossless'], ... + [file_path '/../../code/dependencies/matlog'], ... + [file_path '/../../code/tools']); +cd '/scratch/tk11br/Face13/'; + +% eeglab auto loads the path correctly instead of shotgun blasting with +% addpath(genpath(<>)) as we were doing prior +if ~exist('EEG', 'var') + eeglab; +end +% Use matlog disp function override +logging_override_disp(true); +% Use matlog INFO level debugging +logging_set_log_level('INFO') + +% Don't run with traditional but enable some of the options +save_default_options ('-mat-binary'); +confirm_recursive_rmdir(false); + +% This makes | and & || and && in earlier version of Octave where +% | and & dont short curcuit by default +if exist('do_braindead_shortcircuit_evaluation', 'builtin') + do_braindead_shortcircuit_evaluation(true); +end + +% Options +pop_editoptions('option_savetwofiles', 1,'option_single',0); + +%% SCRIPT DESCRIPTION +% This script features the main preprocessing and cleaning of the data before the ICA process begin. Data is flagged for high standard deveiavtion +% by channel to remove comically bad channels. The data is then rereferenced to the average site, and high/low pass filtered. +% Channel neighbor correlations are then calculated to find odd channels and bridged channels. The outermost channel is also flagged to reduce the rank of the data. +% After another rereference channel correlations are calculated by time epoch and flagged. The file is saved containing the, marks structure, then saved again +% with all of the mentioned flags puregd from the data in order to prime it for ICA. +% +%% From Config key_strswap Description +%----------------------------------------------------------------------------------------------------------------------------- +% in_path = [] Relative path to input data files assuming cd = work_path +% montage_info = [0.500 -22.000 -48.000 -0.065 0.000 -1.580 1060.000 1260.000 1220.000] Montage information to fit channel locations on a standard surface. +% This can be a transformations matrix (e.g. [-.14,-22,-48,-.07,0,-1.57,1080,1260,1240]) +% ... or a a file containing a transformation matrix (e.g. ./sub-005/eeg/sub-005_task-faceFO_eeg.edf_transmat.mat). +% ... or a BIDS channel location tsv file (assuming that during BIDS init the channels were coregistered to the standard surface). +% staging_script = derivatives/BIDS-Lossless-EEG/code/scripts/face13_staging.m Script file path/name that creates marks based on events in the raw data file. +% recur_sec = 1 Recurrence (sec) for artifact detection epoching (e.g. .5, Default 1) +% limit_sec = [0 1] Limits (sec) for artifact detection epoching (e.g. [-.5 0], Default [0 recur_sec]) +% sd_t_meth = na Method used for flagging epochs (e.g. 'q' (quantiles), or 'na' (default)) +% sd_t_vals = [.3 .7] Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7]) +% sd_t_o = 16 z threshold for flagging epochs during standard deviation criteria (e.g. 6) +% sd_t_f_meth = fixed Fixed method used for flagging epochs (should be 'fixed') +% sd_t_f_vals = Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% sd_t_f_o = .2 z threshold for flagging epochs during fixed standard deviation criteria (e.g. .2) +% sd_t_pad = 1 Number of windows to pad onto each side of the ch_sd time flag +% sd_ch_meth = q Method used for flagging channels (e.g. 'q' (quantiles), or 'na' (default)) +% sd_ch_vals = [.3 .7] Percentage trim for confidence intervals during channel standard deviation criteria (e.g. [.3 .7]) +% sd_ch_o = 16 z threshold for flagging channels during standard deviation criteria (e.g. 6) +% sd_ch_f_meth = fixed Fixed method used for flagging channels (should be 'fixed') +% sd_ch_f_vals = Percentage trim for confidence intervals during channel standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% sd_ch_f_o = .2 z threshold for flagging channel during fixed standard deviation criteria (e.g. 6) +% ref_loc_file = derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc Name of file containing the reference locations (including the relative path) +% low_bound_hz = 1 Lower bound of the filter bass-band +% high_bound_hz = 100 Upper bound of the filter bass-band +% save_f_res = 1 1 if you want to save the filter residuals (Default: 1) +% n_nbr_ch = 3 Number of channels to use in nearest neighbour r calculation (for channel criteria) +% r_ch_meth = q Method used for flagging low r channels (e.g. 'q' (quantiles), or 'na' (default)) +% r_ch_vals = [.3,.7] Percentage trim for confidence intervals during low r channel standard deviation criteria (e.g. [.3 .7]) +% r_ch_o = 16 z threshold for flagging channel during neighbour r criteria (e.g. 3) +% r_ch_f_meth = fixed Fixed method used for flagging low r channels (should be 'fixed') +% r_ch_f_vals = Percentage trim for confidence intervals during low r channel standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% r_ch_f_o = .2 z threshold for flagging channel during fixed neighbour r criteria (e.g. 3) +% bridge_trim = 40 Percentage trim for z calculation of bridged channels (e.g. 40 = 20% top and bottom) +% bridge_z = 6 z threshold for flagging channel during bridging criteria (e.g. 8) +% n_nbr_t = 3 Number of channels to use in nearest neighbour r calculation (for epoch criteria) +% r_t_meth = q Method used for flagging low r epochs (e.g. 'q' (quantiles), or 'na' (default)) +% r_t_vals = [.3 .7] Percentage trim for confidence intervals during low r epoch standard deviation criteria (e.g. [.3 .7]) +% r_t_o = 16 z threshold for flagging epochs during neighbour r criteria (e.g. 3) +% r_t_f_meth = fixed Fixed method used for flagging low r epochs (should be 'fixed') +% r_t_f_vals = Percentage trim for confidence intervals during low r epoch standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% r_t_f_o = .2 z threshold for flagging epochs during fixed neighbour r criteria (e.g. 3) +% min_gap_ms = 2000 Minimum time (ms) to allow between periods marked for rejection +% out_path = derivatives/BIDS-Lossless-EEG Relative path to output data files assuming cd = work_path +% amica_param_file = derivatives/BIDS-Lossless-EEG/code/misc/amica15_default.param template amicadefs.param file to modify +% amica_threads_s02 = 8 number of threads to use for running s02 amica script (Default: 8) + +%% CHECK FOR OUTPUT PATH AND CRETAE IF NECESSARY +if ~exist('derivatives/BIDS-Lossless-EEG/./sub-005/eeg','dir'); + disp('Making directory derivatives/BIDS-Lossless-EEG/./sub-005/eeg'); + mkdir derivatives/BIDS-Lossless-EEG/./sub-005/eeg +end + +%% LOAD DATASET +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +EEG = pop_bidsload('./sub-005/eeg/sub-005_task-faceFO_eeg.edf','gui',0); + +if ~isfield(EEG,'marks'); + if isempty(EEG.icaweights) + EEG.marks=marks_init(size(EEG.data)); + else + EEG.marks=marks_init(size(EEG.data),min(size(EEG.icaweights))); + end +end + +EEG.marks = marks_add_label(EEG.marks,'time_info', ... +{'init_ind',[0,0,1],[1:EEG.pnts]}); + +%execute the staging script if specified. +if exist('derivatives/BIDS-Lossless-EEG/code/scripts/face13_staging.m'); + [ssp,ssn,sse]=fileparts('derivatives/BIDS-Lossless-EEG/code/scripts/face13_staging.m'); + addpath(ssp); + eval(ssn); +end + +%add channel coregistration procedure here. +%warp locations to standard head surface: +if ~isempty('[0.500 -22.000 -48.000 -0.065 0.000 -1.580 1060.000 1260.000 1220.000]'); + if isempty(str2num('[0.500 -22.000 -48.000 -0.065 0.000 -1.580 1060.000 1260.000 1220.000]')); + %if it is a BIDS channel tsv, load the tsv,sd_t_f_vals + %else read the file that is assumed to be a transformation matrix. + else + EEG = warp_locs( EEG,'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc', ... + 'transform',[[0.500 -22.000 -48.000 -0.065 0.000 -1.580 1060.000 1260.000 1220.000]], ... + 'manual','off'); + end +end + +% Apply average re-reference +%chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +%chan_m = mean(EEG.data(chan_inds,:),1); +%chan_m_mat = repmat(chan_m,size(EEG.data,1),1); +%EEG.data = EEG.data - chan_m_mat; +%clear chan_m_mat; + +% Window the continuous data +logging_log('INFO', 'Windowing the continous data...'); +EEG = marks_continuous2epochs(EEG,'recurrence',[1],'limits',[[0 1]]); +logging_log('INFO', 'LOADED DATASET...'); + +% rereference to selected channels.. +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +epoch_inds = marks_label2index(EEG.marks.time_info,{'manual'},'indexes','invert','on'); +[EEG,trim_ch_sd]=chan_variance(EEG,'data_field','data', ... + 'chan_inds',chan_inds, ... + 'epoch_inds',epoch_inds, ... + 'plot_figs','off'); + +chan_dist=zeros(size(trim_ch_sd)); +for i=1:size(trim_ch_sd,2); + chan_dist(:,i)=(trim_ch_sd(:,i)-median(trim_ch_sd(:,i)))/diff(quantile(trim_ch_sd(:,i),[.3,.7])); +end +mean_chan_dist=mean(chan_dist,2); +m=median(mean_chan_dist); +q=quantile(mean_chan_dist,[.3,.7]); + +refchans=find(mean_chan_distve_trimmean(msr,40,1)+ve_trimstd(msr,40,1)*6); +logging_log('INFO', 'IDENTIFIED BRIDGED CHANNELS...'); + +% Edit the channel flag info structure +logging_log('INFO', 'Updating chflaginfo structure...'); +lnkflags = zeros(EEG.nbchan,1); +lnkflags(chan_inds(flag_b_chan_inds))=1; +EEG.marks = marks_add_label(EEG.marks,'chan_info', ... + {'bridge',[.7,1,.7],[.2,1,.2],-1,lnkflags}); +logging_log('INFO', 'EDITED CHANFLAGINFO STRUCT...'); + +% Combine low_rand bridge chan_info flags into 'manual'... +EEG = pop_marks_merge_labels(EEG,'chan_info',{'low_r','bridge'},'target_label','manual'); +logging_log('INFO', 'COMBINED MARKS STRUCTURE INTO MANUAL FLAGS...'); + +%% FLAG RANK CHAN +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Flags the channel that is the least unique (the best channel to remove prior +% to ICA in order to account for the rereference rank deficiency. + +logging_log('INFO', 'Updating chflaginfo structure...'); +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +[r_max,rank_ind] = max(data_r_ch(chan_inds)); +rankflags = zeros(EEG.nbchan,1); +rankflags(chan_inds(rank_ind))=1; +EEG.marks = marks_add_label(EEG.marks,'chan_info', ... + {'rank',[.1,.1,.24],[.1,.1,.24],-1,rankflags}); +logging_log('INFO', 'EDITED CHANFLAGINFO STRUCT...'); + +%% REREFERENCE TO INTERPOLATED AVERAGE SITE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Rereference the data to an average interpolated site containined 19 channels +logging_log('INFO', 'Rereferencing to averaged interpolated site...'); +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +EEG = interp_ref(EEG,'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc','chans',chan_inds); +EEG = eeg_checkset(EEG); +logging_log('INFO', 'REREFERENCED TO INTERPOLATED AVERAGE SITE...'); + +%% CALCULATE NEAREST NEIGHBOUR R VALUES +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Similarly to the neighbor r calculation done between channels this section looks +% at the correlation, but between all channels and for epochs of time. Time segmenents +% are flagged for removal. + +logging_log('INFO', 'Calculating nearest neighbour r array for window criteria...'); +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +epoch_inds = marks_label2index(EEG.marks.time_info,{'manual'},'indexes','invert','on'); +[EEG,data_r_t,~,~,~] = chan_neighbour_r(EEG, ... + 3,'max', ... + 'chan_inds',chan_inds, ... + 'epoch_inds',epoch_inds, ... + 'plotfigs','off'); +logging_log('INFO', 'CALCULATED NEAREST NEIGHBOUR R VALUES...'); + +% Create the window criteria vector for flagging low_r time_info... +logging_log('INFO', 'Assessing epoch r distributions criteria...') +[~,flag_r_t_inds]=marks_array2flags(data_r_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_dir','neg', ... + 'init_crit',16, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); +logging_log('INFO', 'CREATED EPOCH CRITERIA VECTOR...'); + +% Edit the time flag info structure +logging_log('INFO', 'Updating latflaginfo structure...'); +lowr_epoch_flags = zeros(size(EEG.data(1,:,:))); +lowr_epoch_flags(1,:,epoch_inds(flag_r_t_inds))=1; +EEG.marks = marks_add_label(EEG.marks,'time_info', ... + {'low_r',[0,1,0],lowr_epoch_flags}); +clear lowr_epoch_flags; +logging_log('INFO', 'TIME TO: UPDATE REJECTION STRUCTURE...'); + +% Combine low_r time_info flags into 'manual'... +EEG = pop_marks_merge_labels(EEG,'time_info',{'low_r'},'target_label','manual'); +logging_log('INFO', 'COMBINED MARKS STRUCTURE INTO MANUAL FLAGS...'); + +% Concatenate epoched data back to continuous data +logging_log('INFO', 'Concatenating windowed data...'); +EEG = marks_epochs2continuous(EEG); +EEG = eeg_checkset(EEG,'eventconsistency'); +logging_log('INFO', 'CONCATENATED THE WINDOWED DATA INTO CONTINUOUS DATA...'); + +EEG=pop_marks_flag_gap(EEG,{'manual'},2000,'mark_gap',[.8,.8,.8],'offsets',[0 0],'ref_point','both'); + +% Combine mark_gap time_info flags into 'manual'... +EEG = pop_marks_merge_labels(EEG,'time_info',{'mark_gap'},'target_label','manual'); +logging_log('INFO', 'COMBINED MARKS STRUCTURE INTO MANUAL FLAGS...'); + +%%%----------------------------------------------------------------------- +%% SAVE sa.set FILE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +logging_log('INFO', 'Saving file: ./sub-005/eeg/sub-005_task-faceFO_sa.set...'); +if ~exist('derivatives/BIDS-Lossless-EEG/./sub-005/eeg','dir'); + mkdir('derivatives/BIDS-Lossless-EEG/./sub-005/eeg'); +end +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_sa.set'); +logging_log('INFO', 'SAVED sa FILE...'); + +%%%----------------------------------------------------------------------- +%% PREPARE FILE FOR AMICA +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Purges the flagged data to make sure it is primed for ICA and creates the amica paramaeter file. + +% Removed flagged channels and time segments +sprintf('%s','Purging flagged channels...\n'); +EEG = pop_marks_select_data(EEG,'channel marks',[],'labels',{'manual','rank'},'remove','on'); +EEG = pop_marks_select_data(EEG,'time marks',[],'labels',{'manual'},'remove','on'); +EEG = eeg_checkset(EEG); +logging_log('INFO', 'TIME TO: PURGE DATA...'); + +% Make sure EEG.data is doubles for use in amica +EEG.data = double(EEG.data); + +% Save the SA purge file +logging_log('INFO',sprintf('%s','Saving file: derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_sa_purge.set...')); +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_sa_purge.set'); + +% Save diagnostic arrays +try OCTAVE_VERSION; + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_data_sd_t.mat','data_sd_t'); + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_data_sd_ch.mat','data_sd_ch'); + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_data_r_ch.mat','data_r_ch'); + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_data_r_t.mat','data_r_t'); +catch + save('derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_data_sd_t.mat','data_sd_t'); + save('derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_data_sd_ch.mat','data_sd_ch'); + save('derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_data_r_ch.mat','data_r_ch'); + save('derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_data_r_t.mat','data_r_t'); +end + +% Save the AMICA parameter file +swapstr = file_strswap('derivatives/BIDS-Lossless-EEG/code/misc/amica15_default.param', ... + '[repstr_fdt_fname]','derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_sa_purge.fdt', ... + '[repstr_outpath]','derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_amicaout_init', ... + '[repstr_nbchan]', num2str(EEG.nbchan), ... + '[repstr_pnts]', sprintf('%12.0f',EEG.pnts), ... + '[repstr_tpp]', '8', ... + '[repstr_pca]', num2str(EEG.nbchan)); +fidparam = fopen('derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_init.param','w'); +fprintf(fidparam,'%s',swapstr); +fclose(fidparam); + +% CREATE THE AMICA OUTPUT FOLDER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Needed for MPI jobs on cluster only in order to prevent a race effect bug. + +[~] = rmdir('derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_amicaout_init','s'); +mkdir derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_amicaout_init; +logging_log('INFO', 'Created Amica Output Folder'); + +logging_log('INFO', 'Scalpart complete'); +logging_log('INFO', 'Scheduler: sbatch'); +print_chan_sample(EEG); diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s01_scalpart-2019-10-01T14-35-11/s01_scalpart_sub_006_task_faceFO_eeg.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s01_scalpart-2019-10-01T14-35-11/s01_scalpart_sub_006_task_faceFO_eeg.m new file mode 100644 index 000000000..ddc44f2dd --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s01_scalpart-2019-10-01T14-35-11/s01_scalpart_sub_006_task_faceFO_eeg.m @@ -0,0 +1,529 @@ +warning off; + +pkg load signal; +pkg load parallel; +pkg load io; + +file_path = fileparts(mfilename('fullpath')); + +addpath([file_path '/../../code/dependencies/eeglab_lossless'], ... + [file_path '/../../code/dependencies/matlog'], ... + [file_path '/../../code/tools']); +cd '/scratch/tk11br/Face13/'; + +% eeglab auto loads the path correctly instead of shotgun blasting with +% addpath(genpath(<>)) as we were doing prior +if ~exist('EEG', 'var') + eeglab; +end +% Use matlog disp function override +logging_override_disp(true); +% Use matlog INFO level debugging +logging_set_log_level('INFO') + +% Don't run with traditional but enable some of the options +save_default_options ('-mat-binary'); +confirm_recursive_rmdir(false); + +% This makes | and & || and && in earlier version of Octave where +% | and & dont short curcuit by default +if exist('do_braindead_shortcircuit_evaluation', 'builtin') + do_braindead_shortcircuit_evaluation(true); +end + +% Options +pop_editoptions('option_savetwofiles', 1,'option_single',0); + +%% SCRIPT DESCRIPTION +% This script features the main preprocessing and cleaning of the data before the ICA process begin. Data is flagged for high standard deveiavtion +% by channel to remove comically bad channels. The data is then rereferenced to the average site, and high/low pass filtered. +% Channel neighbor correlations are then calculated to find odd channels and bridged channels. The outermost channel is also flagged to reduce the rank of the data. +% After another rereference channel correlations are calculated by time epoch and flagged. The file is saved containing the, marks structure, then saved again +% with all of the mentioned flags puregd from the data in order to prime it for ICA. +% +%% From Config key_strswap Description +%----------------------------------------------------------------------------------------------------------------------------- +% in_path = [] Relative path to input data files assuming cd = work_path +% montage_info = [0.500 -22.000 -48.000 -0.065 0.000 -1.580 1060.000 1260.000 1220.000] Montage information to fit channel locations on a standard surface. +% This can be a transformations matrix (e.g. [-.14,-22,-48,-.07,0,-1.57,1080,1260,1240]) +% ... or a a file containing a transformation matrix (e.g. ./sub-006/eeg/sub-006_task-faceFO_eeg.edf_transmat.mat). +% ... or a BIDS channel location tsv file (assuming that during BIDS init the channels were coregistered to the standard surface). +% staging_script = derivatives/BIDS-Lossless-EEG/code/scripts/face13_staging.m Script file path/name that creates marks based on events in the raw data file. +% recur_sec = 1 Recurrence (sec) for artifact detection epoching (e.g. .5, Default 1) +% limit_sec = [0 1] Limits (sec) for artifact detection epoching (e.g. [-.5 0], Default [0 recur_sec]) +% sd_t_meth = na Method used for flagging epochs (e.g. 'q' (quantiles), or 'na' (default)) +% sd_t_vals = [.3 .7] Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7]) +% sd_t_o = 16 z threshold for flagging epochs during standard deviation criteria (e.g. 6) +% sd_t_f_meth = fixed Fixed method used for flagging epochs (should be 'fixed') +% sd_t_f_vals = Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% sd_t_f_o = .2 z threshold for flagging epochs during fixed standard deviation criteria (e.g. .2) +% sd_t_pad = 1 Number of windows to pad onto each side of the ch_sd time flag +% sd_ch_meth = q Method used for flagging channels (e.g. 'q' (quantiles), or 'na' (default)) +% sd_ch_vals = [.3 .7] Percentage trim for confidence intervals during channel standard deviation criteria (e.g. [.3 .7]) +% sd_ch_o = 16 z threshold for flagging channels during standard deviation criteria (e.g. 6) +% sd_ch_f_meth = fixed Fixed method used for flagging channels (should be 'fixed') +% sd_ch_f_vals = Percentage trim for confidence intervals during channel standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% sd_ch_f_o = .2 z threshold for flagging channel during fixed standard deviation criteria (e.g. 6) +% ref_loc_file = derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc Name of file containing the reference locations (including the relative path) +% low_bound_hz = 1 Lower bound of the filter bass-band +% high_bound_hz = 100 Upper bound of the filter bass-band +% save_f_res = 1 1 if you want to save the filter residuals (Default: 1) +% n_nbr_ch = 3 Number of channels to use in nearest neighbour r calculation (for channel criteria) +% r_ch_meth = q Method used for flagging low r channels (e.g. 'q' (quantiles), or 'na' (default)) +% r_ch_vals = [.3,.7] Percentage trim for confidence intervals during low r channel standard deviation criteria (e.g. [.3 .7]) +% r_ch_o = 16 z threshold for flagging channel during neighbour r criteria (e.g. 3) +% r_ch_f_meth = fixed Fixed method used for flagging low r channels (should be 'fixed') +% r_ch_f_vals = Percentage trim for confidence intervals during low r channel standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% r_ch_f_o = .2 z threshold for flagging channel during fixed neighbour r criteria (e.g. 3) +% bridge_trim = 40 Percentage trim for z calculation of bridged channels (e.g. 40 = 20% top and bottom) +% bridge_z = 6 z threshold for flagging channel during bridging criteria (e.g. 8) +% n_nbr_t = 3 Number of channels to use in nearest neighbour r calculation (for epoch criteria) +% r_t_meth = q Method used for flagging low r epochs (e.g. 'q' (quantiles), or 'na' (default)) +% r_t_vals = [.3 .7] Percentage trim for confidence intervals during low r epoch standard deviation criteria (e.g. [.3 .7]) +% r_t_o = 16 z threshold for flagging epochs during neighbour r criteria (e.g. 3) +% r_t_f_meth = fixed Fixed method used for flagging low r epochs (should be 'fixed') +% r_t_f_vals = Percentage trim for confidence intervals during low r epoch standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% r_t_f_o = .2 z threshold for flagging epochs during fixed neighbour r criteria (e.g. 3) +% min_gap_ms = 2000 Minimum time (ms) to allow between periods marked for rejection +% out_path = derivatives/BIDS-Lossless-EEG Relative path to output data files assuming cd = work_path +% amica_param_file = derivatives/BIDS-Lossless-EEG/code/misc/amica15_default.param template amicadefs.param file to modify +% amica_threads_s02 = 8 number of threads to use for running s02 amica script (Default: 8) + +%% CHECK FOR OUTPUT PATH AND CRETAE IF NECESSARY +if ~exist('derivatives/BIDS-Lossless-EEG/./sub-006/eeg','dir'); + disp('Making directory derivatives/BIDS-Lossless-EEG/./sub-006/eeg'); + mkdir derivatives/BIDS-Lossless-EEG/./sub-006/eeg +end + +%% LOAD DATASET +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +EEG = pop_bidsload('./sub-006/eeg/sub-006_task-faceFO_eeg.edf','gui',0); + +if ~isfield(EEG,'marks'); + if isempty(EEG.icaweights) + EEG.marks=marks_init(size(EEG.data)); + else + EEG.marks=marks_init(size(EEG.data),min(size(EEG.icaweights))); + end +end + +EEG.marks = marks_add_label(EEG.marks,'time_info', ... +{'init_ind',[0,0,1],[1:EEG.pnts]}); + +%execute the staging script if specified. +if exist('derivatives/BIDS-Lossless-EEG/code/scripts/face13_staging.m'); + [ssp,ssn,sse]=fileparts('derivatives/BIDS-Lossless-EEG/code/scripts/face13_staging.m'); + addpath(ssp); + eval(ssn); +end + +%add channel coregistration procedure here. +%warp locations to standard head surface: +if ~isempty('[0.500 -22.000 -48.000 -0.065 0.000 -1.580 1060.000 1260.000 1220.000]'); + if isempty(str2num('[0.500 -22.000 -48.000 -0.065 0.000 -1.580 1060.000 1260.000 1220.000]')); + %if it is a BIDS channel tsv, load the tsv,sd_t_f_vals + %else read the file that is assumed to be a transformation matrix. + else + EEG = warp_locs( EEG,'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc', ... + 'transform',[[0.500 -22.000 -48.000 -0.065 0.000 -1.580 1060.000 1260.000 1220.000]], ... + 'manual','off'); + end +end + +% Apply average re-reference +%chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +%chan_m = mean(EEG.data(chan_inds,:),1); +%chan_m_mat = repmat(chan_m,size(EEG.data,1),1); +%EEG.data = EEG.data - chan_m_mat; +%clear chan_m_mat; + +% Window the continuous data +logging_log('INFO', 'Windowing the continous data...'); +EEG = marks_continuous2epochs(EEG,'recurrence',[1],'limits',[[0 1]]); +logging_log('INFO', 'LOADED DATASET...'); + +% rereference to selected channels.. +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +epoch_inds = marks_label2index(EEG.marks.time_info,{'manual'},'indexes','invert','on'); +[EEG,trim_ch_sd]=chan_variance(EEG,'data_field','data', ... + 'chan_inds',chan_inds, ... + 'epoch_inds',epoch_inds, ... + 'plot_figs','off'); + +chan_dist=zeros(size(trim_ch_sd)); +for i=1:size(trim_ch_sd,2); + chan_dist(:,i)=(trim_ch_sd(:,i)-median(trim_ch_sd(:,i)))/diff(quantile(trim_ch_sd(:,i),[.3,.7])); +end +mean_chan_dist=mean(chan_dist,2); +m=median(mean_chan_dist); +q=quantile(mean_chan_dist,[.3,.7]); + +refchans=find(mean_chan_distve_trimmean(msr,40,1)+ve_trimstd(msr,40,1)*6); +logging_log('INFO', 'IDENTIFIED BRIDGED CHANNELS...'); + +% Edit the channel flag info structure +logging_log('INFO', 'Updating chflaginfo structure...'); +lnkflags = zeros(EEG.nbchan,1); +lnkflags(chan_inds(flag_b_chan_inds))=1; +EEG.marks = marks_add_label(EEG.marks,'chan_info', ... + {'bridge',[.7,1,.7],[.2,1,.2],-1,lnkflags}); +logging_log('INFO', 'EDITED CHANFLAGINFO STRUCT...'); + +% Combine low_rand bridge chan_info flags into 'manual'... +EEG = pop_marks_merge_labels(EEG,'chan_info',{'low_r','bridge'},'target_label','manual'); +logging_log('INFO', 'COMBINED MARKS STRUCTURE INTO MANUAL FLAGS...'); + +%% FLAG RANK CHAN +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Flags the channel that is the least unique (the best channel to remove prior +% to ICA in order to account for the rereference rank deficiency. + +logging_log('INFO', 'Updating chflaginfo structure...'); +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +[r_max,rank_ind] = max(data_r_ch(chan_inds)); +rankflags = zeros(EEG.nbchan,1); +rankflags(chan_inds(rank_ind))=1; +EEG.marks = marks_add_label(EEG.marks,'chan_info', ... + {'rank',[.1,.1,.24],[.1,.1,.24],-1,rankflags}); +logging_log('INFO', 'EDITED CHANFLAGINFO STRUCT...'); + +%% REREFERENCE TO INTERPOLATED AVERAGE SITE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Rereference the data to an average interpolated site containined 19 channels +logging_log('INFO', 'Rereferencing to averaged interpolated site...'); +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +EEG = interp_ref(EEG,'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc','chans',chan_inds); +EEG = eeg_checkset(EEG); +logging_log('INFO', 'REREFERENCED TO INTERPOLATED AVERAGE SITE...'); + +%% CALCULATE NEAREST NEIGHBOUR R VALUES +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Similarly to the neighbor r calculation done between channels this section looks +% at the correlation, but between all channels and for epochs of time. Time segmenents +% are flagged for removal. + +logging_log('INFO', 'Calculating nearest neighbour r array for window criteria...'); +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +epoch_inds = marks_label2index(EEG.marks.time_info,{'manual'},'indexes','invert','on'); +[EEG,data_r_t,~,~,~] = chan_neighbour_r(EEG, ... + 3,'max', ... + 'chan_inds',chan_inds, ... + 'epoch_inds',epoch_inds, ... + 'plotfigs','off'); +logging_log('INFO', 'CALCULATED NEAREST NEIGHBOUR R VALUES...'); + +% Create the window criteria vector for flagging low_r time_info... +logging_log('INFO', 'Assessing epoch r distributions criteria...') +[~,flag_r_t_inds]=marks_array2flags(data_r_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_dir','neg', ... + 'init_crit',16, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); +logging_log('INFO', 'CREATED EPOCH CRITERIA VECTOR...'); + +% Edit the time flag info structure +logging_log('INFO', 'Updating latflaginfo structure...'); +lowr_epoch_flags = zeros(size(EEG.data(1,:,:))); +lowr_epoch_flags(1,:,epoch_inds(flag_r_t_inds))=1; +EEG.marks = marks_add_label(EEG.marks,'time_info', ... + {'low_r',[0,1,0],lowr_epoch_flags}); +clear lowr_epoch_flags; +logging_log('INFO', 'TIME TO: UPDATE REJECTION STRUCTURE...'); + +% Combine low_r time_info flags into 'manual'... +EEG = pop_marks_merge_labels(EEG,'time_info',{'low_r'},'target_label','manual'); +logging_log('INFO', 'COMBINED MARKS STRUCTURE INTO MANUAL FLAGS...'); + +% Concatenate epoched data back to continuous data +logging_log('INFO', 'Concatenating windowed data...'); +EEG = marks_epochs2continuous(EEG); +EEG = eeg_checkset(EEG,'eventconsistency'); +logging_log('INFO', 'CONCATENATED THE WINDOWED DATA INTO CONTINUOUS DATA...'); + +EEG=pop_marks_flag_gap(EEG,{'manual'},2000,'mark_gap',[.8,.8,.8],'offsets',[0 0],'ref_point','both'); + +% Combine mark_gap time_info flags into 'manual'... +EEG = pop_marks_merge_labels(EEG,'time_info',{'mark_gap'},'target_label','manual'); +logging_log('INFO', 'COMBINED MARKS STRUCTURE INTO MANUAL FLAGS...'); + +%%%----------------------------------------------------------------------- +%% SAVE sa.set FILE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +logging_log('INFO', 'Saving file: ./sub-006/eeg/sub-006_task-faceFO_sa.set...'); +if ~exist('derivatives/BIDS-Lossless-EEG/./sub-006/eeg','dir'); + mkdir('derivatives/BIDS-Lossless-EEG/./sub-006/eeg'); +end +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_sa.set'); +logging_log('INFO', 'SAVED sa FILE...'); + +%%%----------------------------------------------------------------------- +%% PREPARE FILE FOR AMICA +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Purges the flagged data to make sure it is primed for ICA and creates the amica paramaeter file. + +% Removed flagged channels and time segments +sprintf('%s','Purging flagged channels...\n'); +EEG = pop_marks_select_data(EEG,'channel marks',[],'labels',{'manual','rank'},'remove','on'); +EEG = pop_marks_select_data(EEG,'time marks',[],'labels',{'manual'},'remove','on'); +EEG = eeg_checkset(EEG); +logging_log('INFO', 'TIME TO: PURGE DATA...'); + +% Make sure EEG.data is doubles for use in amica +EEG.data = double(EEG.data); + +% Save the SA purge file +logging_log('INFO',sprintf('%s','Saving file: derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_sa_purge.set...')); +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_sa_purge.set'); + +% Save diagnostic arrays +try OCTAVE_VERSION; + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_data_sd_t.mat','data_sd_t'); + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_data_sd_ch.mat','data_sd_ch'); + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_data_r_ch.mat','data_r_ch'); + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_data_r_t.mat','data_r_t'); +catch + save('derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_data_sd_t.mat','data_sd_t'); + save('derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_data_sd_ch.mat','data_sd_ch'); + save('derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_data_r_ch.mat','data_r_ch'); + save('derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_data_r_t.mat','data_r_t'); +end + +% Save the AMICA parameter file +swapstr = file_strswap('derivatives/BIDS-Lossless-EEG/code/misc/amica15_default.param', ... + '[repstr_fdt_fname]','derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_sa_purge.fdt', ... + '[repstr_outpath]','derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_amicaout_init', ... + '[repstr_nbchan]', num2str(EEG.nbchan), ... + '[repstr_pnts]', sprintf('%12.0f',EEG.pnts), ... + '[repstr_tpp]', '8', ... + '[repstr_pca]', num2str(EEG.nbchan)); +fidparam = fopen('derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_init.param','w'); +fprintf(fidparam,'%s',swapstr); +fclose(fidparam); + +% CREATE THE AMICA OUTPUT FOLDER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Needed for MPI jobs on cluster only in order to prevent a race effect bug. + +[~] = rmdir('derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_amicaout_init','s'); +mkdir derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_amicaout_init; +logging_log('INFO', 'Created Amica Output Folder'); + +logging_log('INFO', 'Scalpart complete'); +logging_log('INFO', 'Scheduler: sbatch'); +print_chan_sample(EEG); diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s01_scalpart-2019-10-01T14-35-11/s01_scalpart_sub_007_task_faceFO_eeg.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s01_scalpart-2019-10-01T14-35-11/s01_scalpart_sub_007_task_faceFO_eeg.m new file mode 100644 index 000000000..82c036c9e --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s01_scalpart-2019-10-01T14-35-11/s01_scalpart_sub_007_task_faceFO_eeg.m @@ -0,0 +1,529 @@ +warning off; + +pkg load signal; +pkg load parallel; +pkg load io; + +file_path = fileparts(mfilename('fullpath')); + +addpath([file_path '/../../code/dependencies/eeglab_lossless'], ... + [file_path '/../../code/dependencies/matlog'], ... + [file_path '/../../code/tools']); +cd '/scratch/tk11br/Face13/'; + +% eeglab auto loads the path correctly instead of shotgun blasting with +% addpath(genpath(<>)) as we were doing prior +if ~exist('EEG', 'var') + eeglab; +end +% Use matlog disp function override +logging_override_disp(true); +% Use matlog INFO level debugging +logging_set_log_level('INFO') + +% Don't run with traditional but enable some of the options +save_default_options ('-mat-binary'); +confirm_recursive_rmdir(false); + +% This makes | and & || and && in earlier version of Octave where +% | and & dont short curcuit by default +if exist('do_braindead_shortcircuit_evaluation', 'builtin') + do_braindead_shortcircuit_evaluation(true); +end + +% Options +pop_editoptions('option_savetwofiles', 1,'option_single',0); + +%% SCRIPT DESCRIPTION +% This script features the main preprocessing and cleaning of the data before the ICA process begin. Data is flagged for high standard deveiavtion +% by channel to remove comically bad channels. The data is then rereferenced to the average site, and high/low pass filtered. +% Channel neighbor correlations are then calculated to find odd channels and bridged channels. The outermost channel is also flagged to reduce the rank of the data. +% After another rereference channel correlations are calculated by time epoch and flagged. The file is saved containing the, marks structure, then saved again +% with all of the mentioned flags puregd from the data in order to prime it for ICA. +% +%% From Config key_strswap Description +%----------------------------------------------------------------------------------------------------------------------------- +% in_path = [] Relative path to input data files assuming cd = work_path +% montage_info = [0.500 -22.000 -48.000 -0.065 0.000 -1.580 1060.000 1260.000 1220.000] Montage information to fit channel locations on a standard surface. +% This can be a transformations matrix (e.g. [-.14,-22,-48,-.07,0,-1.57,1080,1260,1240]) +% ... or a a file containing a transformation matrix (e.g. ./sub-007/eeg/sub-007_task-faceFO_eeg.edf_transmat.mat). +% ... or a BIDS channel location tsv file (assuming that during BIDS init the channels were coregistered to the standard surface). +% staging_script = derivatives/BIDS-Lossless-EEG/code/scripts/face13_staging.m Script file path/name that creates marks based on events in the raw data file. +% recur_sec = 1 Recurrence (sec) for artifact detection epoching (e.g. .5, Default 1) +% limit_sec = [0 1] Limits (sec) for artifact detection epoching (e.g. [-.5 0], Default [0 recur_sec]) +% sd_t_meth = na Method used for flagging epochs (e.g. 'q' (quantiles), or 'na' (default)) +% sd_t_vals = [.3 .7] Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7]) +% sd_t_o = 16 z threshold for flagging epochs during standard deviation criteria (e.g. 6) +% sd_t_f_meth = fixed Fixed method used for flagging epochs (should be 'fixed') +% sd_t_f_vals = Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% sd_t_f_o = .2 z threshold for flagging epochs during fixed standard deviation criteria (e.g. .2) +% sd_t_pad = 1 Number of windows to pad onto each side of the ch_sd time flag +% sd_ch_meth = q Method used for flagging channels (e.g. 'q' (quantiles), or 'na' (default)) +% sd_ch_vals = [.3 .7] Percentage trim for confidence intervals during channel standard deviation criteria (e.g. [.3 .7]) +% sd_ch_o = 16 z threshold for flagging channels during standard deviation criteria (e.g. 6) +% sd_ch_f_meth = fixed Fixed method used for flagging channels (should be 'fixed') +% sd_ch_f_vals = Percentage trim for confidence intervals during channel standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% sd_ch_f_o = .2 z threshold for flagging channel during fixed standard deviation criteria (e.g. 6) +% ref_loc_file = derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc Name of file containing the reference locations (including the relative path) +% low_bound_hz = 1 Lower bound of the filter bass-band +% high_bound_hz = 100 Upper bound of the filter bass-band +% save_f_res = 1 1 if you want to save the filter residuals (Default: 1) +% n_nbr_ch = 3 Number of channels to use in nearest neighbour r calculation (for channel criteria) +% r_ch_meth = q Method used for flagging low r channels (e.g. 'q' (quantiles), or 'na' (default)) +% r_ch_vals = [.3,.7] Percentage trim for confidence intervals during low r channel standard deviation criteria (e.g. [.3 .7]) +% r_ch_o = 16 z threshold for flagging channel during neighbour r criteria (e.g. 3) +% r_ch_f_meth = fixed Fixed method used for flagging low r channels (should be 'fixed') +% r_ch_f_vals = Percentage trim for confidence intervals during low r channel standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% r_ch_f_o = .2 z threshold for flagging channel during fixed neighbour r criteria (e.g. 3) +% bridge_trim = 40 Percentage trim for z calculation of bridged channels (e.g. 40 = 20% top and bottom) +% bridge_z = 6 z threshold for flagging channel during bridging criteria (e.g. 8) +% n_nbr_t = 3 Number of channels to use in nearest neighbour r calculation (for epoch criteria) +% r_t_meth = q Method used for flagging low r epochs (e.g. 'q' (quantiles), or 'na' (default)) +% r_t_vals = [.3 .7] Percentage trim for confidence intervals during low r epoch standard deviation criteria (e.g. [.3 .7]) +% r_t_o = 16 z threshold for flagging epochs during neighbour r criteria (e.g. 3) +% r_t_f_meth = fixed Fixed method used for flagging low r epochs (should be 'fixed') +% r_t_f_vals = Percentage trim for confidence intervals during low r epoch standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% r_t_f_o = .2 z threshold for flagging epochs during fixed neighbour r criteria (e.g. 3) +% min_gap_ms = 2000 Minimum time (ms) to allow between periods marked for rejection +% out_path = derivatives/BIDS-Lossless-EEG Relative path to output data files assuming cd = work_path +% amica_param_file = derivatives/BIDS-Lossless-EEG/code/misc/amica15_default.param template amicadefs.param file to modify +% amica_threads_s02 = 8 number of threads to use for running s02 amica script (Default: 8) + +%% CHECK FOR OUTPUT PATH AND CRETAE IF NECESSARY +if ~exist('derivatives/BIDS-Lossless-EEG/./sub-007/eeg','dir'); + disp('Making directory derivatives/BIDS-Lossless-EEG/./sub-007/eeg'); + mkdir derivatives/BIDS-Lossless-EEG/./sub-007/eeg +end + +%% LOAD DATASET +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +EEG = pop_bidsload('./sub-007/eeg/sub-007_task-faceFO_eeg.edf','gui',0); + +if ~isfield(EEG,'marks'); + if isempty(EEG.icaweights) + EEG.marks=marks_init(size(EEG.data)); + else + EEG.marks=marks_init(size(EEG.data),min(size(EEG.icaweights))); + end +end + +EEG.marks = marks_add_label(EEG.marks,'time_info', ... +{'init_ind',[0,0,1],[1:EEG.pnts]}); + +%execute the staging script if specified. +if exist('derivatives/BIDS-Lossless-EEG/code/scripts/face13_staging.m'); + [ssp,ssn,sse]=fileparts('derivatives/BIDS-Lossless-EEG/code/scripts/face13_staging.m'); + addpath(ssp); + eval(ssn); +end + +%add channel coregistration procedure here. +%warp locations to standard head surface: +if ~isempty('[0.500 -22.000 -48.000 -0.065 0.000 -1.580 1060.000 1260.000 1220.000]'); + if isempty(str2num('[0.500 -22.000 -48.000 -0.065 0.000 -1.580 1060.000 1260.000 1220.000]')); + %if it is a BIDS channel tsv, load the tsv,sd_t_f_vals + %else read the file that is assumed to be a transformation matrix. + else + EEG = warp_locs( EEG,'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc', ... + 'transform',[[0.500 -22.000 -48.000 -0.065 0.000 -1.580 1060.000 1260.000 1220.000]], ... + 'manual','off'); + end +end + +% Apply average re-reference +%chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +%chan_m = mean(EEG.data(chan_inds,:),1); +%chan_m_mat = repmat(chan_m,size(EEG.data,1),1); +%EEG.data = EEG.data - chan_m_mat; +%clear chan_m_mat; + +% Window the continuous data +logging_log('INFO', 'Windowing the continous data...'); +EEG = marks_continuous2epochs(EEG,'recurrence',[1],'limits',[[0 1]]); +logging_log('INFO', 'LOADED DATASET...'); + +% rereference to selected channels.. +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +epoch_inds = marks_label2index(EEG.marks.time_info,{'manual'},'indexes','invert','on'); +[EEG,trim_ch_sd]=chan_variance(EEG,'data_field','data', ... + 'chan_inds',chan_inds, ... + 'epoch_inds',epoch_inds, ... + 'plot_figs','off'); + +chan_dist=zeros(size(trim_ch_sd)); +for i=1:size(trim_ch_sd,2); + chan_dist(:,i)=(trim_ch_sd(:,i)-median(trim_ch_sd(:,i)))/diff(quantile(trim_ch_sd(:,i),[.3,.7])); +end +mean_chan_dist=mean(chan_dist,2); +m=median(mean_chan_dist); +q=quantile(mean_chan_dist,[.3,.7]); + +refchans=find(mean_chan_distve_trimmean(msr,40,1)+ve_trimstd(msr,40,1)*6); +logging_log('INFO', 'IDENTIFIED BRIDGED CHANNELS...'); + +% Edit the channel flag info structure +logging_log('INFO', 'Updating chflaginfo structure...'); +lnkflags = zeros(EEG.nbchan,1); +lnkflags(chan_inds(flag_b_chan_inds))=1; +EEG.marks = marks_add_label(EEG.marks,'chan_info', ... + {'bridge',[.7,1,.7],[.2,1,.2],-1,lnkflags}); +logging_log('INFO', 'EDITED CHANFLAGINFO STRUCT...'); + +% Combine low_rand bridge chan_info flags into 'manual'... +EEG = pop_marks_merge_labels(EEG,'chan_info',{'low_r','bridge'},'target_label','manual'); +logging_log('INFO', 'COMBINED MARKS STRUCTURE INTO MANUAL FLAGS...'); + +%% FLAG RANK CHAN +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Flags the channel that is the least unique (the best channel to remove prior +% to ICA in order to account for the rereference rank deficiency. + +logging_log('INFO', 'Updating chflaginfo structure...'); +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +[r_max,rank_ind] = max(data_r_ch(chan_inds)); +rankflags = zeros(EEG.nbchan,1); +rankflags(chan_inds(rank_ind))=1; +EEG.marks = marks_add_label(EEG.marks,'chan_info', ... + {'rank',[.1,.1,.24],[.1,.1,.24],-1,rankflags}); +logging_log('INFO', 'EDITED CHANFLAGINFO STRUCT...'); + +%% REREFERENCE TO INTERPOLATED AVERAGE SITE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Rereference the data to an average interpolated site containined 19 channels +logging_log('INFO', 'Rereferencing to averaged interpolated site...'); +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +EEG = interp_ref(EEG,'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc','chans',chan_inds); +EEG = eeg_checkset(EEG); +logging_log('INFO', 'REREFERENCED TO INTERPOLATED AVERAGE SITE...'); + +%% CALCULATE NEAREST NEIGHBOUR R VALUES +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Similarly to the neighbor r calculation done between channels this section looks +% at the correlation, but between all channels and for epochs of time. Time segmenents +% are flagged for removal. + +logging_log('INFO', 'Calculating nearest neighbour r array for window criteria...'); +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +epoch_inds = marks_label2index(EEG.marks.time_info,{'manual'},'indexes','invert','on'); +[EEG,data_r_t,~,~,~] = chan_neighbour_r(EEG, ... + 3,'max', ... + 'chan_inds',chan_inds, ... + 'epoch_inds',epoch_inds, ... + 'plotfigs','off'); +logging_log('INFO', 'CALCULATED NEAREST NEIGHBOUR R VALUES...'); + +% Create the window criteria vector for flagging low_r time_info... +logging_log('INFO', 'Assessing epoch r distributions criteria...') +[~,flag_r_t_inds]=marks_array2flags(data_r_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_dir','neg', ... + 'init_crit',16, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); +logging_log('INFO', 'CREATED EPOCH CRITERIA VECTOR...'); + +% Edit the time flag info structure +logging_log('INFO', 'Updating latflaginfo structure...'); +lowr_epoch_flags = zeros(size(EEG.data(1,:,:))); +lowr_epoch_flags(1,:,epoch_inds(flag_r_t_inds))=1; +EEG.marks = marks_add_label(EEG.marks,'time_info', ... + {'low_r',[0,1,0],lowr_epoch_flags}); +clear lowr_epoch_flags; +logging_log('INFO', 'TIME TO: UPDATE REJECTION STRUCTURE...'); + +% Combine low_r time_info flags into 'manual'... +EEG = pop_marks_merge_labels(EEG,'time_info',{'low_r'},'target_label','manual'); +logging_log('INFO', 'COMBINED MARKS STRUCTURE INTO MANUAL FLAGS...'); + +% Concatenate epoched data back to continuous data +logging_log('INFO', 'Concatenating windowed data...'); +EEG = marks_epochs2continuous(EEG); +EEG = eeg_checkset(EEG,'eventconsistency'); +logging_log('INFO', 'CONCATENATED THE WINDOWED DATA INTO CONTINUOUS DATA...'); + +EEG=pop_marks_flag_gap(EEG,{'manual'},2000,'mark_gap',[.8,.8,.8],'offsets',[0 0],'ref_point','both'); + +% Combine mark_gap time_info flags into 'manual'... +EEG = pop_marks_merge_labels(EEG,'time_info',{'mark_gap'},'target_label','manual'); +logging_log('INFO', 'COMBINED MARKS STRUCTURE INTO MANUAL FLAGS...'); + +%%%----------------------------------------------------------------------- +%% SAVE sa.set FILE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +logging_log('INFO', 'Saving file: ./sub-007/eeg/sub-007_task-faceFO_sa.set...'); +if ~exist('derivatives/BIDS-Lossless-EEG/./sub-007/eeg','dir'); + mkdir('derivatives/BIDS-Lossless-EEG/./sub-007/eeg'); +end +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_sa.set'); +logging_log('INFO', 'SAVED sa FILE...'); + +%%%----------------------------------------------------------------------- +%% PREPARE FILE FOR AMICA +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Purges the flagged data to make sure it is primed for ICA and creates the amica paramaeter file. + +% Removed flagged channels and time segments +sprintf('%s','Purging flagged channels...\n'); +EEG = pop_marks_select_data(EEG,'channel marks',[],'labels',{'manual','rank'},'remove','on'); +EEG = pop_marks_select_data(EEG,'time marks',[],'labels',{'manual'},'remove','on'); +EEG = eeg_checkset(EEG); +logging_log('INFO', 'TIME TO: PURGE DATA...'); + +% Make sure EEG.data is doubles for use in amica +EEG.data = double(EEG.data); + +% Save the SA purge file +logging_log('INFO',sprintf('%s','Saving file: derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_sa_purge.set...')); +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_sa_purge.set'); + +% Save diagnostic arrays +try OCTAVE_VERSION; + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_data_sd_t.mat','data_sd_t'); + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_data_sd_ch.mat','data_sd_ch'); + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_data_r_ch.mat','data_r_ch'); + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_data_r_t.mat','data_r_t'); +catch + save('derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_data_sd_t.mat','data_sd_t'); + save('derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_data_sd_ch.mat','data_sd_ch'); + save('derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_data_r_ch.mat','data_r_ch'); + save('derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_data_r_t.mat','data_r_t'); +end + +% Save the AMICA parameter file +swapstr = file_strswap('derivatives/BIDS-Lossless-EEG/code/misc/amica15_default.param', ... + '[repstr_fdt_fname]','derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_sa_purge.fdt', ... + '[repstr_outpath]','derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_amicaout_init', ... + '[repstr_nbchan]', num2str(EEG.nbchan), ... + '[repstr_pnts]', sprintf('%12.0f',EEG.pnts), ... + '[repstr_tpp]', '8', ... + '[repstr_pca]', num2str(EEG.nbchan)); +fidparam = fopen('derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_init.param','w'); +fprintf(fidparam,'%s',swapstr); +fclose(fidparam); + +% CREATE THE AMICA OUTPUT FOLDER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Needed for MPI jobs on cluster only in order to prevent a race effect bug. + +[~] = rmdir('derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_amicaout_init','s'); +mkdir derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_amicaout_init; +logging_log('INFO', 'Created Amica Output Folder'); + +logging_log('INFO', 'Scalpart complete'); +logging_log('INFO', 'Scheduler: sbatch'); +print_chan_sample(EEG); diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s01_scalpart-2019-10-01T14-35-11/s01_scalpart_sub_008_task_faceFO_eeg.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s01_scalpart-2019-10-01T14-35-11/s01_scalpart_sub_008_task_faceFO_eeg.m new file mode 100644 index 000000000..80bd2922b --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s01_scalpart-2019-10-01T14-35-11/s01_scalpart_sub_008_task_faceFO_eeg.m @@ -0,0 +1,529 @@ +warning off; + +pkg load signal; +pkg load parallel; +pkg load io; + +file_path = fileparts(mfilename('fullpath')); + +addpath([file_path '/../../code/dependencies/eeglab_lossless'], ... + [file_path '/../../code/dependencies/matlog'], ... + [file_path '/../../code/tools']); +cd '/scratch/tk11br/Face13/'; + +% eeglab auto loads the path correctly instead of shotgun blasting with +% addpath(genpath(<>)) as we were doing prior +if ~exist('EEG', 'var') + eeglab; +end +% Use matlog disp function override +logging_override_disp(true); +% Use matlog INFO level debugging +logging_set_log_level('INFO') + +% Don't run with traditional but enable some of the options +save_default_options ('-mat-binary'); +confirm_recursive_rmdir(false); + +% This makes | and & || and && in earlier version of Octave where +% | and & dont short curcuit by default +if exist('do_braindead_shortcircuit_evaluation', 'builtin') + do_braindead_shortcircuit_evaluation(true); +end + +% Options +pop_editoptions('option_savetwofiles', 1,'option_single',0); + +%% SCRIPT DESCRIPTION +% This script features the main preprocessing and cleaning of the data before the ICA process begin. Data is flagged for high standard deveiavtion +% by channel to remove comically bad channels. The data is then rereferenced to the average site, and high/low pass filtered. +% Channel neighbor correlations are then calculated to find odd channels and bridged channels. The outermost channel is also flagged to reduce the rank of the data. +% After another rereference channel correlations are calculated by time epoch and flagged. The file is saved containing the, marks structure, then saved again +% with all of the mentioned flags puregd from the data in order to prime it for ICA. +% +%% From Config key_strswap Description +%----------------------------------------------------------------------------------------------------------------------------- +% in_path = [] Relative path to input data files assuming cd = work_path +% montage_info = [0.500 -22.000 -48.000 -0.065 0.000 -1.580 1060.000 1260.000 1220.000] Montage information to fit channel locations on a standard surface. +% This can be a transformations matrix (e.g. [-.14,-22,-48,-.07,0,-1.57,1080,1260,1240]) +% ... or a a file containing a transformation matrix (e.g. ./sub-008/eeg/sub-008_task-faceFO_eeg.edf_transmat.mat). +% ... or a BIDS channel location tsv file (assuming that during BIDS init the channels were coregistered to the standard surface). +% staging_script = derivatives/BIDS-Lossless-EEG/code/scripts/face13_staging.m Script file path/name that creates marks based on events in the raw data file. +% recur_sec = 1 Recurrence (sec) for artifact detection epoching (e.g. .5, Default 1) +% limit_sec = [0 1] Limits (sec) for artifact detection epoching (e.g. [-.5 0], Default [0 recur_sec]) +% sd_t_meth = na Method used for flagging epochs (e.g. 'q' (quantiles), or 'na' (default)) +% sd_t_vals = [.3 .7] Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7]) +% sd_t_o = 16 z threshold for flagging epochs during standard deviation criteria (e.g. 6) +% sd_t_f_meth = fixed Fixed method used for flagging epochs (should be 'fixed') +% sd_t_f_vals = Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% sd_t_f_o = .2 z threshold for flagging epochs during fixed standard deviation criteria (e.g. .2) +% sd_t_pad = 1 Number of windows to pad onto each side of the ch_sd time flag +% sd_ch_meth = q Method used for flagging channels (e.g. 'q' (quantiles), or 'na' (default)) +% sd_ch_vals = [.3 .7] Percentage trim for confidence intervals during channel standard deviation criteria (e.g. [.3 .7]) +% sd_ch_o = 16 z threshold for flagging channels during standard deviation criteria (e.g. 6) +% sd_ch_f_meth = fixed Fixed method used for flagging channels (should be 'fixed') +% sd_ch_f_vals = Percentage trim for confidence intervals during channel standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% sd_ch_f_o = .2 z threshold for flagging channel during fixed standard deviation criteria (e.g. 6) +% ref_loc_file = derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc Name of file containing the reference locations (including the relative path) +% low_bound_hz = 1 Lower bound of the filter bass-band +% high_bound_hz = 100 Upper bound of the filter bass-band +% save_f_res = 1 1 if you want to save the filter residuals (Default: 1) +% n_nbr_ch = 3 Number of channels to use in nearest neighbour r calculation (for channel criteria) +% r_ch_meth = q Method used for flagging low r channels (e.g. 'q' (quantiles), or 'na' (default)) +% r_ch_vals = [.3,.7] Percentage trim for confidence intervals during low r channel standard deviation criteria (e.g. [.3 .7]) +% r_ch_o = 16 z threshold for flagging channel during neighbour r criteria (e.g. 3) +% r_ch_f_meth = fixed Fixed method used for flagging low r channels (should be 'fixed') +% r_ch_f_vals = Percentage trim for confidence intervals during low r channel standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% r_ch_f_o = .2 z threshold for flagging channel during fixed neighbour r criteria (e.g. 3) +% bridge_trim = 40 Percentage trim for z calculation of bridged channels (e.g. 40 = 20% top and bottom) +% bridge_z = 6 z threshold for flagging channel during bridging criteria (e.g. 8) +% n_nbr_t = 3 Number of channels to use in nearest neighbour r calculation (for epoch criteria) +% r_t_meth = q Method used for flagging low r epochs (e.g. 'q' (quantiles), or 'na' (default)) +% r_t_vals = [.3 .7] Percentage trim for confidence intervals during low r epoch standard deviation criteria (e.g. [.3 .7]) +% r_t_o = 16 z threshold for flagging epochs during neighbour r criteria (e.g. 3) +% r_t_f_meth = fixed Fixed method used for flagging low r epochs (should be 'fixed') +% r_t_f_vals = Percentage trim for confidence intervals during low r epoch standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% r_t_f_o = .2 z threshold for flagging epochs during fixed neighbour r criteria (e.g. 3) +% min_gap_ms = 2000 Minimum time (ms) to allow between periods marked for rejection +% out_path = derivatives/BIDS-Lossless-EEG Relative path to output data files assuming cd = work_path +% amica_param_file = derivatives/BIDS-Lossless-EEG/code/misc/amica15_default.param template amicadefs.param file to modify +% amica_threads_s02 = 8 number of threads to use for running s02 amica script (Default: 8) + +%% CHECK FOR OUTPUT PATH AND CRETAE IF NECESSARY +if ~exist('derivatives/BIDS-Lossless-EEG/./sub-008/eeg','dir'); + disp('Making directory derivatives/BIDS-Lossless-EEG/./sub-008/eeg'); + mkdir derivatives/BIDS-Lossless-EEG/./sub-008/eeg +end + +%% LOAD DATASET +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +EEG = pop_bidsload('./sub-008/eeg/sub-008_task-faceFO_eeg.edf','gui',0); + +if ~isfield(EEG,'marks'); + if isempty(EEG.icaweights) + EEG.marks=marks_init(size(EEG.data)); + else + EEG.marks=marks_init(size(EEG.data),min(size(EEG.icaweights))); + end +end + +EEG.marks = marks_add_label(EEG.marks,'time_info', ... +{'init_ind',[0,0,1],[1:EEG.pnts]}); + +%execute the staging script if specified. +if exist('derivatives/BIDS-Lossless-EEG/code/scripts/face13_staging.m'); + [ssp,ssn,sse]=fileparts('derivatives/BIDS-Lossless-EEG/code/scripts/face13_staging.m'); + addpath(ssp); + eval(ssn); +end + +%add channel coregistration procedure here. +%warp locations to standard head surface: +if ~isempty('[0.500 -22.000 -48.000 -0.065 0.000 -1.580 1060.000 1260.000 1220.000]'); + if isempty(str2num('[0.500 -22.000 -48.000 -0.065 0.000 -1.580 1060.000 1260.000 1220.000]')); + %if it is a BIDS channel tsv, load the tsv,sd_t_f_vals + %else read the file that is assumed to be a transformation matrix. + else + EEG = warp_locs( EEG,'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc', ... + 'transform',[[0.500 -22.000 -48.000 -0.065 0.000 -1.580 1060.000 1260.000 1220.000]], ... + 'manual','off'); + end +end + +% Apply average re-reference +%chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +%chan_m = mean(EEG.data(chan_inds,:),1); +%chan_m_mat = repmat(chan_m,size(EEG.data,1),1); +%EEG.data = EEG.data - chan_m_mat; +%clear chan_m_mat; + +% Window the continuous data +logging_log('INFO', 'Windowing the continous data...'); +EEG = marks_continuous2epochs(EEG,'recurrence',[1],'limits',[[0 1]]); +logging_log('INFO', 'LOADED DATASET...'); + +% rereference to selected channels.. +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +epoch_inds = marks_label2index(EEG.marks.time_info,{'manual'},'indexes','invert','on'); +[EEG,trim_ch_sd]=chan_variance(EEG,'data_field','data', ... + 'chan_inds',chan_inds, ... + 'epoch_inds',epoch_inds, ... + 'plot_figs','off'); + +chan_dist=zeros(size(trim_ch_sd)); +for i=1:size(trim_ch_sd,2); + chan_dist(:,i)=(trim_ch_sd(:,i)-median(trim_ch_sd(:,i)))/diff(quantile(trim_ch_sd(:,i),[.3,.7])); +end +mean_chan_dist=mean(chan_dist,2); +m=median(mean_chan_dist); +q=quantile(mean_chan_dist,[.3,.7]); + +refchans=find(mean_chan_distve_trimmean(msr,40,1)+ve_trimstd(msr,40,1)*6); +logging_log('INFO', 'IDENTIFIED BRIDGED CHANNELS...'); + +% Edit the channel flag info structure +logging_log('INFO', 'Updating chflaginfo structure...'); +lnkflags = zeros(EEG.nbchan,1); +lnkflags(chan_inds(flag_b_chan_inds))=1; +EEG.marks = marks_add_label(EEG.marks,'chan_info', ... + {'bridge',[.7,1,.7],[.2,1,.2],-1,lnkflags}); +logging_log('INFO', 'EDITED CHANFLAGINFO STRUCT...'); + +% Combine low_rand bridge chan_info flags into 'manual'... +EEG = pop_marks_merge_labels(EEG,'chan_info',{'low_r','bridge'},'target_label','manual'); +logging_log('INFO', 'COMBINED MARKS STRUCTURE INTO MANUAL FLAGS...'); + +%% FLAG RANK CHAN +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Flags the channel that is the least unique (the best channel to remove prior +% to ICA in order to account for the rereference rank deficiency. + +logging_log('INFO', 'Updating chflaginfo structure...'); +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +[r_max,rank_ind] = max(data_r_ch(chan_inds)); +rankflags = zeros(EEG.nbchan,1); +rankflags(chan_inds(rank_ind))=1; +EEG.marks = marks_add_label(EEG.marks,'chan_info', ... + {'rank',[.1,.1,.24],[.1,.1,.24],-1,rankflags}); +logging_log('INFO', 'EDITED CHANFLAGINFO STRUCT...'); + +%% REREFERENCE TO INTERPOLATED AVERAGE SITE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Rereference the data to an average interpolated site containined 19 channels +logging_log('INFO', 'Rereferencing to averaged interpolated site...'); +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +EEG = interp_ref(EEG,'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc','chans',chan_inds); +EEG = eeg_checkset(EEG); +logging_log('INFO', 'REREFERENCED TO INTERPOLATED AVERAGE SITE...'); + +%% CALCULATE NEAREST NEIGHBOUR R VALUES +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Similarly to the neighbor r calculation done between channels this section looks +% at the correlation, but between all channels and for epochs of time. Time segmenents +% are flagged for removal. + +logging_log('INFO', 'Calculating nearest neighbour r array for window criteria...'); +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +epoch_inds = marks_label2index(EEG.marks.time_info,{'manual'},'indexes','invert','on'); +[EEG,data_r_t,~,~,~] = chan_neighbour_r(EEG, ... + 3,'max', ... + 'chan_inds',chan_inds, ... + 'epoch_inds',epoch_inds, ... + 'plotfigs','off'); +logging_log('INFO', 'CALCULATED NEAREST NEIGHBOUR R VALUES...'); + +% Create the window criteria vector for flagging low_r time_info... +logging_log('INFO', 'Assessing epoch r distributions criteria...') +[~,flag_r_t_inds]=marks_array2flags(data_r_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_dir','neg', ... + 'init_crit',16, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); +logging_log('INFO', 'CREATED EPOCH CRITERIA VECTOR...'); + +% Edit the time flag info structure +logging_log('INFO', 'Updating latflaginfo structure...'); +lowr_epoch_flags = zeros(size(EEG.data(1,:,:))); +lowr_epoch_flags(1,:,epoch_inds(flag_r_t_inds))=1; +EEG.marks = marks_add_label(EEG.marks,'time_info', ... + {'low_r',[0,1,0],lowr_epoch_flags}); +clear lowr_epoch_flags; +logging_log('INFO', 'TIME TO: UPDATE REJECTION STRUCTURE...'); + +% Combine low_r time_info flags into 'manual'... +EEG = pop_marks_merge_labels(EEG,'time_info',{'low_r'},'target_label','manual'); +logging_log('INFO', 'COMBINED MARKS STRUCTURE INTO MANUAL FLAGS...'); + +% Concatenate epoched data back to continuous data +logging_log('INFO', 'Concatenating windowed data...'); +EEG = marks_epochs2continuous(EEG); +EEG = eeg_checkset(EEG,'eventconsistency'); +logging_log('INFO', 'CONCATENATED THE WINDOWED DATA INTO CONTINUOUS DATA...'); + +EEG=pop_marks_flag_gap(EEG,{'manual'},2000,'mark_gap',[.8,.8,.8],'offsets',[0 0],'ref_point','both'); + +% Combine mark_gap time_info flags into 'manual'... +EEG = pop_marks_merge_labels(EEG,'time_info',{'mark_gap'},'target_label','manual'); +logging_log('INFO', 'COMBINED MARKS STRUCTURE INTO MANUAL FLAGS...'); + +%%%----------------------------------------------------------------------- +%% SAVE sa.set FILE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +logging_log('INFO', 'Saving file: ./sub-008/eeg/sub-008_task-faceFO_sa.set...'); +if ~exist('derivatives/BIDS-Lossless-EEG/./sub-008/eeg','dir'); + mkdir('derivatives/BIDS-Lossless-EEG/./sub-008/eeg'); +end +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_sa.set'); +logging_log('INFO', 'SAVED sa FILE...'); + +%%%----------------------------------------------------------------------- +%% PREPARE FILE FOR AMICA +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Purges the flagged data to make sure it is primed for ICA and creates the amica paramaeter file. + +% Removed flagged channels and time segments +sprintf('%s','Purging flagged channels...\n'); +EEG = pop_marks_select_data(EEG,'channel marks',[],'labels',{'manual','rank'},'remove','on'); +EEG = pop_marks_select_data(EEG,'time marks',[],'labels',{'manual'},'remove','on'); +EEG = eeg_checkset(EEG); +logging_log('INFO', 'TIME TO: PURGE DATA...'); + +% Make sure EEG.data is doubles for use in amica +EEG.data = double(EEG.data); + +% Save the SA purge file +logging_log('INFO',sprintf('%s','Saving file: derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_sa_purge.set...')); +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_sa_purge.set'); + +% Save diagnostic arrays +try OCTAVE_VERSION; + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_data_sd_t.mat','data_sd_t'); + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_data_sd_ch.mat','data_sd_ch'); + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_data_r_ch.mat','data_r_ch'); + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_data_r_t.mat','data_r_t'); +catch + save('derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_data_sd_t.mat','data_sd_t'); + save('derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_data_sd_ch.mat','data_sd_ch'); + save('derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_data_r_ch.mat','data_r_ch'); + save('derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_data_r_t.mat','data_r_t'); +end + +% Save the AMICA parameter file +swapstr = file_strswap('derivatives/BIDS-Lossless-EEG/code/misc/amica15_default.param', ... + '[repstr_fdt_fname]','derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_sa_purge.fdt', ... + '[repstr_outpath]','derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_amicaout_init', ... + '[repstr_nbchan]', num2str(EEG.nbchan), ... + '[repstr_pnts]', sprintf('%12.0f',EEG.pnts), ... + '[repstr_tpp]', '8', ... + '[repstr_pca]', num2str(EEG.nbchan)); +fidparam = fopen('derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_init.param','w'); +fprintf(fidparam,'%s',swapstr); +fclose(fidparam); + +% CREATE THE AMICA OUTPUT FOLDER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Needed for MPI jobs on cluster only in order to prevent a race effect bug. + +[~] = rmdir('derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_amicaout_init','s'); +mkdir derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_amicaout_init; +logging_log('INFO', 'Created Amica Output Folder'); + +logging_log('INFO', 'Scalpart complete'); +logging_log('INFO', 'Scheduler: sbatch'); +print_chan_sample(EEG); diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s01_scalpart-2019-10-01T14-35-11/s01_scalpart_sub_009_task_faceFO_eeg.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s01_scalpart-2019-10-01T14-35-11/s01_scalpart_sub_009_task_faceFO_eeg.m new file mode 100644 index 000000000..d69c111e8 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s01_scalpart-2019-10-01T14-35-11/s01_scalpart_sub_009_task_faceFO_eeg.m @@ -0,0 +1,529 @@ +warning off; + +pkg load signal; +pkg load parallel; +pkg load io; + +file_path = fileparts(mfilename('fullpath')); + +addpath([file_path '/../../code/dependencies/eeglab_lossless'], ... + [file_path '/../../code/dependencies/matlog'], ... + [file_path '/../../code/tools']); +cd '/scratch/tk11br/Face13/'; + +% eeglab auto loads the path correctly instead of shotgun blasting with +% addpath(genpath(<>)) as we were doing prior +if ~exist('EEG', 'var') + eeglab; +end +% Use matlog disp function override +logging_override_disp(true); +% Use matlog INFO level debugging +logging_set_log_level('INFO') + +% Don't run with traditional but enable some of the options +save_default_options ('-mat-binary'); +confirm_recursive_rmdir(false); + +% This makes | and & || and && in earlier version of Octave where +% | and & dont short curcuit by default +if exist('do_braindead_shortcircuit_evaluation', 'builtin') + do_braindead_shortcircuit_evaluation(true); +end + +% Options +pop_editoptions('option_savetwofiles', 1,'option_single',0); + +%% SCRIPT DESCRIPTION +% This script features the main preprocessing and cleaning of the data before the ICA process begin. Data is flagged for high standard deveiavtion +% by channel to remove comically bad channels. The data is then rereferenced to the average site, and high/low pass filtered. +% Channel neighbor correlations are then calculated to find odd channels and bridged channels. The outermost channel is also flagged to reduce the rank of the data. +% After another rereference channel correlations are calculated by time epoch and flagged. The file is saved containing the, marks structure, then saved again +% with all of the mentioned flags puregd from the data in order to prime it for ICA. +% +%% From Config key_strswap Description +%----------------------------------------------------------------------------------------------------------------------------- +% in_path = [] Relative path to input data files assuming cd = work_path +% montage_info = [0.500 -22.000 -48.000 -0.065 0.000 -1.580 1060.000 1260.000 1220.000] Montage information to fit channel locations on a standard surface. +% This can be a transformations matrix (e.g. [-.14,-22,-48,-.07,0,-1.57,1080,1260,1240]) +% ... or a a file containing a transformation matrix (e.g. ./sub-009/eeg/sub-009_task-faceFO_eeg.edf_transmat.mat). +% ... or a BIDS channel location tsv file (assuming that during BIDS init the channels were coregistered to the standard surface). +% staging_script = derivatives/BIDS-Lossless-EEG/code/scripts/face13_staging.m Script file path/name that creates marks based on events in the raw data file. +% recur_sec = 1 Recurrence (sec) for artifact detection epoching (e.g. .5, Default 1) +% limit_sec = [0 1] Limits (sec) for artifact detection epoching (e.g. [-.5 0], Default [0 recur_sec]) +% sd_t_meth = na Method used for flagging epochs (e.g. 'q' (quantiles), or 'na' (default)) +% sd_t_vals = [.3 .7] Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7]) +% sd_t_o = 16 z threshold for flagging epochs during standard deviation criteria (e.g. 6) +% sd_t_f_meth = fixed Fixed method used for flagging epochs (should be 'fixed') +% sd_t_f_vals = Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% sd_t_f_o = .2 z threshold for flagging epochs during fixed standard deviation criteria (e.g. .2) +% sd_t_pad = 1 Number of windows to pad onto each side of the ch_sd time flag +% sd_ch_meth = q Method used for flagging channels (e.g. 'q' (quantiles), or 'na' (default)) +% sd_ch_vals = [.3 .7] Percentage trim for confidence intervals during channel standard deviation criteria (e.g. [.3 .7]) +% sd_ch_o = 16 z threshold for flagging channels during standard deviation criteria (e.g. 6) +% sd_ch_f_meth = fixed Fixed method used for flagging channels (should be 'fixed') +% sd_ch_f_vals = Percentage trim for confidence intervals during channel standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% sd_ch_f_o = .2 z threshold for flagging channel during fixed standard deviation criteria (e.g. 6) +% ref_loc_file = derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc Name of file containing the reference locations (including the relative path) +% low_bound_hz = 1 Lower bound of the filter bass-band +% high_bound_hz = 100 Upper bound of the filter bass-band +% save_f_res = 1 1 if you want to save the filter residuals (Default: 1) +% n_nbr_ch = 3 Number of channels to use in nearest neighbour r calculation (for channel criteria) +% r_ch_meth = q Method used for flagging low r channels (e.g. 'q' (quantiles), or 'na' (default)) +% r_ch_vals = [.3,.7] Percentage trim for confidence intervals during low r channel standard deviation criteria (e.g. [.3 .7]) +% r_ch_o = 16 z threshold for flagging channel during neighbour r criteria (e.g. 3) +% r_ch_f_meth = fixed Fixed method used for flagging low r channels (should be 'fixed') +% r_ch_f_vals = Percentage trim for confidence intervals during low r channel standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% r_ch_f_o = .2 z threshold for flagging channel during fixed neighbour r criteria (e.g. 3) +% bridge_trim = 40 Percentage trim for z calculation of bridged channels (e.g. 40 = 20% top and bottom) +% bridge_z = 6 z threshold for flagging channel during bridging criteria (e.g. 8) +% n_nbr_t = 3 Number of channels to use in nearest neighbour r calculation (for epoch criteria) +% r_t_meth = q Method used for flagging low r epochs (e.g. 'q' (quantiles), or 'na' (default)) +% r_t_vals = [.3 .7] Percentage trim for confidence intervals during low r epoch standard deviation criteria (e.g. [.3 .7]) +% r_t_o = 16 z threshold for flagging epochs during neighbour r criteria (e.g. 3) +% r_t_f_meth = fixed Fixed method used for flagging low r epochs (should be 'fixed') +% r_t_f_vals = Percentage trim for confidence intervals during low r epoch standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% r_t_f_o = .2 z threshold for flagging epochs during fixed neighbour r criteria (e.g. 3) +% min_gap_ms = 2000 Minimum time (ms) to allow between periods marked for rejection +% out_path = derivatives/BIDS-Lossless-EEG Relative path to output data files assuming cd = work_path +% amica_param_file = derivatives/BIDS-Lossless-EEG/code/misc/amica15_default.param template amicadefs.param file to modify +% amica_threads_s02 = 8 number of threads to use for running s02 amica script (Default: 8) + +%% CHECK FOR OUTPUT PATH AND CRETAE IF NECESSARY +if ~exist('derivatives/BIDS-Lossless-EEG/./sub-009/eeg','dir'); + disp('Making directory derivatives/BIDS-Lossless-EEG/./sub-009/eeg'); + mkdir derivatives/BIDS-Lossless-EEG/./sub-009/eeg +end + +%% LOAD DATASET +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +EEG = pop_bidsload('./sub-009/eeg/sub-009_task-faceFO_eeg.edf','gui',0); + +if ~isfield(EEG,'marks'); + if isempty(EEG.icaweights) + EEG.marks=marks_init(size(EEG.data)); + else + EEG.marks=marks_init(size(EEG.data),min(size(EEG.icaweights))); + end +end + +EEG.marks = marks_add_label(EEG.marks,'time_info', ... +{'init_ind',[0,0,1],[1:EEG.pnts]}); + +%execute the staging script if specified. +if exist('derivatives/BIDS-Lossless-EEG/code/scripts/face13_staging.m'); + [ssp,ssn,sse]=fileparts('derivatives/BIDS-Lossless-EEG/code/scripts/face13_staging.m'); + addpath(ssp); + eval(ssn); +end + +%add channel coregistration procedure here. +%warp locations to standard head surface: +if ~isempty('[0.500 -22.000 -48.000 -0.065 0.000 -1.580 1060.000 1260.000 1220.000]'); + if isempty(str2num('[0.500 -22.000 -48.000 -0.065 0.000 -1.580 1060.000 1260.000 1220.000]')); + %if it is a BIDS channel tsv, load the tsv,sd_t_f_vals + %else read the file that is assumed to be a transformation matrix. + else + EEG = warp_locs( EEG,'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc', ... + 'transform',[[0.500 -22.000 -48.000 -0.065 0.000 -1.580 1060.000 1260.000 1220.000]], ... + 'manual','off'); + end +end + +% Apply average re-reference +%chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +%chan_m = mean(EEG.data(chan_inds,:),1); +%chan_m_mat = repmat(chan_m,size(EEG.data,1),1); +%EEG.data = EEG.data - chan_m_mat; +%clear chan_m_mat; + +% Window the continuous data +logging_log('INFO', 'Windowing the continous data...'); +EEG = marks_continuous2epochs(EEG,'recurrence',[1],'limits',[[0 1]]); +logging_log('INFO', 'LOADED DATASET...'); + +% rereference to selected channels.. +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +epoch_inds = marks_label2index(EEG.marks.time_info,{'manual'},'indexes','invert','on'); +[EEG,trim_ch_sd]=chan_variance(EEG,'data_field','data', ... + 'chan_inds',chan_inds, ... + 'epoch_inds',epoch_inds, ... + 'plot_figs','off'); + +chan_dist=zeros(size(trim_ch_sd)); +for i=1:size(trim_ch_sd,2); + chan_dist(:,i)=(trim_ch_sd(:,i)-median(trim_ch_sd(:,i)))/diff(quantile(trim_ch_sd(:,i),[.3,.7])); +end +mean_chan_dist=mean(chan_dist,2); +m=median(mean_chan_dist); +q=quantile(mean_chan_dist,[.3,.7]); + +refchans=find(mean_chan_distve_trimmean(msr,40,1)+ve_trimstd(msr,40,1)*6); +logging_log('INFO', 'IDENTIFIED BRIDGED CHANNELS...'); + +% Edit the channel flag info structure +logging_log('INFO', 'Updating chflaginfo structure...'); +lnkflags = zeros(EEG.nbchan,1); +lnkflags(chan_inds(flag_b_chan_inds))=1; +EEG.marks = marks_add_label(EEG.marks,'chan_info', ... + {'bridge',[.7,1,.7],[.2,1,.2],-1,lnkflags}); +logging_log('INFO', 'EDITED CHANFLAGINFO STRUCT...'); + +% Combine low_rand bridge chan_info flags into 'manual'... +EEG = pop_marks_merge_labels(EEG,'chan_info',{'low_r','bridge'},'target_label','manual'); +logging_log('INFO', 'COMBINED MARKS STRUCTURE INTO MANUAL FLAGS...'); + +%% FLAG RANK CHAN +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Flags the channel that is the least unique (the best channel to remove prior +% to ICA in order to account for the rereference rank deficiency. + +logging_log('INFO', 'Updating chflaginfo structure...'); +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +[r_max,rank_ind] = max(data_r_ch(chan_inds)); +rankflags = zeros(EEG.nbchan,1); +rankflags(chan_inds(rank_ind))=1; +EEG.marks = marks_add_label(EEG.marks,'chan_info', ... + {'rank',[.1,.1,.24],[.1,.1,.24],-1,rankflags}); +logging_log('INFO', 'EDITED CHANFLAGINFO STRUCT...'); + +%% REREFERENCE TO INTERPOLATED AVERAGE SITE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Rereference the data to an average interpolated site containined 19 channels +logging_log('INFO', 'Rereferencing to averaged interpolated site...'); +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +EEG = interp_ref(EEG,'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc','chans',chan_inds); +EEG = eeg_checkset(EEG); +logging_log('INFO', 'REREFERENCED TO INTERPOLATED AVERAGE SITE...'); + +%% CALCULATE NEAREST NEIGHBOUR R VALUES +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Similarly to the neighbor r calculation done between channels this section looks +% at the correlation, but between all channels and for epochs of time. Time segmenents +% are flagged for removal. + +logging_log('INFO', 'Calculating nearest neighbour r array for window criteria...'); +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +epoch_inds = marks_label2index(EEG.marks.time_info,{'manual'},'indexes','invert','on'); +[EEG,data_r_t,~,~,~] = chan_neighbour_r(EEG, ... + 3,'max', ... + 'chan_inds',chan_inds, ... + 'epoch_inds',epoch_inds, ... + 'plotfigs','off'); +logging_log('INFO', 'CALCULATED NEAREST NEIGHBOUR R VALUES...'); + +% Create the window criteria vector for flagging low_r time_info... +logging_log('INFO', 'Assessing epoch r distributions criteria...') +[~,flag_r_t_inds]=marks_array2flags(data_r_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_dir','neg', ... + 'init_crit',16, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); +logging_log('INFO', 'CREATED EPOCH CRITERIA VECTOR...'); + +% Edit the time flag info structure +logging_log('INFO', 'Updating latflaginfo structure...'); +lowr_epoch_flags = zeros(size(EEG.data(1,:,:))); +lowr_epoch_flags(1,:,epoch_inds(flag_r_t_inds))=1; +EEG.marks = marks_add_label(EEG.marks,'time_info', ... + {'low_r',[0,1,0],lowr_epoch_flags}); +clear lowr_epoch_flags; +logging_log('INFO', 'TIME TO: UPDATE REJECTION STRUCTURE...'); + +% Combine low_r time_info flags into 'manual'... +EEG = pop_marks_merge_labels(EEG,'time_info',{'low_r'},'target_label','manual'); +logging_log('INFO', 'COMBINED MARKS STRUCTURE INTO MANUAL FLAGS...'); + +% Concatenate epoched data back to continuous data +logging_log('INFO', 'Concatenating windowed data...'); +EEG = marks_epochs2continuous(EEG); +EEG = eeg_checkset(EEG,'eventconsistency'); +logging_log('INFO', 'CONCATENATED THE WINDOWED DATA INTO CONTINUOUS DATA...'); + +EEG=pop_marks_flag_gap(EEG,{'manual'},2000,'mark_gap',[.8,.8,.8],'offsets',[0 0],'ref_point','both'); + +% Combine mark_gap time_info flags into 'manual'... +EEG = pop_marks_merge_labels(EEG,'time_info',{'mark_gap'},'target_label','manual'); +logging_log('INFO', 'COMBINED MARKS STRUCTURE INTO MANUAL FLAGS...'); + +%%%----------------------------------------------------------------------- +%% SAVE sa.set FILE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +logging_log('INFO', 'Saving file: ./sub-009/eeg/sub-009_task-faceFO_sa.set...'); +if ~exist('derivatives/BIDS-Lossless-EEG/./sub-009/eeg','dir'); + mkdir('derivatives/BIDS-Lossless-EEG/./sub-009/eeg'); +end +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_sa.set'); +logging_log('INFO', 'SAVED sa FILE...'); + +%%%----------------------------------------------------------------------- +%% PREPARE FILE FOR AMICA +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Purges the flagged data to make sure it is primed for ICA and creates the amica paramaeter file. + +% Removed flagged channels and time segments +sprintf('%s','Purging flagged channels...\n'); +EEG = pop_marks_select_data(EEG,'channel marks',[],'labels',{'manual','rank'},'remove','on'); +EEG = pop_marks_select_data(EEG,'time marks',[],'labels',{'manual'},'remove','on'); +EEG = eeg_checkset(EEG); +logging_log('INFO', 'TIME TO: PURGE DATA...'); + +% Make sure EEG.data is doubles for use in amica +EEG.data = double(EEG.data); + +% Save the SA purge file +logging_log('INFO',sprintf('%s','Saving file: derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_sa_purge.set...')); +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_sa_purge.set'); + +% Save diagnostic arrays +try OCTAVE_VERSION; + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_data_sd_t.mat','data_sd_t'); + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_data_sd_ch.mat','data_sd_ch'); + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_data_r_ch.mat','data_r_ch'); + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_data_r_t.mat','data_r_t'); +catch + save('derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_data_sd_t.mat','data_sd_t'); + save('derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_data_sd_ch.mat','data_sd_ch'); + save('derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_data_r_ch.mat','data_r_ch'); + save('derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_data_r_t.mat','data_r_t'); +end + +% Save the AMICA parameter file +swapstr = file_strswap('derivatives/BIDS-Lossless-EEG/code/misc/amica15_default.param', ... + '[repstr_fdt_fname]','derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_sa_purge.fdt', ... + '[repstr_outpath]','derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_amicaout_init', ... + '[repstr_nbchan]', num2str(EEG.nbchan), ... + '[repstr_pnts]', sprintf('%12.0f',EEG.pnts), ... + '[repstr_tpp]', '8', ... + '[repstr_pca]', num2str(EEG.nbchan)); +fidparam = fopen('derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_init.param','w'); +fprintf(fidparam,'%s',swapstr); +fclose(fidparam); + +% CREATE THE AMICA OUTPUT FOLDER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Needed for MPI jobs on cluster only in order to prevent a race effect bug. + +[~] = rmdir('derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_amicaout_init','s'); +mkdir derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_amicaout_init; +logging_log('INFO', 'Created Amica Output Folder'); + +logging_log('INFO', 'Scalpart complete'); +logging_log('INFO', 'Scheduler: sbatch'); +print_chan_sample(EEG); diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s01_scalpart-2019-10-01T14-35-11/s01_scalpart_sub_010_task_faceFO_eeg.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s01_scalpart-2019-10-01T14-35-11/s01_scalpart_sub_010_task_faceFO_eeg.m new file mode 100644 index 000000000..6e9225d8b --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s01_scalpart-2019-10-01T14-35-11/s01_scalpart_sub_010_task_faceFO_eeg.m @@ -0,0 +1,529 @@ +warning off; + +pkg load signal; +pkg load parallel; +pkg load io; + +file_path = fileparts(mfilename('fullpath')); + +addpath([file_path '/../../code/dependencies/eeglab_lossless'], ... + [file_path '/../../code/dependencies/matlog'], ... + [file_path '/../../code/tools']); +cd '/scratch/tk11br/Face13/'; + +% eeglab auto loads the path correctly instead of shotgun blasting with +% addpath(genpath(<>)) as we were doing prior +if ~exist('EEG', 'var') + eeglab; +end +% Use matlog disp function override +logging_override_disp(true); +% Use matlog INFO level debugging +logging_set_log_level('INFO') + +% Don't run with traditional but enable some of the options +save_default_options ('-mat-binary'); +confirm_recursive_rmdir(false); + +% This makes | and & || and && in earlier version of Octave where +% | and & dont short curcuit by default +if exist('do_braindead_shortcircuit_evaluation', 'builtin') + do_braindead_shortcircuit_evaluation(true); +end + +% Options +pop_editoptions('option_savetwofiles', 1,'option_single',0); + +%% SCRIPT DESCRIPTION +% This script features the main preprocessing and cleaning of the data before the ICA process begin. Data is flagged for high standard deveiavtion +% by channel to remove comically bad channels. The data is then rereferenced to the average site, and high/low pass filtered. +% Channel neighbor correlations are then calculated to find odd channels and bridged channels. The outermost channel is also flagged to reduce the rank of the data. +% After another rereference channel correlations are calculated by time epoch and flagged. The file is saved containing the, marks structure, then saved again +% with all of the mentioned flags puregd from the data in order to prime it for ICA. +% +%% From Config key_strswap Description +%----------------------------------------------------------------------------------------------------------------------------- +% in_path = [] Relative path to input data files assuming cd = work_path +% montage_info = [0.500 -22.000 -48.000 -0.065 0.000 -1.580 1060.000 1260.000 1220.000] Montage information to fit channel locations on a standard surface. +% This can be a transformations matrix (e.g. [-.14,-22,-48,-.07,0,-1.57,1080,1260,1240]) +% ... or a a file containing a transformation matrix (e.g. ./sub-010/eeg/sub-010_task-faceFO_eeg.edf_transmat.mat). +% ... or a BIDS channel location tsv file (assuming that during BIDS init the channels were coregistered to the standard surface). +% staging_script = derivatives/BIDS-Lossless-EEG/code/scripts/face13_staging.m Script file path/name that creates marks based on events in the raw data file. +% recur_sec = 1 Recurrence (sec) for artifact detection epoching (e.g. .5, Default 1) +% limit_sec = [0 1] Limits (sec) for artifact detection epoching (e.g. [-.5 0], Default [0 recur_sec]) +% sd_t_meth = na Method used for flagging epochs (e.g. 'q' (quantiles), or 'na' (default)) +% sd_t_vals = [.3 .7] Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7]) +% sd_t_o = 16 z threshold for flagging epochs during standard deviation criteria (e.g. 6) +% sd_t_f_meth = fixed Fixed method used for flagging epochs (should be 'fixed') +% sd_t_f_vals = Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% sd_t_f_o = .2 z threshold for flagging epochs during fixed standard deviation criteria (e.g. .2) +% sd_t_pad = 1 Number of windows to pad onto each side of the ch_sd time flag +% sd_ch_meth = q Method used for flagging channels (e.g. 'q' (quantiles), or 'na' (default)) +% sd_ch_vals = [.3 .7] Percentage trim for confidence intervals during channel standard deviation criteria (e.g. [.3 .7]) +% sd_ch_o = 16 z threshold for flagging channels during standard deviation criteria (e.g. 6) +% sd_ch_f_meth = fixed Fixed method used for flagging channels (should be 'fixed') +% sd_ch_f_vals = Percentage trim for confidence intervals during channel standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% sd_ch_f_o = .2 z threshold for flagging channel during fixed standard deviation criteria (e.g. 6) +% ref_loc_file = derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc Name of file containing the reference locations (including the relative path) +% low_bound_hz = 1 Lower bound of the filter bass-band +% high_bound_hz = 100 Upper bound of the filter bass-band +% save_f_res = 1 1 if you want to save the filter residuals (Default: 1) +% n_nbr_ch = 3 Number of channels to use in nearest neighbour r calculation (for channel criteria) +% r_ch_meth = q Method used for flagging low r channels (e.g. 'q' (quantiles), or 'na' (default)) +% r_ch_vals = [.3,.7] Percentage trim for confidence intervals during low r channel standard deviation criteria (e.g. [.3 .7]) +% r_ch_o = 16 z threshold for flagging channel during neighbour r criteria (e.g. 3) +% r_ch_f_meth = fixed Fixed method used for flagging low r channels (should be 'fixed') +% r_ch_f_vals = Percentage trim for confidence intervals during low r channel standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% r_ch_f_o = .2 z threshold for flagging channel during fixed neighbour r criteria (e.g. 3) +% bridge_trim = 40 Percentage trim for z calculation of bridged channels (e.g. 40 = 20% top and bottom) +% bridge_z = 6 z threshold for flagging channel during bridging criteria (e.g. 8) +% n_nbr_t = 3 Number of channels to use in nearest neighbour r calculation (for epoch criteria) +% r_t_meth = q Method used for flagging low r epochs (e.g. 'q' (quantiles), or 'na' (default)) +% r_t_vals = [.3 .7] Percentage trim for confidence intervals during low r epoch standard deviation criteria (e.g. [.3 .7]) +% r_t_o = 16 z threshold for flagging epochs during neighbour r criteria (e.g. 3) +% r_t_f_meth = fixed Fixed method used for flagging low r epochs (should be 'fixed') +% r_t_f_vals = Percentage trim for confidence intervals during low r epoch standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% r_t_f_o = .2 z threshold for flagging epochs during fixed neighbour r criteria (e.g. 3) +% min_gap_ms = 2000 Minimum time (ms) to allow between periods marked for rejection +% out_path = derivatives/BIDS-Lossless-EEG Relative path to output data files assuming cd = work_path +% amica_param_file = derivatives/BIDS-Lossless-EEG/code/misc/amica15_default.param template amicadefs.param file to modify +% amica_threads_s02 = 8 number of threads to use for running s02 amica script (Default: 8) + +%% CHECK FOR OUTPUT PATH AND CRETAE IF NECESSARY +if ~exist('derivatives/BIDS-Lossless-EEG/./sub-010/eeg','dir'); + disp('Making directory derivatives/BIDS-Lossless-EEG/./sub-010/eeg'); + mkdir derivatives/BIDS-Lossless-EEG/./sub-010/eeg +end + +%% LOAD DATASET +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +EEG = pop_bidsload('./sub-010/eeg/sub-010_task-faceFO_eeg.edf','gui',0); + +if ~isfield(EEG,'marks'); + if isempty(EEG.icaweights) + EEG.marks=marks_init(size(EEG.data)); + else + EEG.marks=marks_init(size(EEG.data),min(size(EEG.icaweights))); + end +end + +EEG.marks = marks_add_label(EEG.marks,'time_info', ... +{'init_ind',[0,0,1],[1:EEG.pnts]}); + +%execute the staging script if specified. +if exist('derivatives/BIDS-Lossless-EEG/code/scripts/face13_staging.m'); + [ssp,ssn,sse]=fileparts('derivatives/BIDS-Lossless-EEG/code/scripts/face13_staging.m'); + addpath(ssp); + eval(ssn); +end + +%add channel coregistration procedure here. +%warp locations to standard head surface: +if ~isempty('[0.500 -22.000 -48.000 -0.065 0.000 -1.580 1060.000 1260.000 1220.000]'); + if isempty(str2num('[0.500 -22.000 -48.000 -0.065 0.000 -1.580 1060.000 1260.000 1220.000]')); + %if it is a BIDS channel tsv, load the tsv,sd_t_f_vals + %else read the file that is assumed to be a transformation matrix. + else + EEG = warp_locs( EEG,'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc', ... + 'transform',[[0.500 -22.000 -48.000 -0.065 0.000 -1.580 1060.000 1260.000 1220.000]], ... + 'manual','off'); + end +end + +% Apply average re-reference +%chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +%chan_m = mean(EEG.data(chan_inds,:),1); +%chan_m_mat = repmat(chan_m,size(EEG.data,1),1); +%EEG.data = EEG.data - chan_m_mat; +%clear chan_m_mat; + +% Window the continuous data +logging_log('INFO', 'Windowing the continous data...'); +EEG = marks_continuous2epochs(EEG,'recurrence',[1],'limits',[[0 1]]); +logging_log('INFO', 'LOADED DATASET...'); + +% rereference to selected channels.. +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +epoch_inds = marks_label2index(EEG.marks.time_info,{'manual'},'indexes','invert','on'); +[EEG,trim_ch_sd]=chan_variance(EEG,'data_field','data', ... + 'chan_inds',chan_inds, ... + 'epoch_inds',epoch_inds, ... + 'plot_figs','off'); + +chan_dist=zeros(size(trim_ch_sd)); +for i=1:size(trim_ch_sd,2); + chan_dist(:,i)=(trim_ch_sd(:,i)-median(trim_ch_sd(:,i)))/diff(quantile(trim_ch_sd(:,i),[.3,.7])); +end +mean_chan_dist=mean(chan_dist,2); +m=median(mean_chan_dist); +q=quantile(mean_chan_dist,[.3,.7]); + +refchans=find(mean_chan_distve_trimmean(msr,40,1)+ve_trimstd(msr,40,1)*6); +logging_log('INFO', 'IDENTIFIED BRIDGED CHANNELS...'); + +% Edit the channel flag info structure +logging_log('INFO', 'Updating chflaginfo structure...'); +lnkflags = zeros(EEG.nbchan,1); +lnkflags(chan_inds(flag_b_chan_inds))=1; +EEG.marks = marks_add_label(EEG.marks,'chan_info', ... + {'bridge',[.7,1,.7],[.2,1,.2],-1,lnkflags}); +logging_log('INFO', 'EDITED CHANFLAGINFO STRUCT...'); + +% Combine low_rand bridge chan_info flags into 'manual'... +EEG = pop_marks_merge_labels(EEG,'chan_info',{'low_r','bridge'},'target_label','manual'); +logging_log('INFO', 'COMBINED MARKS STRUCTURE INTO MANUAL FLAGS...'); + +%% FLAG RANK CHAN +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Flags the channel that is the least unique (the best channel to remove prior +% to ICA in order to account for the rereference rank deficiency. + +logging_log('INFO', 'Updating chflaginfo structure...'); +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +[r_max,rank_ind] = max(data_r_ch(chan_inds)); +rankflags = zeros(EEG.nbchan,1); +rankflags(chan_inds(rank_ind))=1; +EEG.marks = marks_add_label(EEG.marks,'chan_info', ... + {'rank',[.1,.1,.24],[.1,.1,.24],-1,rankflags}); +logging_log('INFO', 'EDITED CHANFLAGINFO STRUCT...'); + +%% REREFERENCE TO INTERPOLATED AVERAGE SITE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Rereference the data to an average interpolated site containined 19 channels +logging_log('INFO', 'Rereferencing to averaged interpolated site...'); +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +EEG = interp_ref(EEG,'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020_ll_ref19.elc','chans',chan_inds); +EEG = eeg_checkset(EEG); +logging_log('INFO', 'REREFERENCED TO INTERPOLATED AVERAGE SITE...'); + +%% CALCULATE NEAREST NEIGHBOUR R VALUES +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Similarly to the neighbor r calculation done between channels this section looks +% at the correlation, but between all channels and for epochs of time. Time segmenents +% are flagged for removal. + +logging_log('INFO', 'Calculating nearest neighbour r array for window criteria...'); +chan_inds = marks_label2index(EEG.marks.chan_info,{'manual'},'indexes','invert','on'); +epoch_inds = marks_label2index(EEG.marks.time_info,{'manual'},'indexes','invert','on'); +[EEG,data_r_t,~,~,~] = chan_neighbour_r(EEG, ... + 3,'max', ... + 'chan_inds',chan_inds, ... + 'epoch_inds',epoch_inds, ... + 'plotfigs','off'); +logging_log('INFO', 'CALCULATED NEAREST NEIGHBOUR R VALUES...'); + +% Create the window criteria vector for flagging low_r time_info... +logging_log('INFO', 'Assessing epoch r distributions criteria...') +[~,flag_r_t_inds]=marks_array2flags(data_r_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_dir','neg', ... + 'init_crit',16, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); +logging_log('INFO', 'CREATED EPOCH CRITERIA VECTOR...'); + +% Edit the time flag info structure +logging_log('INFO', 'Updating latflaginfo structure...'); +lowr_epoch_flags = zeros(size(EEG.data(1,:,:))); +lowr_epoch_flags(1,:,epoch_inds(flag_r_t_inds))=1; +EEG.marks = marks_add_label(EEG.marks,'time_info', ... + {'low_r',[0,1,0],lowr_epoch_flags}); +clear lowr_epoch_flags; +logging_log('INFO', 'TIME TO: UPDATE REJECTION STRUCTURE...'); + +% Combine low_r time_info flags into 'manual'... +EEG = pop_marks_merge_labels(EEG,'time_info',{'low_r'},'target_label','manual'); +logging_log('INFO', 'COMBINED MARKS STRUCTURE INTO MANUAL FLAGS...'); + +% Concatenate epoched data back to continuous data +logging_log('INFO', 'Concatenating windowed data...'); +EEG = marks_epochs2continuous(EEG); +EEG = eeg_checkset(EEG,'eventconsistency'); +logging_log('INFO', 'CONCATENATED THE WINDOWED DATA INTO CONTINUOUS DATA...'); + +EEG=pop_marks_flag_gap(EEG,{'manual'},2000,'mark_gap',[.8,.8,.8],'offsets',[0 0],'ref_point','both'); + +% Combine mark_gap time_info flags into 'manual'... +EEG = pop_marks_merge_labels(EEG,'time_info',{'mark_gap'},'target_label','manual'); +logging_log('INFO', 'COMBINED MARKS STRUCTURE INTO MANUAL FLAGS...'); + +%%%----------------------------------------------------------------------- +%% SAVE sa.set FILE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +logging_log('INFO', 'Saving file: ./sub-010/eeg/sub-010_task-faceFO_sa.set...'); +if ~exist('derivatives/BIDS-Lossless-EEG/./sub-010/eeg','dir'); + mkdir('derivatives/BIDS-Lossless-EEG/./sub-010/eeg'); +end +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_sa.set'); +logging_log('INFO', 'SAVED sa FILE...'); + +%%%----------------------------------------------------------------------- +%% PREPARE FILE FOR AMICA +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Purges the flagged data to make sure it is primed for ICA and creates the amica paramaeter file. + +% Removed flagged channels and time segments +sprintf('%s','Purging flagged channels...\n'); +EEG = pop_marks_select_data(EEG,'channel marks',[],'labels',{'manual','rank'},'remove','on'); +EEG = pop_marks_select_data(EEG,'time marks',[],'labels',{'manual'},'remove','on'); +EEG = eeg_checkset(EEG); +logging_log('INFO', 'TIME TO: PURGE DATA...'); + +% Make sure EEG.data is doubles for use in amica +EEG.data = double(EEG.data); + +% Save the SA purge file +logging_log('INFO',sprintf('%s','Saving file: derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_sa_purge.set...')); +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_sa_purge.set'); + +% Save diagnostic arrays +try OCTAVE_VERSION; + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_data_sd_t.mat','data_sd_t'); + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_data_sd_ch.mat','data_sd_ch'); + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_data_r_ch.mat','data_r_ch'); + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_data_r_t.mat','data_r_t'); +catch + save('derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_data_sd_t.mat','data_sd_t'); + save('derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_data_sd_ch.mat','data_sd_ch'); + save('derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_data_r_ch.mat','data_r_ch'); + save('derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_data_r_t.mat','data_r_t'); +end + +% Save the AMICA parameter file +swapstr = file_strswap('derivatives/BIDS-Lossless-EEG/code/misc/amica15_default.param', ... + '[repstr_fdt_fname]','derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_sa_purge.fdt', ... + '[repstr_outpath]','derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_amicaout_init', ... + '[repstr_nbchan]', num2str(EEG.nbchan), ... + '[repstr_pnts]', sprintf('%12.0f',EEG.pnts), ... + '[repstr_tpp]', '8', ... + '[repstr_pca]', num2str(EEG.nbchan)); +fidparam = fopen('derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_init.param','w'); +fprintf(fidparam,'%s',swapstr); +fclose(fidparam); + +% CREATE THE AMICA OUTPUT FOLDER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Needed for MPI jobs on cluster only in order to prevent a race effect bug. + +[~] = rmdir('derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_amicaout_init','s'); +mkdir derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_amicaout_init; +logging_log('INFO', 'Created Amica Output Folder'); + +logging_log('INFO', 'Scalpart complete'); +logging_log('INFO', 'Scheduler: sbatch'); +print_chan_sample(EEG); diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s01_scalpart-2019-10-01T14-35-11/submit.sh b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s01_scalpart-2019-10-01T14-35-11/submit.sh new file mode 100644 index 000000000..502ca50bf --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s01_scalpart-2019-10-01T14-35-11/submit.sh @@ -0,0 +1,24 @@ +#!/bin/bash +set -e +module load gcc/7.3.0; +module load octave/4.4.1; + +sbatch --job-name=s01_scalpart_sub-001_task-faceFO_eeg --output=derivatives/BIDS-Lossless-EEG/log/s01_scalpart-2019-10-01T14-35-11/s01_scalpart_sub-001_task-faceFO_eeg.log --mem-per-cpu=16000m --time=00:30:00 --account=def-bucanl <(echo -e '#!/bin/bash\noctave derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/batch_context/batch/exec_func/octave_exit_wrapper.m derivatives/BIDS-Lossless-EEG/log/s01_scalpart-2019-10-01T14-35-11/s01_scalpart_sub_001_task_faceFO_eeg.m') + +sbatch --job-name=s01_scalpart_sub-002_task-faceFO_eeg --output=derivatives/BIDS-Lossless-EEG/log/s01_scalpart-2019-10-01T14-35-11/s01_scalpart_sub-002_task-faceFO_eeg.log --mem-per-cpu=16000m --time=00:30:00 --account=def-bucanl <(echo -e '#!/bin/bash\noctave derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/batch_context/batch/exec_func/octave_exit_wrapper.m derivatives/BIDS-Lossless-EEG/log/s01_scalpart-2019-10-01T14-35-11/s01_scalpart_sub_002_task_faceFO_eeg.m') + +sbatch --job-name=s01_scalpart_sub-003_task-faceFO_eeg --output=derivatives/BIDS-Lossless-EEG/log/s01_scalpart-2019-10-01T14-35-11/s01_scalpart_sub-003_task-faceFO_eeg.log --mem-per-cpu=16000m --time=00:30:00 --account=def-bucanl <(echo -e '#!/bin/bash\noctave derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/batch_context/batch/exec_func/octave_exit_wrapper.m derivatives/BIDS-Lossless-EEG/log/s01_scalpart-2019-10-01T14-35-11/s01_scalpart_sub_003_task_faceFO_eeg.m') + +sbatch --job-name=s01_scalpart_sub-004_task-faceFO_eeg --output=derivatives/BIDS-Lossless-EEG/log/s01_scalpart-2019-10-01T14-35-11/s01_scalpart_sub-004_task-faceFO_eeg.log --mem-per-cpu=16000m --time=00:30:00 --account=def-bucanl <(echo -e '#!/bin/bash\noctave derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/batch_context/batch/exec_func/octave_exit_wrapper.m derivatives/BIDS-Lossless-EEG/log/s01_scalpart-2019-10-01T14-35-11/s01_scalpart_sub_004_task_faceFO_eeg.m') + +sbatch --job-name=s01_scalpart_sub-005_task-faceFO_eeg --output=derivatives/BIDS-Lossless-EEG/log/s01_scalpart-2019-10-01T14-35-11/s01_scalpart_sub-005_task-faceFO_eeg.log --mem-per-cpu=16000m --time=00:30:00 --account=def-bucanl <(echo -e '#!/bin/bash\noctave derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/batch_context/batch/exec_func/octave_exit_wrapper.m derivatives/BIDS-Lossless-EEG/log/s01_scalpart-2019-10-01T14-35-11/s01_scalpart_sub_005_task_faceFO_eeg.m') + +sbatch --job-name=s01_scalpart_sub-006_task-faceFO_eeg --output=derivatives/BIDS-Lossless-EEG/log/s01_scalpart-2019-10-01T14-35-11/s01_scalpart_sub-006_task-faceFO_eeg.log --mem-per-cpu=16000m --time=00:30:00 --account=def-bucanl <(echo -e '#!/bin/bash\noctave derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/batch_context/batch/exec_func/octave_exit_wrapper.m derivatives/BIDS-Lossless-EEG/log/s01_scalpart-2019-10-01T14-35-11/s01_scalpart_sub_006_task_faceFO_eeg.m') + +sbatch --job-name=s01_scalpart_sub-007_task-faceFO_eeg --output=derivatives/BIDS-Lossless-EEG/log/s01_scalpart-2019-10-01T14-35-11/s01_scalpart_sub-007_task-faceFO_eeg.log --mem-per-cpu=16000m --time=00:30:00 --account=def-bucanl <(echo -e '#!/bin/bash\noctave derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/batch_context/batch/exec_func/octave_exit_wrapper.m derivatives/BIDS-Lossless-EEG/log/s01_scalpart-2019-10-01T14-35-11/s01_scalpart_sub_007_task_faceFO_eeg.m') + +sbatch --job-name=s01_scalpart_sub-008_task-faceFO_eeg --output=derivatives/BIDS-Lossless-EEG/log/s01_scalpart-2019-10-01T14-35-11/s01_scalpart_sub-008_task-faceFO_eeg.log --mem-per-cpu=16000m --time=00:30:00 --account=def-bucanl <(echo -e '#!/bin/bash\noctave derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/batch_context/batch/exec_func/octave_exit_wrapper.m derivatives/BIDS-Lossless-EEG/log/s01_scalpart-2019-10-01T14-35-11/s01_scalpart_sub_008_task_faceFO_eeg.m') + +sbatch --job-name=s01_scalpart_sub-009_task-faceFO_eeg --output=derivatives/BIDS-Lossless-EEG/log/s01_scalpart-2019-10-01T14-35-11/s01_scalpart_sub-009_task-faceFO_eeg.log --mem-per-cpu=16000m --time=00:30:00 --account=def-bucanl <(echo -e '#!/bin/bash\noctave derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/batch_context/batch/exec_func/octave_exit_wrapper.m derivatives/BIDS-Lossless-EEG/log/s01_scalpart-2019-10-01T14-35-11/s01_scalpart_sub_009_task_faceFO_eeg.m') + +sbatch --job-name=s01_scalpart_sub-010_task-faceFO_eeg 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This will help make the next +% components stronger. The log likelihood of the model is also flagged for +% reference. Sections marked for high SD often have low log likelihoods +% as well. It then prepares the required parameter files for the parrallel amicas ABC. +% +%% From Config key_strswap Description +%----------------------------------------------------------------------------------------------------------------------------- +% data_path = derivatives/BIDS-Lossless-EEG Path to input and output data files assuming cd = work_path +% recur_sec = 1 Recurrence (sec) for artifact detection epoching (e.g. .3) +% limit_sec = [0 1] Limits (sec) for artifact detection epoching (e.g. [-.3 .3]) +% sd_t_meth = q Method used for flagging epochs (e.g. 'q' (quantiles), or 'na' (default)) +% sd_t_vals = [.3 .7] Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7]) +% sd_t_o = 6 z threshold for flagging epochs during standard deviation criteria (e.g. 6) +% sd_t_f_meth = fixed Fixed method used for flagging epochs (should be 'fixed') +% sd_t_f_vals = Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% sd_t_f_o = .2 z threshold for flagging epochs during fixed standard deviation criteria (e.g. .2) +% out_path = derivatives/BIDS-Lossless-EEG Relative path to output data files assuming cd = work_path +% min_gap_ms = 2000 Minimum time (ms) to allow between periods marked for rejection +% amica_param_file = derivatives/BIDS-Lossless-EEG/code/misc/amica_default.param template amicadefs.param file to modify +% amica_threads_s04 = 8 number of threads to use for running s04a, s04b, s04c amica scripts (Default: 8) + +%% LOAD SA DATASET +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Loads the batched EEG datafile and the AMICA output files. Since ICA is +% done on a channel subset the components need to be returned to the +% correct indicies. + +% Load the EEG datafile +logging_log('INFO', 'Loading set file: ./sub-001/eeg/sub-001_task-faceFO_sa.set...'); +tic; +EEG = pop_loadset('filepath','derivatives/BIDS-Lossless-EEG','filename','./sub-001/eeg/sub-001_task-faceFO_sa.set'); +EEG = eeg_checkset( EEG ); +logging_log('INFO', 'TIME TO: LOAD DATASET...'); +toc + +% Prepare returning components +EEG.icachansind = marks_label2index(EEG.marks.chan_info,{'manual','rank'},'indices','invert','on'); +icatimeind = find(~EEG.marks.time_info(1).flags); + +% Load AMICA Model 1 +tic; +EEG.amica(1).models=loadmodout15('derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_amicaout_init'); +EEG.icaweights=EEG.amica(1).models.W; +EEG.icasphere=EEG.amica(1).models.S(1:EEG.amica(1).models.num_pcs,:); +EEG=eeg_checkset(EEG); +logging_log('INFO', 'TIME TO: LOAD ICA INFO...'); +toc +%% PASS EEGDATA THROUGH THE AMICA MATRIX +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%Uses the ICA weights and the channel data to recreate the ICA components. + +tic; +% In place of EEG checkset +tmpdata = EEG.data(EEG.icachansind,:); +tmpindices = find(~sum(isnan(tmpdata))); +EEG.icaact = zeros(size(EEG.icaweights,1), size(tmpdata,2)); EEG.icaact(:) = NaN; +EEG.icaact(:,tmpindices) = (EEG.icaweights*EEG.icasphere)*tmpdata(:,tmpindices); +clear tmpdata; + +%% LOG LIKELIHOOD FLAGS +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%Creates marks based off of the log likelihood of the ICA model to the data. +% Create initial log likelihood marks + +logl_init_flags=zeros(size(EEG.data(1,:))); +logl_init_flag_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indices','invert','on'); +logl_init_flags(logl_init_flag_inds)=(EEG.amica(1).models.Lt-min(EEG.amica(1).models.Lt))/(max(EEG.amica(1).models.Lt)-min(EEG.amica(1).models.Lt)); + +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'logl_init',[1,0.45,0],logl_init_flags}); +logging_log('INFO', 'TIME TO: RECREATE ICA COMPONENTS...'); +toc + +% Window the continous Data +logging_log('INFO', 'Windowing the continous data...'); +tic; +EEG=marks_continuous2epochs(EEG,'recurrence',1,'limits',[0 1]); +logging_log('INFO', 'TIME TO: WINDOW THE CONTINUOUS DATA...'); +toc +%% CALCULATE ICAACT SD +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates the IC standard Deviation by epoch window. Flags windows with +% too much standard deviation. + +% Calculate IC sd by window +logging_log('INFO', 'Calculating the IC sd array for window criteria...'); +tic; +epoch_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indexes','invert','on'); +[EEG,icaact_sd1_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'plot_figs','off'); + +% Create the windowing sd criteria +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_t_inds]=marks_array2flags(icaact_sd1_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +% Update marks rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +icsd_epoch_flags=zeros(size(EEG.data(1,:,:))); +icsd_epoch_flags(1,:,epoch_inds(flag_t_inds))=1; +icsd_epoch_flags=padflags(EEG,icsd_epoch_flags,1,'value',.5); +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_sd1',[.4,.6,.6],icsd_epoch_flags}); +logging_log('INFO', 'TIME TO: UPDATE REJECTION STRUCTURE WITH IC SD MARKS...'); +toc + +% Concat the windowed data back to continuous +logging_log('INFO', 'Concatenating windowed data...'); +tic; +EEG=marks_epochs2continuous(EEG); +EEG=eeg_checkset(EEG,'eventconsistency'); +logging_log('INFO', 'TIME TO: CONCATENATE THE WINDOWED DATA INTO CONTINUOUS DATA...'); +toc +%% FLAG GAPS +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Flags small gaps between rejection flags so that there are not as many +% short segments to be analyzed + +% Mark any flag gaps < 2 sec. +tic; +EEG=pop_marks_flag_gap(EEG,{'manual','mark_gap','ic_sd1'}, ... + 2000,'mark_gap',[.8,.8,.8],'offsets',[0 0],'ref_point','both'); + +%%COMBINE MARKS STRUCTURE INTO MANUAL FLAGS... +EEG=pop_marks_merge_labels(EEG,'time_info',{'manual','ic_sd1','mark_gap'},'target_label','manual'); +logging_log('INFO', 'TIME TO: FLAG GAPS AND COMBINE MARKS STRUCTURE INTO MANUAL FLAGS...'); +toc +%% SAVE compart_data.set FILE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% A saved copy is made before the file is purged to its ready for ICA +% version. + +tic; +logging_log('INFO', 'Saving file: ./sub-001/eeg/sub-001_task-faceFO_compart_data.set...'); +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_compart_data.set'); +logging_log('INFO', 'TIME TO: SAVE compart_data FILE...'); +toc; +%% SAVE compart_data_purge FILE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% All EEG parts associated with ICA need to be cleared as models seem to +% adapt based on previous data. Old and New flagged channels/time need to +% be purged again in preparation of ICA. + +% Clearing ICA Data +EEG.icaact = []; +EEG.icawinv = []; +EEG.icasphere = []; +EEG.icaweights = []; +EEG.icachansind = []; +EEG.amica = []; +logging_log('INFO', 'ICA data Reset'); + +% Removing Flagged Channels +tic; +sprintf('%s','Purging flagged channels...\n'); +EEG=pop_marks_select_data(EEG,'channel marks',[],'labels',{'manual','rank'},'remove','on'); +EEG=pop_marks_select_data(EEG,'time marks',[],'labels',{'manual'},'remove','on'); +EEG=eeg_checkset(EEG); +logging_log('INFO', 'TIME TO: PURGE DATA...'); +toc; + +% Saving File to enter next ICAs +logging_log('INFO', 'Saving the Param files'); +sprintf('%s','Saving file: ./sub-001/eeg/sub-001_task-faceFO_compart_data_purge.set...\n'); +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_compart_data_purge.set'); + +% Save diagnostic arrays +try OCTAVE_VERSION; + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_icaact_sd1_t.mat','icaact_sd1_t'); +catch + save('derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_icaact_sd1_t.mat','icaact_sd1_t'); +end + +%% SAVE AMICA PARAMETER FILES +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% A parameter file need to be made for each of the ICAs running after this +% script. In This case AMICA A, B and C are run. + +tic; +% A +% Saving AMICA Parameter File +swapstr=file_strswap('derivatives/BIDS-Lossless-EEG/code/misc/amica_default.param', ... + '[repstr_fdt_fname]','derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_compart_data_purge.fdt', ... + '[repstr_outpath]','derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_amicaout_A', ... + '[repstr_nbchan]', num2str(EEG.nbchan), ... + '[repstr_pnts]', sprintf('%12.0f',EEG.pnts), ... + '[repstr_tpp]', '8', ... + '[repstr_pca]', num2str(EEG.nbchan)); +fidparam=fopen('derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_A.param','w'); +fprintf(fidparam,'%s',swapstr); +fclose(fidparam); + +% B +% Saving AMICA Parameter File +swapstr=file_strswap('derivatives/BIDS-Lossless-EEG/code/misc/amica_default.param', ... + '[repstr_fdt_fname]','derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_compart_data_purge.fdt', ... + '[repstr_outpath]','derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_amicaout_B', ... + '[repstr_nbchan]', num2str(EEG.nbchan), ... + '[repstr_pnts]', sprintf('%12.0f',EEG.pnts), ... + '[repstr_tpp]', '8', ... + '[repstr_pca]', num2str(EEG.nbchan)); +fidparam=fopen('derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_B.param','w'); +fprintf(fidparam,'%s',swapstr); +fclose(fidparam); + +% C +% Saving AMICA Parameter File +swapstr=file_strswap('derivatives/BIDS-Lossless-EEG/code/misc/amica_default.param', ... + '[repstr_fdt_fname]','derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_compart_data_purge.fdt', ... + '[repstr_outpath]','derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_amicaout_C', ... + '[repstr_nbchan]', num2str(EEG.nbchan), ... + '[repstr_pnts]', sprintf('%12.0f',EEG.pnts), ... + '[repstr_tpp]', '8', ... + '[repstr_pca]', num2str(EEG.nbchan)); +fidparam=fopen('derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_C.param','w'); +fprintf(fidparam,'%s',swapstr); +fclose(fidparam); + +%% Create the Amica Output Folder +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Needed for MPI jobs on cluster. Otherwise race effect will occur. + +[~] = rmdir('derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_amicaout_A','s'); +[~] = rmdir('derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_amicaout_B','s'); +[~] = rmdir('derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_amicaout_C','s'); + + +mkdir 'derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_amicaout_A'; +mkdir 'derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_amicaout_B'; +mkdir 'derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_amicaout_C'; + +logging_log('INFO', 'TIME TO: CREATE AMICA PARAMETER FILES...'); +toc + +% FINISH +logging_log('INFO', '***********COMPART DATA COMPLETE!**********'); +logging_log('INFO', 'Scheduler: sbatch'); +print_chan_sample(EEG); diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/s03_compart_data_sub_002_task_faceFO_eeg.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/s03_compart_data_sub_002_task_faceFO_eeg.m new file mode 100644 index 000000000..195ff8a83 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/s03_compart_data_sub_002_task_faceFO_eeg.m @@ -0,0 +1,285 @@ +warning off; + +pkg load signal; +pkg load parallel; +pkg load io; + +file_path = fileparts(mfilename('fullpath')); + +addpath([file_path '/../../code/dependencies/eeglab_lossless'], ... + [file_path '/../../code/dependencies/matlog'], ... + [file_path '/../../code/tools']); +cd '/scratch/tk11br/Face13/'; + +% eeglab auto loads the path correctly instead of shotgun blasting with +% addpath(genpath(<>)) as we were doing prior +if ~exist('EEG', 'var') + eeglab; +end +% Use matlog disp function override +logging_override_disp(true); +% Use matlog INFO level debugging +logging_set_log_level('INFO') + +% Don't run with traditional but enable some of the options +save_default_options ('-mat-binary'); +confirm_recursive_rmdir(false); + +% This makes | and & || and && in earlier version of Octave where +% | and & dont short curcuit by default +if exist('do_braindead_shortcircuit_evaluation', 'builtin') + do_braindead_shortcircuit_evaluation(true); +end + +% Options +pop_editoptions('option_savetwofiles', 1,'option_single',0); + +%% SCRIPT DESCRIPTION +% This script is loads the results of a preliminary ICA and uses the +% standard deviation of the components to flag and remove data time that +% does not fit a well behaved model. This will help make the next +% components stronger. The log likelihood of the model is also flagged for +% reference. Sections marked for high SD often have low log likelihoods +% as well. It then prepares the required parameter files for the parrallel amicas ABC. +% +%% From Config key_strswap Description +%----------------------------------------------------------------------------------------------------------------------------- +% data_path = derivatives/BIDS-Lossless-EEG Path to input and output data files assuming cd = work_path +% recur_sec = 1 Recurrence (sec) for artifact detection epoching (e.g. .3) +% limit_sec = [0 1] Limits (sec) for artifact detection epoching (e.g. [-.3 .3]) +% sd_t_meth = q Method used for flagging epochs (e.g. 'q' (quantiles), or 'na' (default)) +% sd_t_vals = [.3 .7] Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7]) +% sd_t_o = 6 z threshold for flagging epochs during standard deviation criteria (e.g. 6) +% sd_t_f_meth = fixed Fixed method used for flagging epochs (should be 'fixed') +% sd_t_f_vals = Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% sd_t_f_o = .2 z threshold for flagging epochs during fixed standard deviation criteria (e.g. .2) +% out_path = derivatives/BIDS-Lossless-EEG Relative path to output data files assuming cd = work_path +% min_gap_ms = 2000 Minimum time (ms) to allow between periods marked for rejection +% amica_param_file = derivatives/BIDS-Lossless-EEG/code/misc/amica_default.param template amicadefs.param file to modify +% amica_threads_s04 = 8 number of threads to use for running s04a, s04b, s04c amica scripts (Default: 8) + +%% LOAD SA DATASET +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Loads the batched EEG datafile and the AMICA output files. Since ICA is +% done on a channel subset the components need to be returned to the +% correct indicies. + +% Load the EEG datafile +logging_log('INFO', 'Loading set file: ./sub-002/eeg/sub-002_task-faceFO_sa.set...'); +tic; +EEG = pop_loadset('filepath','derivatives/BIDS-Lossless-EEG','filename','./sub-002/eeg/sub-002_task-faceFO_sa.set'); +EEG = eeg_checkset( EEG ); +logging_log('INFO', 'TIME TO: LOAD DATASET...'); +toc + +% Prepare returning components +EEG.icachansind = marks_label2index(EEG.marks.chan_info,{'manual','rank'},'indices','invert','on'); +icatimeind = find(~EEG.marks.time_info(1).flags); + +% Load AMICA Model 1 +tic; +EEG.amica(1).models=loadmodout15('derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_amicaout_init'); +EEG.icaweights=EEG.amica(1).models.W; +EEG.icasphere=EEG.amica(1).models.S(1:EEG.amica(1).models.num_pcs,:); +EEG=eeg_checkset(EEG); +logging_log('INFO', 'TIME TO: LOAD ICA INFO...'); +toc +%% PASS EEGDATA THROUGH THE AMICA MATRIX +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%Uses the ICA weights and the channel data to recreate the ICA components. + +tic; +% In place of EEG checkset +tmpdata = EEG.data(EEG.icachansind,:); +tmpindices = find(~sum(isnan(tmpdata))); +EEG.icaact = zeros(size(EEG.icaweights,1), size(tmpdata,2)); EEG.icaact(:) = NaN; +EEG.icaact(:,tmpindices) = (EEG.icaweights*EEG.icasphere)*tmpdata(:,tmpindices); +clear tmpdata; + +%% LOG LIKELIHOOD FLAGS +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%Creates marks based off of the log likelihood of the ICA model to the data. +% Create initial log likelihood marks + +logl_init_flags=zeros(size(EEG.data(1,:))); +logl_init_flag_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indices','invert','on'); +logl_init_flags(logl_init_flag_inds)=(EEG.amica(1).models.Lt-min(EEG.amica(1).models.Lt))/(max(EEG.amica(1).models.Lt)-min(EEG.amica(1).models.Lt)); + +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'logl_init',[1,0.45,0],logl_init_flags}); +logging_log('INFO', 'TIME TO: RECREATE ICA COMPONENTS...'); +toc + +% Window the continous Data +logging_log('INFO', 'Windowing the continous data...'); +tic; +EEG=marks_continuous2epochs(EEG,'recurrence',1,'limits',[0 1]); +logging_log('INFO', 'TIME TO: WINDOW THE CONTINUOUS DATA...'); +toc +%% CALCULATE ICAACT SD +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates the IC standard Deviation by epoch window. Flags windows with +% too much standard deviation. + +% Calculate IC sd by window +logging_log('INFO', 'Calculating the IC sd array for window criteria...'); +tic; +epoch_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indexes','invert','on'); +[EEG,icaact_sd1_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'plot_figs','off'); + +% Create the windowing sd criteria +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_t_inds]=marks_array2flags(icaact_sd1_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +% Update marks rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +icsd_epoch_flags=zeros(size(EEG.data(1,:,:))); +icsd_epoch_flags(1,:,epoch_inds(flag_t_inds))=1; +icsd_epoch_flags=padflags(EEG,icsd_epoch_flags,1,'value',.5); +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_sd1',[.4,.6,.6],icsd_epoch_flags}); +logging_log('INFO', 'TIME TO: UPDATE REJECTION STRUCTURE WITH IC SD MARKS...'); +toc + +% Concat the windowed data back to continuous +logging_log('INFO', 'Concatenating windowed data...'); +tic; +EEG=marks_epochs2continuous(EEG); +EEG=eeg_checkset(EEG,'eventconsistency'); +logging_log('INFO', 'TIME TO: CONCATENATE THE WINDOWED DATA INTO CONTINUOUS DATA...'); +toc +%% FLAG GAPS +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Flags small gaps between rejection flags so that there are not as many +% short segments to be analyzed + +% Mark any flag gaps < 2 sec. +tic; +EEG=pop_marks_flag_gap(EEG,{'manual','mark_gap','ic_sd1'}, ... + 2000,'mark_gap',[.8,.8,.8],'offsets',[0 0],'ref_point','both'); + +%%COMBINE MARKS STRUCTURE INTO MANUAL FLAGS... +EEG=pop_marks_merge_labels(EEG,'time_info',{'manual','ic_sd1','mark_gap'},'target_label','manual'); +logging_log('INFO', 'TIME TO: FLAG GAPS AND COMBINE MARKS STRUCTURE INTO MANUAL FLAGS...'); +toc +%% SAVE compart_data.set FILE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% A saved copy is made before the file is purged to its ready for ICA +% version. + +tic; +logging_log('INFO', 'Saving file: ./sub-002/eeg/sub-002_task-faceFO_compart_data.set...'); +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_compart_data.set'); +logging_log('INFO', 'TIME TO: SAVE compart_data FILE...'); +toc; +%% SAVE compart_data_purge FILE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% All EEG parts associated with ICA need to be cleared as models seem to +% adapt based on previous data. Old and New flagged channels/time need to +% be purged again in preparation of ICA. + +% Clearing ICA Data +EEG.icaact = []; +EEG.icawinv = []; +EEG.icasphere = []; +EEG.icaweights = []; +EEG.icachansind = []; +EEG.amica = []; +logging_log('INFO', 'ICA data Reset'); + +% Removing Flagged Channels +tic; +sprintf('%s','Purging flagged channels...\n'); +EEG=pop_marks_select_data(EEG,'channel marks',[],'labels',{'manual','rank'},'remove','on'); +EEG=pop_marks_select_data(EEG,'time marks',[],'labels',{'manual'},'remove','on'); +EEG=eeg_checkset(EEG); +logging_log('INFO', 'TIME TO: PURGE DATA...'); +toc; + +% Saving File to enter next ICAs +logging_log('INFO', 'Saving the Param files'); +sprintf('%s','Saving file: ./sub-002/eeg/sub-002_task-faceFO_compart_data_purge.set...\n'); +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_compart_data_purge.set'); + +% Save diagnostic arrays +try OCTAVE_VERSION; + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_icaact_sd1_t.mat','icaact_sd1_t'); +catch + save('derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_icaact_sd1_t.mat','icaact_sd1_t'); +end + +%% SAVE AMICA PARAMETER FILES +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% A parameter file need to be made for each of the ICAs running after this +% script. In This case AMICA A, B and C are run. + +tic; +% A +% Saving AMICA Parameter File +swapstr=file_strswap('derivatives/BIDS-Lossless-EEG/code/misc/amica_default.param', ... + '[repstr_fdt_fname]','derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_compart_data_purge.fdt', ... + '[repstr_outpath]','derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_amicaout_A', ... + '[repstr_nbchan]', num2str(EEG.nbchan), ... + '[repstr_pnts]', sprintf('%12.0f',EEG.pnts), ... + '[repstr_tpp]', '8', ... + '[repstr_pca]', num2str(EEG.nbchan)); +fidparam=fopen('derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_A.param','w'); +fprintf(fidparam,'%s',swapstr); +fclose(fidparam); + +% B +% Saving AMICA Parameter File +swapstr=file_strswap('derivatives/BIDS-Lossless-EEG/code/misc/amica_default.param', ... + '[repstr_fdt_fname]','derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_compart_data_purge.fdt', ... + '[repstr_outpath]','derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_amicaout_B', ... + '[repstr_nbchan]', num2str(EEG.nbchan), ... + '[repstr_pnts]', sprintf('%12.0f',EEG.pnts), ... + '[repstr_tpp]', '8', ... + '[repstr_pca]', num2str(EEG.nbchan)); +fidparam=fopen('derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_B.param','w'); +fprintf(fidparam,'%s',swapstr); +fclose(fidparam); + +% C +% Saving AMICA Parameter File +swapstr=file_strswap('derivatives/BIDS-Lossless-EEG/code/misc/amica_default.param', ... + '[repstr_fdt_fname]','derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_compart_data_purge.fdt', ... + '[repstr_outpath]','derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_amicaout_C', ... + '[repstr_nbchan]', num2str(EEG.nbchan), ... + '[repstr_pnts]', sprintf('%12.0f',EEG.pnts), ... + '[repstr_tpp]', '8', ... + '[repstr_pca]', num2str(EEG.nbchan)); +fidparam=fopen('derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_C.param','w'); +fprintf(fidparam,'%s',swapstr); +fclose(fidparam); + +%% Create the Amica Output Folder +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Needed for MPI jobs on cluster. Otherwise race effect will occur. + +[~] = rmdir('derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_amicaout_A','s'); +[~] = rmdir('derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_amicaout_B','s'); +[~] = rmdir('derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_amicaout_C','s'); + + +mkdir 'derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_amicaout_A'; +mkdir 'derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_amicaout_B'; +mkdir 'derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_amicaout_C'; + +logging_log('INFO', 'TIME TO: CREATE AMICA PARAMETER FILES...'); +toc + +% FINISH +logging_log('INFO', '***********COMPART DATA COMPLETE!**********'); +logging_log('INFO', 'Scheduler: sbatch'); +print_chan_sample(EEG); diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/s03_compart_data_sub_003_task_faceFO_eeg.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/s03_compart_data_sub_003_task_faceFO_eeg.m new file mode 100644 index 000000000..a503acb22 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/s03_compart_data_sub_003_task_faceFO_eeg.m @@ -0,0 +1,285 @@ +warning off; + +pkg load signal; +pkg load parallel; +pkg load io; + +file_path = fileparts(mfilename('fullpath')); + +addpath([file_path '/../../code/dependencies/eeglab_lossless'], ... + [file_path '/../../code/dependencies/matlog'], ... + [file_path '/../../code/tools']); +cd '/scratch/tk11br/Face13/'; + +% eeglab auto loads the path correctly instead of shotgun blasting with +% addpath(genpath(<>)) as we were doing prior +if ~exist('EEG', 'var') + eeglab; +end +% Use matlog disp function override +logging_override_disp(true); +% Use matlog INFO level debugging +logging_set_log_level('INFO') + +% Don't run with traditional but enable some of the options +save_default_options ('-mat-binary'); +confirm_recursive_rmdir(false); + +% This makes | and & || and && in earlier version of Octave where +% | and & dont short curcuit by default +if exist('do_braindead_shortcircuit_evaluation', 'builtin') + do_braindead_shortcircuit_evaluation(true); +end + +% Options +pop_editoptions('option_savetwofiles', 1,'option_single',0); + +%% SCRIPT DESCRIPTION +% This script is loads the results of a preliminary ICA and uses the +% standard deviation of the components to flag and remove data time that +% does not fit a well behaved model. This will help make the next +% components stronger. The log likelihood of the model is also flagged for +% reference. Sections marked for high SD often have low log likelihoods +% as well. It then prepares the required parameter files for the parrallel amicas ABC. +% +%% From Config key_strswap Description +%----------------------------------------------------------------------------------------------------------------------------- +% data_path = derivatives/BIDS-Lossless-EEG Path to input and output data files assuming cd = work_path +% recur_sec = 1 Recurrence (sec) for artifact detection epoching (e.g. .3) +% limit_sec = [0 1] Limits (sec) for artifact detection epoching (e.g. [-.3 .3]) +% sd_t_meth = q Method used for flagging epochs (e.g. 'q' (quantiles), or 'na' (default)) +% sd_t_vals = [.3 .7] Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7]) +% sd_t_o = 6 z threshold for flagging epochs during standard deviation criteria (e.g. 6) +% sd_t_f_meth = fixed Fixed method used for flagging epochs (should be 'fixed') +% sd_t_f_vals = Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% sd_t_f_o = .2 z threshold for flagging epochs during fixed standard deviation criteria (e.g. .2) +% out_path = derivatives/BIDS-Lossless-EEG Relative path to output data files assuming cd = work_path +% min_gap_ms = 2000 Minimum time (ms) to allow between periods marked for rejection +% amica_param_file = derivatives/BIDS-Lossless-EEG/code/misc/amica_default.param template amicadefs.param file to modify +% amica_threads_s04 = 8 number of threads to use for running s04a, s04b, s04c amica scripts (Default: 8) + +%% LOAD SA DATASET +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Loads the batched EEG datafile and the AMICA output files. Since ICA is +% done on a channel subset the components need to be returned to the +% correct indicies. + +% Load the EEG datafile +logging_log('INFO', 'Loading set file: ./sub-003/eeg/sub-003_task-faceFO_sa.set...'); +tic; +EEG = pop_loadset('filepath','derivatives/BIDS-Lossless-EEG','filename','./sub-003/eeg/sub-003_task-faceFO_sa.set'); +EEG = eeg_checkset( EEG ); +logging_log('INFO', 'TIME TO: LOAD DATASET...'); +toc + +% Prepare returning components +EEG.icachansind = marks_label2index(EEG.marks.chan_info,{'manual','rank'},'indices','invert','on'); +icatimeind = find(~EEG.marks.time_info(1).flags); + +% Load AMICA Model 1 +tic; +EEG.amica(1).models=loadmodout15('derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_amicaout_init'); +EEG.icaweights=EEG.amica(1).models.W; +EEG.icasphere=EEG.amica(1).models.S(1:EEG.amica(1).models.num_pcs,:); +EEG=eeg_checkset(EEG); +logging_log('INFO', 'TIME TO: LOAD ICA INFO...'); +toc +%% PASS EEGDATA THROUGH THE AMICA MATRIX +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%Uses the ICA weights and the channel data to recreate the ICA components. + +tic; +% In place of EEG checkset +tmpdata = EEG.data(EEG.icachansind,:); +tmpindices = find(~sum(isnan(tmpdata))); +EEG.icaact = zeros(size(EEG.icaweights,1), size(tmpdata,2)); EEG.icaact(:) = NaN; +EEG.icaact(:,tmpindices) = (EEG.icaweights*EEG.icasphere)*tmpdata(:,tmpindices); +clear tmpdata; + +%% LOG LIKELIHOOD FLAGS +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%Creates marks based off of the log likelihood of the ICA model to the data. +% Create initial log likelihood marks + +logl_init_flags=zeros(size(EEG.data(1,:))); +logl_init_flag_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indices','invert','on'); +logl_init_flags(logl_init_flag_inds)=(EEG.amica(1).models.Lt-min(EEG.amica(1).models.Lt))/(max(EEG.amica(1).models.Lt)-min(EEG.amica(1).models.Lt)); + +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'logl_init',[1,0.45,0],logl_init_flags}); +logging_log('INFO', 'TIME TO: RECREATE ICA COMPONENTS...'); +toc + +% Window the continous Data +logging_log('INFO', 'Windowing the continous data...'); +tic; +EEG=marks_continuous2epochs(EEG,'recurrence',1,'limits',[0 1]); +logging_log('INFO', 'TIME TO: WINDOW THE CONTINUOUS DATA...'); +toc +%% CALCULATE ICAACT SD +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates the IC standard Deviation by epoch window. Flags windows with +% too much standard deviation. + +% Calculate IC sd by window +logging_log('INFO', 'Calculating the IC sd array for window criteria...'); +tic; +epoch_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indexes','invert','on'); +[EEG,icaact_sd1_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'plot_figs','off'); + +% Create the windowing sd criteria +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_t_inds]=marks_array2flags(icaact_sd1_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +% Update marks rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +icsd_epoch_flags=zeros(size(EEG.data(1,:,:))); +icsd_epoch_flags(1,:,epoch_inds(flag_t_inds))=1; +icsd_epoch_flags=padflags(EEG,icsd_epoch_flags,1,'value',.5); +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_sd1',[.4,.6,.6],icsd_epoch_flags}); +logging_log('INFO', 'TIME TO: UPDATE REJECTION STRUCTURE WITH IC SD MARKS...'); +toc + +% Concat the windowed data back to continuous +logging_log('INFO', 'Concatenating windowed data...'); +tic; +EEG=marks_epochs2continuous(EEG); +EEG=eeg_checkset(EEG,'eventconsistency'); +logging_log('INFO', 'TIME TO: CONCATENATE THE WINDOWED DATA INTO CONTINUOUS DATA...'); +toc +%% FLAG GAPS +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Flags small gaps between rejection flags so that there are not as many +% short segments to be analyzed + +% Mark any flag gaps < 2 sec. +tic; +EEG=pop_marks_flag_gap(EEG,{'manual','mark_gap','ic_sd1'}, ... + 2000,'mark_gap',[.8,.8,.8],'offsets',[0 0],'ref_point','both'); + +%%COMBINE MARKS STRUCTURE INTO MANUAL FLAGS... +EEG=pop_marks_merge_labels(EEG,'time_info',{'manual','ic_sd1','mark_gap'},'target_label','manual'); +logging_log('INFO', 'TIME TO: FLAG GAPS AND COMBINE MARKS STRUCTURE INTO MANUAL FLAGS...'); +toc +%% SAVE compart_data.set FILE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% A saved copy is made before the file is purged to its ready for ICA +% version. + +tic; +logging_log('INFO', 'Saving file: ./sub-003/eeg/sub-003_task-faceFO_compart_data.set...'); +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_compart_data.set'); +logging_log('INFO', 'TIME TO: SAVE compart_data FILE...'); +toc; +%% SAVE compart_data_purge FILE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% All EEG parts associated with ICA need to be cleared as models seem to +% adapt based on previous data. Old and New flagged channels/time need to +% be purged again in preparation of ICA. + +% Clearing ICA Data +EEG.icaact = []; +EEG.icawinv = []; +EEG.icasphere = []; +EEG.icaweights = []; +EEG.icachansind = []; +EEG.amica = []; +logging_log('INFO', 'ICA data Reset'); + +% Removing Flagged Channels +tic; +sprintf('%s','Purging flagged channels...\n'); +EEG=pop_marks_select_data(EEG,'channel marks',[],'labels',{'manual','rank'},'remove','on'); +EEG=pop_marks_select_data(EEG,'time marks',[],'labels',{'manual'},'remove','on'); +EEG=eeg_checkset(EEG); +logging_log('INFO', 'TIME TO: PURGE DATA...'); +toc; + +% Saving File to enter next ICAs +logging_log('INFO', 'Saving the Param files'); +sprintf('%s','Saving file: ./sub-003/eeg/sub-003_task-faceFO_compart_data_purge.set...\n'); +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_compart_data_purge.set'); + +% Save diagnostic arrays +try OCTAVE_VERSION; + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_icaact_sd1_t.mat','icaact_sd1_t'); +catch + save('derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_icaact_sd1_t.mat','icaact_sd1_t'); +end + +%% SAVE AMICA PARAMETER FILES +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% A parameter file need to be made for each of the ICAs running after this +% script. In This case AMICA A, B and C are run. + +tic; +% A +% Saving AMICA Parameter File +swapstr=file_strswap('derivatives/BIDS-Lossless-EEG/code/misc/amica_default.param', ... + '[repstr_fdt_fname]','derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_compart_data_purge.fdt', ... + '[repstr_outpath]','derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_amicaout_A', ... + '[repstr_nbchan]', num2str(EEG.nbchan), ... + '[repstr_pnts]', sprintf('%12.0f',EEG.pnts), ... + '[repstr_tpp]', '8', ... + '[repstr_pca]', num2str(EEG.nbchan)); +fidparam=fopen('derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_A.param','w'); +fprintf(fidparam,'%s',swapstr); +fclose(fidparam); + +% B +% Saving AMICA Parameter File +swapstr=file_strswap('derivatives/BIDS-Lossless-EEG/code/misc/amica_default.param', ... + '[repstr_fdt_fname]','derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_compart_data_purge.fdt', ... + '[repstr_outpath]','derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_amicaout_B', ... + '[repstr_nbchan]', num2str(EEG.nbchan), ... + '[repstr_pnts]', sprintf('%12.0f',EEG.pnts), ... + '[repstr_tpp]', '8', ... + '[repstr_pca]', num2str(EEG.nbchan)); +fidparam=fopen('derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_B.param','w'); +fprintf(fidparam,'%s',swapstr); +fclose(fidparam); + +% C +% Saving AMICA Parameter File +swapstr=file_strswap('derivatives/BIDS-Lossless-EEG/code/misc/amica_default.param', ... + '[repstr_fdt_fname]','derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_compart_data_purge.fdt', ... + '[repstr_outpath]','derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_amicaout_C', ... + '[repstr_nbchan]', num2str(EEG.nbchan), ... + '[repstr_pnts]', sprintf('%12.0f',EEG.pnts), ... + '[repstr_tpp]', '8', ... + '[repstr_pca]', num2str(EEG.nbchan)); +fidparam=fopen('derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_C.param','w'); +fprintf(fidparam,'%s',swapstr); +fclose(fidparam); + +%% Create the Amica Output Folder +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Needed for MPI jobs on cluster. Otherwise race effect will occur. + +[~] = rmdir('derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_amicaout_A','s'); +[~] = rmdir('derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_amicaout_B','s'); +[~] = rmdir('derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_amicaout_C','s'); + + +mkdir 'derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_amicaout_A'; +mkdir 'derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_amicaout_B'; +mkdir 'derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_amicaout_C'; + +logging_log('INFO', 'TIME TO: CREATE AMICA PARAMETER FILES...'); +toc + +% FINISH +logging_log('INFO', '***********COMPART DATA COMPLETE!**********'); +logging_log('INFO', 'Scheduler: sbatch'); +print_chan_sample(EEG); diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/s03_compart_data_sub_004_task_faceFO_eeg.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/s03_compart_data_sub_004_task_faceFO_eeg.m new file mode 100644 index 000000000..eefd79a97 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/s03_compart_data_sub_004_task_faceFO_eeg.m @@ -0,0 +1,285 @@ +warning off; + +pkg load signal; +pkg load parallel; +pkg load io; + +file_path = fileparts(mfilename('fullpath')); + +addpath([file_path '/../../code/dependencies/eeglab_lossless'], ... + [file_path '/../../code/dependencies/matlog'], ... + [file_path '/../../code/tools']); +cd '/scratch/tk11br/Face13/'; + +% eeglab auto loads the path correctly instead of shotgun blasting with +% addpath(genpath(<>)) as we were doing prior +if ~exist('EEG', 'var') + eeglab; +end +% Use matlog disp function override +logging_override_disp(true); +% Use matlog INFO level debugging +logging_set_log_level('INFO') + +% Don't run with traditional but enable some of the options +save_default_options ('-mat-binary'); +confirm_recursive_rmdir(false); + +% This makes | and & || and && in earlier version of Octave where +% | and & dont short curcuit by default +if exist('do_braindead_shortcircuit_evaluation', 'builtin') + do_braindead_shortcircuit_evaluation(true); +end + +% Options +pop_editoptions('option_savetwofiles', 1,'option_single',0); + +%% SCRIPT DESCRIPTION +% This script is loads the results of a preliminary ICA and uses the +% standard deviation of the components to flag and remove data time that +% does not fit a well behaved model. This will help make the next +% components stronger. The log likelihood of the model is also flagged for +% reference. Sections marked for high SD often have low log likelihoods +% as well. It then prepares the required parameter files for the parrallel amicas ABC. +% +%% From Config key_strswap Description +%----------------------------------------------------------------------------------------------------------------------------- +% data_path = derivatives/BIDS-Lossless-EEG Path to input and output data files assuming cd = work_path +% recur_sec = 1 Recurrence (sec) for artifact detection epoching (e.g. .3) +% limit_sec = [0 1] Limits (sec) for artifact detection epoching (e.g. [-.3 .3]) +% sd_t_meth = q Method used for flagging epochs (e.g. 'q' (quantiles), or 'na' (default)) +% sd_t_vals = [.3 .7] Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7]) +% sd_t_o = 6 z threshold for flagging epochs during standard deviation criteria (e.g. 6) +% sd_t_f_meth = fixed Fixed method used for flagging epochs (should be 'fixed') +% sd_t_f_vals = Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% sd_t_f_o = .2 z threshold for flagging epochs during fixed standard deviation criteria (e.g. .2) +% out_path = derivatives/BIDS-Lossless-EEG Relative path to output data files assuming cd = work_path +% min_gap_ms = 2000 Minimum time (ms) to allow between periods marked for rejection +% amica_param_file = derivatives/BIDS-Lossless-EEG/code/misc/amica_default.param template amicadefs.param file to modify +% amica_threads_s04 = 8 number of threads to use for running s04a, s04b, s04c amica scripts (Default: 8) + +%% LOAD SA DATASET +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Loads the batched EEG datafile and the AMICA output files. Since ICA is +% done on a channel subset the components need to be returned to the +% correct indicies. + +% Load the EEG datafile +logging_log('INFO', 'Loading set file: ./sub-004/eeg/sub-004_task-faceFO_sa.set...'); +tic; +EEG = pop_loadset('filepath','derivatives/BIDS-Lossless-EEG','filename','./sub-004/eeg/sub-004_task-faceFO_sa.set'); +EEG = eeg_checkset( EEG ); +logging_log('INFO', 'TIME TO: LOAD DATASET...'); +toc + +% Prepare returning components +EEG.icachansind = marks_label2index(EEG.marks.chan_info,{'manual','rank'},'indices','invert','on'); +icatimeind = find(~EEG.marks.time_info(1).flags); + +% Load AMICA Model 1 +tic; +EEG.amica(1).models=loadmodout15('derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_amicaout_init'); +EEG.icaweights=EEG.amica(1).models.W; +EEG.icasphere=EEG.amica(1).models.S(1:EEG.amica(1).models.num_pcs,:); +EEG=eeg_checkset(EEG); +logging_log('INFO', 'TIME TO: LOAD ICA INFO...'); +toc +%% PASS EEGDATA THROUGH THE AMICA MATRIX +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%Uses the ICA weights and the channel data to recreate the ICA components. + +tic; +% In place of EEG checkset +tmpdata = EEG.data(EEG.icachansind,:); +tmpindices = find(~sum(isnan(tmpdata))); +EEG.icaact = zeros(size(EEG.icaweights,1), size(tmpdata,2)); EEG.icaact(:) = NaN; +EEG.icaact(:,tmpindices) = (EEG.icaweights*EEG.icasphere)*tmpdata(:,tmpindices); +clear tmpdata; + +%% LOG LIKELIHOOD FLAGS +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%Creates marks based off of the log likelihood of the ICA model to the data. +% Create initial log likelihood marks + +logl_init_flags=zeros(size(EEG.data(1,:))); +logl_init_flag_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indices','invert','on'); +logl_init_flags(logl_init_flag_inds)=(EEG.amica(1).models.Lt-min(EEG.amica(1).models.Lt))/(max(EEG.amica(1).models.Lt)-min(EEG.amica(1).models.Lt)); + +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'logl_init',[1,0.45,0],logl_init_flags}); +logging_log('INFO', 'TIME TO: RECREATE ICA COMPONENTS...'); +toc + +% Window the continous Data +logging_log('INFO', 'Windowing the continous data...'); +tic; +EEG=marks_continuous2epochs(EEG,'recurrence',1,'limits',[0 1]); +logging_log('INFO', 'TIME TO: WINDOW THE CONTINUOUS DATA...'); +toc +%% CALCULATE ICAACT SD +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates the IC standard Deviation by epoch window. Flags windows with +% too much standard deviation. + +% Calculate IC sd by window +logging_log('INFO', 'Calculating the IC sd array for window criteria...'); +tic; +epoch_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indexes','invert','on'); +[EEG,icaact_sd1_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'plot_figs','off'); + +% Create the windowing sd criteria +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_t_inds]=marks_array2flags(icaact_sd1_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +% Update marks rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +icsd_epoch_flags=zeros(size(EEG.data(1,:,:))); +icsd_epoch_flags(1,:,epoch_inds(flag_t_inds))=1; +icsd_epoch_flags=padflags(EEG,icsd_epoch_flags,1,'value',.5); +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_sd1',[.4,.6,.6],icsd_epoch_flags}); +logging_log('INFO', 'TIME TO: UPDATE REJECTION STRUCTURE WITH IC SD MARKS...'); +toc + +% Concat the windowed data back to continuous +logging_log('INFO', 'Concatenating windowed data...'); +tic; +EEG=marks_epochs2continuous(EEG); +EEG=eeg_checkset(EEG,'eventconsistency'); +logging_log('INFO', 'TIME TO: CONCATENATE THE WINDOWED DATA INTO CONTINUOUS DATA...'); +toc +%% FLAG GAPS +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Flags small gaps between rejection flags so that there are not as many +% short segments to be analyzed + +% Mark any flag gaps < 2 sec. +tic; +EEG=pop_marks_flag_gap(EEG,{'manual','mark_gap','ic_sd1'}, ... + 2000,'mark_gap',[.8,.8,.8],'offsets',[0 0],'ref_point','both'); + +%%COMBINE MARKS STRUCTURE INTO MANUAL FLAGS... +EEG=pop_marks_merge_labels(EEG,'time_info',{'manual','ic_sd1','mark_gap'},'target_label','manual'); +logging_log('INFO', 'TIME TO: FLAG GAPS AND COMBINE MARKS STRUCTURE INTO MANUAL FLAGS...'); +toc +%% SAVE compart_data.set FILE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% A saved copy is made before the file is purged to its ready for ICA +% version. + +tic; +logging_log('INFO', 'Saving file: ./sub-004/eeg/sub-004_task-faceFO_compart_data.set...'); +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_compart_data.set'); +logging_log('INFO', 'TIME TO: SAVE compart_data FILE...'); +toc; +%% SAVE compart_data_purge FILE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% All EEG parts associated with ICA need to be cleared as models seem to +% adapt based on previous data. Old and New flagged channels/time need to +% be purged again in preparation of ICA. + +% Clearing ICA Data +EEG.icaact = []; +EEG.icawinv = []; +EEG.icasphere = []; +EEG.icaweights = []; +EEG.icachansind = []; +EEG.amica = []; +logging_log('INFO', 'ICA data Reset'); + +% Removing Flagged Channels +tic; +sprintf('%s','Purging flagged channels...\n'); +EEG=pop_marks_select_data(EEG,'channel marks',[],'labels',{'manual','rank'},'remove','on'); +EEG=pop_marks_select_data(EEG,'time marks',[],'labels',{'manual'},'remove','on'); +EEG=eeg_checkset(EEG); +logging_log('INFO', 'TIME TO: PURGE DATA...'); +toc; + +% Saving File to enter next ICAs +logging_log('INFO', 'Saving the Param files'); +sprintf('%s','Saving file: ./sub-004/eeg/sub-004_task-faceFO_compart_data_purge.set...\n'); +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_compart_data_purge.set'); + +% Save diagnostic arrays +try OCTAVE_VERSION; + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_icaact_sd1_t.mat','icaact_sd1_t'); +catch + save('derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_icaact_sd1_t.mat','icaact_sd1_t'); +end + +%% SAVE AMICA PARAMETER FILES +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% A parameter file need to be made for each of the ICAs running after this +% script. In This case AMICA A, B and C are run. + +tic; +% A +% Saving AMICA Parameter File +swapstr=file_strswap('derivatives/BIDS-Lossless-EEG/code/misc/amica_default.param', ... + '[repstr_fdt_fname]','derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_compart_data_purge.fdt', ... + '[repstr_outpath]','derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_amicaout_A', ... + '[repstr_nbchan]', num2str(EEG.nbchan), ... + '[repstr_pnts]', sprintf('%12.0f',EEG.pnts), ... + '[repstr_tpp]', '8', ... + '[repstr_pca]', num2str(EEG.nbchan)); +fidparam=fopen('derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_A.param','w'); +fprintf(fidparam,'%s',swapstr); +fclose(fidparam); + +% B +% Saving AMICA Parameter File +swapstr=file_strswap('derivatives/BIDS-Lossless-EEG/code/misc/amica_default.param', ... + '[repstr_fdt_fname]','derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_compart_data_purge.fdt', ... + '[repstr_outpath]','derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_amicaout_B', ... + '[repstr_nbchan]', num2str(EEG.nbchan), ... + '[repstr_pnts]', sprintf('%12.0f',EEG.pnts), ... + '[repstr_tpp]', '8', ... + '[repstr_pca]', num2str(EEG.nbchan)); +fidparam=fopen('derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_B.param','w'); +fprintf(fidparam,'%s',swapstr); +fclose(fidparam); + +% C +% Saving AMICA Parameter File +swapstr=file_strswap('derivatives/BIDS-Lossless-EEG/code/misc/amica_default.param', ... + '[repstr_fdt_fname]','derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_compart_data_purge.fdt', ... + '[repstr_outpath]','derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_amicaout_C', ... + '[repstr_nbchan]', num2str(EEG.nbchan), ... + '[repstr_pnts]', sprintf('%12.0f',EEG.pnts), ... + '[repstr_tpp]', '8', ... + '[repstr_pca]', num2str(EEG.nbchan)); +fidparam=fopen('derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_C.param','w'); +fprintf(fidparam,'%s',swapstr); +fclose(fidparam); + +%% Create the Amica Output Folder +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Needed for MPI jobs on cluster. Otherwise race effect will occur. + +[~] = rmdir('derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_amicaout_A','s'); +[~] = rmdir('derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_amicaout_B','s'); +[~] = rmdir('derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_amicaout_C','s'); + + +mkdir 'derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_amicaout_A'; +mkdir 'derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_amicaout_B'; +mkdir 'derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_amicaout_C'; + +logging_log('INFO', 'TIME TO: CREATE AMICA PARAMETER FILES...'); +toc + +% FINISH +logging_log('INFO', '***********COMPART DATA COMPLETE!**********'); +logging_log('INFO', 'Scheduler: sbatch'); +print_chan_sample(EEG); diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/s03_compart_data_sub_005_task_faceFO_eeg.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/s03_compart_data_sub_005_task_faceFO_eeg.m new file mode 100644 index 000000000..9c87553b8 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/s03_compart_data_sub_005_task_faceFO_eeg.m @@ -0,0 +1,285 @@ +warning off; + +pkg load signal; +pkg load parallel; +pkg load io; + +file_path = fileparts(mfilename('fullpath')); + +addpath([file_path '/../../code/dependencies/eeglab_lossless'], ... + [file_path '/../../code/dependencies/matlog'], ... + [file_path '/../../code/tools']); +cd '/scratch/tk11br/Face13/'; + +% eeglab auto loads the path correctly instead of shotgun blasting with +% addpath(genpath(<>)) as we were doing prior +if ~exist('EEG', 'var') + eeglab; +end +% Use matlog disp function override +logging_override_disp(true); +% Use matlog INFO level debugging +logging_set_log_level('INFO') + +% Don't run with traditional but enable some of the options +save_default_options ('-mat-binary'); +confirm_recursive_rmdir(false); + +% This makes | and & || and && in earlier version of Octave where +% | and & dont short curcuit by default +if exist('do_braindead_shortcircuit_evaluation', 'builtin') + do_braindead_shortcircuit_evaluation(true); +end + +% Options +pop_editoptions('option_savetwofiles', 1,'option_single',0); + +%% SCRIPT DESCRIPTION +% This script is loads the results of a preliminary ICA and uses the +% standard deviation of the components to flag and remove data time that +% does not fit a well behaved model. This will help make the next +% components stronger. The log likelihood of the model is also flagged for +% reference. Sections marked for high SD often have low log likelihoods +% as well. It then prepares the required parameter files for the parrallel amicas ABC. +% +%% From Config key_strswap Description +%----------------------------------------------------------------------------------------------------------------------------- +% data_path = derivatives/BIDS-Lossless-EEG Path to input and output data files assuming cd = work_path +% recur_sec = 1 Recurrence (sec) for artifact detection epoching (e.g. .3) +% limit_sec = [0 1] Limits (sec) for artifact detection epoching (e.g. [-.3 .3]) +% sd_t_meth = q Method used for flagging epochs (e.g. 'q' (quantiles), or 'na' (default)) +% sd_t_vals = [.3 .7] Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7]) +% sd_t_o = 6 z threshold for flagging epochs during standard deviation criteria (e.g. 6) +% sd_t_f_meth = fixed Fixed method used for flagging epochs (should be 'fixed') +% sd_t_f_vals = Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% sd_t_f_o = .2 z threshold for flagging epochs during fixed standard deviation criteria (e.g. .2) +% out_path = derivatives/BIDS-Lossless-EEG Relative path to output data files assuming cd = work_path +% min_gap_ms = 2000 Minimum time (ms) to allow between periods marked for rejection +% amica_param_file = derivatives/BIDS-Lossless-EEG/code/misc/amica_default.param template amicadefs.param file to modify +% amica_threads_s04 = 8 number of threads to use for running s04a, s04b, s04c amica scripts (Default: 8) + +%% LOAD SA DATASET +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Loads the batched EEG datafile and the AMICA output files. Since ICA is +% done on a channel subset the components need to be returned to the +% correct indicies. + +% Load the EEG datafile +logging_log('INFO', 'Loading set file: ./sub-005/eeg/sub-005_task-faceFO_sa.set...'); +tic; +EEG = pop_loadset('filepath','derivatives/BIDS-Lossless-EEG','filename','./sub-005/eeg/sub-005_task-faceFO_sa.set'); +EEG = eeg_checkset( EEG ); +logging_log('INFO', 'TIME TO: LOAD DATASET...'); +toc + +% Prepare returning components +EEG.icachansind = marks_label2index(EEG.marks.chan_info,{'manual','rank'},'indices','invert','on'); +icatimeind = find(~EEG.marks.time_info(1).flags); + +% Load AMICA Model 1 +tic; +EEG.amica(1).models=loadmodout15('derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_amicaout_init'); +EEG.icaweights=EEG.amica(1).models.W; +EEG.icasphere=EEG.amica(1).models.S(1:EEG.amica(1).models.num_pcs,:); +EEG=eeg_checkset(EEG); +logging_log('INFO', 'TIME TO: LOAD ICA INFO...'); +toc +%% PASS EEGDATA THROUGH THE AMICA MATRIX +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%Uses the ICA weights and the channel data to recreate the ICA components. + +tic; +% In place of EEG checkset +tmpdata = EEG.data(EEG.icachansind,:); +tmpindices = find(~sum(isnan(tmpdata))); +EEG.icaact = zeros(size(EEG.icaweights,1), size(tmpdata,2)); EEG.icaact(:) = NaN; +EEG.icaact(:,tmpindices) = (EEG.icaweights*EEG.icasphere)*tmpdata(:,tmpindices); +clear tmpdata; + +%% LOG LIKELIHOOD FLAGS +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%Creates marks based off of the log likelihood of the ICA model to the data. +% Create initial log likelihood marks + +logl_init_flags=zeros(size(EEG.data(1,:))); +logl_init_flag_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indices','invert','on'); +logl_init_flags(logl_init_flag_inds)=(EEG.amica(1).models.Lt-min(EEG.amica(1).models.Lt))/(max(EEG.amica(1).models.Lt)-min(EEG.amica(1).models.Lt)); + +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'logl_init',[1,0.45,0],logl_init_flags}); +logging_log('INFO', 'TIME TO: RECREATE ICA COMPONENTS...'); +toc + +% Window the continous Data +logging_log('INFO', 'Windowing the continous data...'); +tic; +EEG=marks_continuous2epochs(EEG,'recurrence',1,'limits',[0 1]); +logging_log('INFO', 'TIME TO: WINDOW THE CONTINUOUS DATA...'); +toc +%% CALCULATE ICAACT SD +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates the IC standard Deviation by epoch window. Flags windows with +% too much standard deviation. + +% Calculate IC sd by window +logging_log('INFO', 'Calculating the IC sd array for window criteria...'); +tic; +epoch_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indexes','invert','on'); +[EEG,icaact_sd1_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'plot_figs','off'); + +% Create the windowing sd criteria +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_t_inds]=marks_array2flags(icaact_sd1_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +% Update marks rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +icsd_epoch_flags=zeros(size(EEG.data(1,:,:))); +icsd_epoch_flags(1,:,epoch_inds(flag_t_inds))=1; +icsd_epoch_flags=padflags(EEG,icsd_epoch_flags,1,'value',.5); +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_sd1',[.4,.6,.6],icsd_epoch_flags}); +logging_log('INFO', 'TIME TO: UPDATE REJECTION STRUCTURE WITH IC SD MARKS...'); +toc + +% Concat the windowed data back to continuous +logging_log('INFO', 'Concatenating windowed data...'); +tic; +EEG=marks_epochs2continuous(EEG); +EEG=eeg_checkset(EEG,'eventconsistency'); +logging_log('INFO', 'TIME TO: CONCATENATE THE WINDOWED DATA INTO CONTINUOUS DATA...'); +toc +%% FLAG GAPS +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Flags small gaps between rejection flags so that there are not as many +% short segments to be analyzed + +% Mark any flag gaps < 2 sec. +tic; +EEG=pop_marks_flag_gap(EEG,{'manual','mark_gap','ic_sd1'}, ... + 2000,'mark_gap',[.8,.8,.8],'offsets',[0 0],'ref_point','both'); + +%%COMBINE MARKS STRUCTURE INTO MANUAL FLAGS... +EEG=pop_marks_merge_labels(EEG,'time_info',{'manual','ic_sd1','mark_gap'},'target_label','manual'); +logging_log('INFO', 'TIME TO: FLAG GAPS AND COMBINE MARKS STRUCTURE INTO MANUAL FLAGS...'); +toc +%% SAVE compart_data.set FILE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% A saved copy is made before the file is purged to its ready for ICA +% version. + +tic; +logging_log('INFO', 'Saving file: ./sub-005/eeg/sub-005_task-faceFO_compart_data.set...'); +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_compart_data.set'); +logging_log('INFO', 'TIME TO: SAVE compart_data FILE...'); +toc; +%% SAVE compart_data_purge FILE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% All EEG parts associated with ICA need to be cleared as models seem to +% adapt based on previous data. Old and New flagged channels/time need to +% be purged again in preparation of ICA. + +% Clearing ICA Data +EEG.icaact = []; +EEG.icawinv = []; +EEG.icasphere = []; +EEG.icaweights = []; +EEG.icachansind = []; +EEG.amica = []; +logging_log('INFO', 'ICA data Reset'); + +% Removing Flagged Channels +tic; +sprintf('%s','Purging flagged channels...\n'); +EEG=pop_marks_select_data(EEG,'channel marks',[],'labels',{'manual','rank'},'remove','on'); +EEG=pop_marks_select_data(EEG,'time marks',[],'labels',{'manual'},'remove','on'); +EEG=eeg_checkset(EEG); +logging_log('INFO', 'TIME TO: PURGE DATA...'); +toc; + +% Saving File to enter next ICAs +logging_log('INFO', 'Saving the Param files'); +sprintf('%s','Saving file: ./sub-005/eeg/sub-005_task-faceFO_compart_data_purge.set...\n'); +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_compart_data_purge.set'); + +% Save diagnostic arrays +try OCTAVE_VERSION; + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_icaact_sd1_t.mat','icaact_sd1_t'); +catch + save('derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_icaact_sd1_t.mat','icaact_sd1_t'); +end + +%% SAVE AMICA PARAMETER FILES +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% A parameter file need to be made for each of the ICAs running after this +% script. In This case AMICA A, B and C are run. + +tic; +% A +% Saving AMICA Parameter File +swapstr=file_strswap('derivatives/BIDS-Lossless-EEG/code/misc/amica_default.param', ... + '[repstr_fdt_fname]','derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_compart_data_purge.fdt', ... + '[repstr_outpath]','derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_amicaout_A', ... + '[repstr_nbchan]', num2str(EEG.nbchan), ... + '[repstr_pnts]', sprintf('%12.0f',EEG.pnts), ... + '[repstr_tpp]', '8', ... + '[repstr_pca]', num2str(EEG.nbchan)); +fidparam=fopen('derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_A.param','w'); +fprintf(fidparam,'%s',swapstr); +fclose(fidparam); + +% B +% Saving AMICA Parameter File +swapstr=file_strswap('derivatives/BIDS-Lossless-EEG/code/misc/amica_default.param', ... + '[repstr_fdt_fname]','derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_compart_data_purge.fdt', ... + '[repstr_outpath]','derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_amicaout_B', ... + '[repstr_nbchan]', num2str(EEG.nbchan), ... + '[repstr_pnts]', sprintf('%12.0f',EEG.pnts), ... + '[repstr_tpp]', '8', ... + '[repstr_pca]', num2str(EEG.nbchan)); +fidparam=fopen('derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_B.param','w'); +fprintf(fidparam,'%s',swapstr); +fclose(fidparam); + +% C +% Saving AMICA Parameter File +swapstr=file_strswap('derivatives/BIDS-Lossless-EEG/code/misc/amica_default.param', ... + '[repstr_fdt_fname]','derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_compart_data_purge.fdt', ... + '[repstr_outpath]','derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_amicaout_C', ... + '[repstr_nbchan]', num2str(EEG.nbchan), ... + '[repstr_pnts]', sprintf('%12.0f',EEG.pnts), ... + '[repstr_tpp]', '8', ... + '[repstr_pca]', num2str(EEG.nbchan)); +fidparam=fopen('derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_C.param','w'); +fprintf(fidparam,'%s',swapstr); +fclose(fidparam); + +%% Create the Amica Output Folder +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Needed for MPI jobs on cluster. Otherwise race effect will occur. + +[~] = rmdir('derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_amicaout_A','s'); +[~] = rmdir('derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_amicaout_B','s'); +[~] = rmdir('derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_amicaout_C','s'); + + +mkdir 'derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_amicaout_A'; +mkdir 'derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_amicaout_B'; +mkdir 'derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_amicaout_C'; + +logging_log('INFO', 'TIME TO: CREATE AMICA PARAMETER FILES...'); +toc + +% FINISH +logging_log('INFO', '***********COMPART DATA COMPLETE!**********'); +logging_log('INFO', 'Scheduler: sbatch'); +print_chan_sample(EEG); diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/s03_compart_data_sub_006_task_faceFO_eeg.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/s03_compart_data_sub_006_task_faceFO_eeg.m new file mode 100644 index 000000000..3dd2182da --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/s03_compart_data_sub_006_task_faceFO_eeg.m @@ -0,0 +1,285 @@ +warning off; + +pkg load signal; +pkg load parallel; +pkg load io; + +file_path = fileparts(mfilename('fullpath')); + +addpath([file_path '/../../code/dependencies/eeglab_lossless'], ... + [file_path '/../../code/dependencies/matlog'], ... + [file_path '/../../code/tools']); +cd '/scratch/tk11br/Face13/'; + +% eeglab auto loads the path correctly instead of shotgun blasting with +% addpath(genpath(<>)) as we were doing prior +if ~exist('EEG', 'var') + eeglab; +end +% Use matlog disp function override +logging_override_disp(true); +% Use matlog INFO level debugging +logging_set_log_level('INFO') + +% Don't run with traditional but enable some of the options +save_default_options ('-mat-binary'); +confirm_recursive_rmdir(false); + +% This makes | and & || and && in earlier version of Octave where +% | and & dont short curcuit by default +if exist('do_braindead_shortcircuit_evaluation', 'builtin') + do_braindead_shortcircuit_evaluation(true); +end + +% Options +pop_editoptions('option_savetwofiles', 1,'option_single',0); + +%% SCRIPT DESCRIPTION +% This script is loads the results of a preliminary ICA and uses the +% standard deviation of the components to flag and remove data time that +% does not fit a well behaved model. This will help make the next +% components stronger. The log likelihood of the model is also flagged for +% reference. Sections marked for high SD often have low log likelihoods +% as well. It then prepares the required parameter files for the parrallel amicas ABC. +% +%% From Config key_strswap Description +%----------------------------------------------------------------------------------------------------------------------------- +% data_path = derivatives/BIDS-Lossless-EEG Path to input and output data files assuming cd = work_path +% recur_sec = 1 Recurrence (sec) for artifact detection epoching (e.g. .3) +% limit_sec = [0 1] Limits (sec) for artifact detection epoching (e.g. [-.3 .3]) +% sd_t_meth = q Method used for flagging epochs (e.g. 'q' (quantiles), or 'na' (default)) +% sd_t_vals = [.3 .7] Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7]) +% sd_t_o = 6 z threshold for flagging epochs during standard deviation criteria (e.g. 6) +% sd_t_f_meth = fixed Fixed method used for flagging epochs (should be 'fixed') +% sd_t_f_vals = Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% sd_t_f_o = .2 z threshold for flagging epochs during fixed standard deviation criteria (e.g. .2) +% out_path = derivatives/BIDS-Lossless-EEG Relative path to output data files assuming cd = work_path +% min_gap_ms = 2000 Minimum time (ms) to allow between periods marked for rejection +% amica_param_file = derivatives/BIDS-Lossless-EEG/code/misc/amica_default.param template amicadefs.param file to modify +% amica_threads_s04 = 8 number of threads to use for running s04a, s04b, s04c amica scripts (Default: 8) + +%% LOAD SA DATASET +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Loads the batched EEG datafile and the AMICA output files. Since ICA is +% done on a channel subset the components need to be returned to the +% correct indicies. + +% Load the EEG datafile +logging_log('INFO', 'Loading set file: ./sub-006/eeg/sub-006_task-faceFO_sa.set...'); +tic; +EEG = pop_loadset('filepath','derivatives/BIDS-Lossless-EEG','filename','./sub-006/eeg/sub-006_task-faceFO_sa.set'); +EEG = eeg_checkset( EEG ); +logging_log('INFO', 'TIME TO: LOAD DATASET...'); +toc + +% Prepare returning components +EEG.icachansind = marks_label2index(EEG.marks.chan_info,{'manual','rank'},'indices','invert','on'); +icatimeind = find(~EEG.marks.time_info(1).flags); + +% Load AMICA Model 1 +tic; +EEG.amica(1).models=loadmodout15('derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_amicaout_init'); +EEG.icaweights=EEG.amica(1).models.W; +EEG.icasphere=EEG.amica(1).models.S(1:EEG.amica(1).models.num_pcs,:); +EEG=eeg_checkset(EEG); +logging_log('INFO', 'TIME TO: LOAD ICA INFO...'); +toc +%% PASS EEGDATA THROUGH THE AMICA MATRIX +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%Uses the ICA weights and the channel data to recreate the ICA components. + +tic; +% In place of EEG checkset +tmpdata = EEG.data(EEG.icachansind,:); +tmpindices = find(~sum(isnan(tmpdata))); +EEG.icaact = zeros(size(EEG.icaweights,1), size(tmpdata,2)); EEG.icaact(:) = NaN; +EEG.icaact(:,tmpindices) = (EEG.icaweights*EEG.icasphere)*tmpdata(:,tmpindices); +clear tmpdata; + +%% LOG LIKELIHOOD FLAGS +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%Creates marks based off of the log likelihood of the ICA model to the data. +% Create initial log likelihood marks + +logl_init_flags=zeros(size(EEG.data(1,:))); +logl_init_flag_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indices','invert','on'); +logl_init_flags(logl_init_flag_inds)=(EEG.amica(1).models.Lt-min(EEG.amica(1).models.Lt))/(max(EEG.amica(1).models.Lt)-min(EEG.amica(1).models.Lt)); + +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'logl_init',[1,0.45,0],logl_init_flags}); +logging_log('INFO', 'TIME TO: RECREATE ICA COMPONENTS...'); +toc + +% Window the continous Data +logging_log('INFO', 'Windowing the continous data...'); +tic; +EEG=marks_continuous2epochs(EEG,'recurrence',1,'limits',[0 1]); +logging_log('INFO', 'TIME TO: WINDOW THE CONTINUOUS DATA...'); +toc +%% CALCULATE ICAACT SD +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates the IC standard Deviation by epoch window. Flags windows with +% too much standard deviation. + +% Calculate IC sd by window +logging_log('INFO', 'Calculating the IC sd array for window criteria...'); +tic; +epoch_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indexes','invert','on'); +[EEG,icaact_sd1_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'plot_figs','off'); + +% Create the windowing sd criteria +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_t_inds]=marks_array2flags(icaact_sd1_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +% Update marks rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +icsd_epoch_flags=zeros(size(EEG.data(1,:,:))); +icsd_epoch_flags(1,:,epoch_inds(flag_t_inds))=1; +icsd_epoch_flags=padflags(EEG,icsd_epoch_flags,1,'value',.5); +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_sd1',[.4,.6,.6],icsd_epoch_flags}); +logging_log('INFO', 'TIME TO: UPDATE REJECTION STRUCTURE WITH IC SD MARKS...'); +toc + +% Concat the windowed data back to continuous +logging_log('INFO', 'Concatenating windowed data...'); +tic; +EEG=marks_epochs2continuous(EEG); +EEG=eeg_checkset(EEG,'eventconsistency'); +logging_log('INFO', 'TIME TO: CONCATENATE THE WINDOWED DATA INTO CONTINUOUS DATA...'); +toc +%% FLAG GAPS +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Flags small gaps between rejection flags so that there are not as many +% short segments to be analyzed + +% Mark any flag gaps < 2 sec. +tic; +EEG=pop_marks_flag_gap(EEG,{'manual','mark_gap','ic_sd1'}, ... + 2000,'mark_gap',[.8,.8,.8],'offsets',[0 0],'ref_point','both'); + +%%COMBINE MARKS STRUCTURE INTO MANUAL FLAGS... +EEG=pop_marks_merge_labels(EEG,'time_info',{'manual','ic_sd1','mark_gap'},'target_label','manual'); +logging_log('INFO', 'TIME TO: FLAG GAPS AND COMBINE MARKS STRUCTURE INTO MANUAL FLAGS...'); +toc +%% SAVE compart_data.set FILE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% A saved copy is made before the file is purged to its ready for ICA +% version. + +tic; +logging_log('INFO', 'Saving file: ./sub-006/eeg/sub-006_task-faceFO_compart_data.set...'); +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_compart_data.set'); +logging_log('INFO', 'TIME TO: SAVE compart_data FILE...'); +toc; +%% SAVE compart_data_purge FILE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% All EEG parts associated with ICA need to be cleared as models seem to +% adapt based on previous data. Old and New flagged channels/time need to +% be purged again in preparation of ICA. + +% Clearing ICA Data +EEG.icaact = []; +EEG.icawinv = []; +EEG.icasphere = []; +EEG.icaweights = []; +EEG.icachansind = []; +EEG.amica = []; +logging_log('INFO', 'ICA data Reset'); + +% Removing Flagged Channels +tic; +sprintf('%s','Purging flagged channels...\n'); +EEG=pop_marks_select_data(EEG,'channel marks',[],'labels',{'manual','rank'},'remove','on'); +EEG=pop_marks_select_data(EEG,'time marks',[],'labels',{'manual'},'remove','on'); +EEG=eeg_checkset(EEG); +logging_log('INFO', 'TIME TO: PURGE DATA...'); +toc; + +% Saving File to enter next ICAs +logging_log('INFO', 'Saving the Param files'); +sprintf('%s','Saving file: ./sub-006/eeg/sub-006_task-faceFO_compart_data_purge.set...\n'); +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_compart_data_purge.set'); + +% Save diagnostic arrays +try OCTAVE_VERSION; + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_icaact_sd1_t.mat','icaact_sd1_t'); +catch + save('derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_icaact_sd1_t.mat','icaact_sd1_t'); +end + +%% SAVE AMICA PARAMETER FILES +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% A parameter file need to be made for each of the ICAs running after this +% script. In This case AMICA A, B and C are run. + +tic; +% A +% Saving AMICA Parameter File +swapstr=file_strswap('derivatives/BIDS-Lossless-EEG/code/misc/amica_default.param', ... + '[repstr_fdt_fname]','derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_compart_data_purge.fdt', ... + '[repstr_outpath]','derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_amicaout_A', ... + '[repstr_nbchan]', num2str(EEG.nbchan), ... + '[repstr_pnts]', sprintf('%12.0f',EEG.pnts), ... + '[repstr_tpp]', '8', ... + '[repstr_pca]', num2str(EEG.nbchan)); +fidparam=fopen('derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_A.param','w'); +fprintf(fidparam,'%s',swapstr); +fclose(fidparam); + +% B +% Saving AMICA Parameter File +swapstr=file_strswap('derivatives/BIDS-Lossless-EEG/code/misc/amica_default.param', ... + '[repstr_fdt_fname]','derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_compart_data_purge.fdt', ... + '[repstr_outpath]','derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_amicaout_B', ... + '[repstr_nbchan]', num2str(EEG.nbchan), ... + '[repstr_pnts]', sprintf('%12.0f',EEG.pnts), ... + '[repstr_tpp]', '8', ... + '[repstr_pca]', num2str(EEG.nbchan)); +fidparam=fopen('derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_B.param','w'); +fprintf(fidparam,'%s',swapstr); +fclose(fidparam); + +% C +% Saving AMICA Parameter File +swapstr=file_strswap('derivatives/BIDS-Lossless-EEG/code/misc/amica_default.param', ... + '[repstr_fdt_fname]','derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_compart_data_purge.fdt', ... + '[repstr_outpath]','derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_amicaout_C', ... + '[repstr_nbchan]', num2str(EEG.nbchan), ... + '[repstr_pnts]', sprintf('%12.0f',EEG.pnts), ... + '[repstr_tpp]', '8', ... + '[repstr_pca]', num2str(EEG.nbchan)); +fidparam=fopen('derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_C.param','w'); +fprintf(fidparam,'%s',swapstr); +fclose(fidparam); + +%% Create the Amica Output Folder +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Needed for MPI jobs on cluster. Otherwise race effect will occur. + +[~] = rmdir('derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_amicaout_A','s'); +[~] = rmdir('derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_amicaout_B','s'); +[~] = rmdir('derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_amicaout_C','s'); + + +mkdir 'derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_amicaout_A'; +mkdir 'derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_amicaout_B'; +mkdir 'derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_amicaout_C'; + +logging_log('INFO', 'TIME TO: CREATE AMICA PARAMETER FILES...'); +toc + +% FINISH +logging_log('INFO', '***********COMPART DATA COMPLETE!**********'); +logging_log('INFO', 'Scheduler: sbatch'); +print_chan_sample(EEG); diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/s03_compart_data_sub_007_task_faceFO_eeg.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/s03_compart_data_sub_007_task_faceFO_eeg.m new file mode 100644 index 000000000..027da367c --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/s03_compart_data_sub_007_task_faceFO_eeg.m @@ -0,0 +1,285 @@ +warning off; + +pkg load signal; +pkg load parallel; +pkg load io; + +file_path = fileparts(mfilename('fullpath')); + +addpath([file_path '/../../code/dependencies/eeglab_lossless'], ... + [file_path '/../../code/dependencies/matlog'], ... + [file_path '/../../code/tools']); +cd '/scratch/tk11br/Face13/'; + +% eeglab auto loads the path correctly instead of shotgun blasting with +% addpath(genpath(<>)) as we were doing prior +if ~exist('EEG', 'var') + eeglab; +end +% Use matlog disp function override +logging_override_disp(true); +% Use matlog INFO level debugging +logging_set_log_level('INFO') + +% Don't run with traditional but enable some of the options +save_default_options ('-mat-binary'); +confirm_recursive_rmdir(false); + +% This makes | and & || and && in earlier version of Octave where +% | and & dont short curcuit by default +if exist('do_braindead_shortcircuit_evaluation', 'builtin') + do_braindead_shortcircuit_evaluation(true); +end + +% Options +pop_editoptions('option_savetwofiles', 1,'option_single',0); + +%% SCRIPT DESCRIPTION +% This script is loads the results of a preliminary ICA and uses the +% standard deviation of the components to flag and remove data time that +% does not fit a well behaved model. This will help make the next +% components stronger. The log likelihood of the model is also flagged for +% reference. Sections marked for high SD often have low log likelihoods +% as well. It then prepares the required parameter files for the parrallel amicas ABC. +% +%% From Config key_strswap Description +%----------------------------------------------------------------------------------------------------------------------------- +% data_path = derivatives/BIDS-Lossless-EEG Path to input and output data files assuming cd = work_path +% recur_sec = 1 Recurrence (sec) for artifact detection epoching (e.g. .3) +% limit_sec = [0 1] Limits (sec) for artifact detection epoching (e.g. [-.3 .3]) +% sd_t_meth = q Method used for flagging epochs (e.g. 'q' (quantiles), or 'na' (default)) +% sd_t_vals = [.3 .7] Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7]) +% sd_t_o = 6 z threshold for flagging epochs during standard deviation criteria (e.g. 6) +% sd_t_f_meth = fixed Fixed method used for flagging epochs (should be 'fixed') +% sd_t_f_vals = Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% sd_t_f_o = .2 z threshold for flagging epochs during fixed standard deviation criteria (e.g. .2) +% out_path = derivatives/BIDS-Lossless-EEG Relative path to output data files assuming cd = work_path +% min_gap_ms = 2000 Minimum time (ms) to allow between periods marked for rejection +% amica_param_file = derivatives/BIDS-Lossless-EEG/code/misc/amica_default.param template amicadefs.param file to modify +% amica_threads_s04 = 8 number of threads to use for running s04a, s04b, s04c amica scripts (Default: 8) + +%% LOAD SA DATASET +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Loads the batched EEG datafile and the AMICA output files. Since ICA is +% done on a channel subset the components need to be returned to the +% correct indicies. + +% Load the EEG datafile +logging_log('INFO', 'Loading set file: ./sub-007/eeg/sub-007_task-faceFO_sa.set...'); +tic; +EEG = pop_loadset('filepath','derivatives/BIDS-Lossless-EEG','filename','./sub-007/eeg/sub-007_task-faceFO_sa.set'); +EEG = eeg_checkset( EEG ); +logging_log('INFO', 'TIME TO: LOAD DATASET...'); +toc + +% Prepare returning components +EEG.icachansind = marks_label2index(EEG.marks.chan_info,{'manual','rank'},'indices','invert','on'); +icatimeind = find(~EEG.marks.time_info(1).flags); + +% Load AMICA Model 1 +tic; +EEG.amica(1).models=loadmodout15('derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_amicaout_init'); +EEG.icaweights=EEG.amica(1).models.W; +EEG.icasphere=EEG.amica(1).models.S(1:EEG.amica(1).models.num_pcs,:); +EEG=eeg_checkset(EEG); +logging_log('INFO', 'TIME TO: LOAD ICA INFO...'); +toc +%% PASS EEGDATA THROUGH THE AMICA MATRIX +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%Uses the ICA weights and the channel data to recreate the ICA components. + +tic; +% In place of EEG checkset +tmpdata = EEG.data(EEG.icachansind,:); +tmpindices = find(~sum(isnan(tmpdata))); +EEG.icaact = zeros(size(EEG.icaweights,1), size(tmpdata,2)); EEG.icaact(:) = NaN; +EEG.icaact(:,tmpindices) = (EEG.icaweights*EEG.icasphere)*tmpdata(:,tmpindices); +clear tmpdata; + +%% LOG LIKELIHOOD FLAGS +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%Creates marks based off of the log likelihood of the ICA model to the data. +% Create initial log likelihood marks + +logl_init_flags=zeros(size(EEG.data(1,:))); +logl_init_flag_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indices','invert','on'); +logl_init_flags(logl_init_flag_inds)=(EEG.amica(1).models.Lt-min(EEG.amica(1).models.Lt))/(max(EEG.amica(1).models.Lt)-min(EEG.amica(1).models.Lt)); + +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'logl_init',[1,0.45,0],logl_init_flags}); +logging_log('INFO', 'TIME TO: RECREATE ICA COMPONENTS...'); +toc + +% Window the continous Data +logging_log('INFO', 'Windowing the continous data...'); +tic; +EEG=marks_continuous2epochs(EEG,'recurrence',1,'limits',[0 1]); +logging_log('INFO', 'TIME TO: WINDOW THE CONTINUOUS DATA...'); +toc +%% CALCULATE ICAACT SD +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates the IC standard Deviation by epoch window. Flags windows with +% too much standard deviation. + +% Calculate IC sd by window +logging_log('INFO', 'Calculating the IC sd array for window criteria...'); +tic; +epoch_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indexes','invert','on'); +[EEG,icaact_sd1_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'plot_figs','off'); + +% Create the windowing sd criteria +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_t_inds]=marks_array2flags(icaact_sd1_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +% Update marks rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +icsd_epoch_flags=zeros(size(EEG.data(1,:,:))); +icsd_epoch_flags(1,:,epoch_inds(flag_t_inds))=1; +icsd_epoch_flags=padflags(EEG,icsd_epoch_flags,1,'value',.5); +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_sd1',[.4,.6,.6],icsd_epoch_flags}); +logging_log('INFO', 'TIME TO: UPDATE REJECTION STRUCTURE WITH IC SD MARKS...'); +toc + +% Concat the windowed data back to continuous +logging_log('INFO', 'Concatenating windowed data...'); +tic; +EEG=marks_epochs2continuous(EEG); +EEG=eeg_checkset(EEG,'eventconsistency'); +logging_log('INFO', 'TIME TO: CONCATENATE THE WINDOWED DATA INTO CONTINUOUS DATA...'); +toc +%% FLAG GAPS +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Flags small gaps between rejection flags so that there are not as many +% short segments to be analyzed + +% Mark any flag gaps < 2 sec. +tic; +EEG=pop_marks_flag_gap(EEG,{'manual','mark_gap','ic_sd1'}, ... + 2000,'mark_gap',[.8,.8,.8],'offsets',[0 0],'ref_point','both'); + +%%COMBINE MARKS STRUCTURE INTO MANUAL FLAGS... +EEG=pop_marks_merge_labels(EEG,'time_info',{'manual','ic_sd1','mark_gap'},'target_label','manual'); +logging_log('INFO', 'TIME TO: FLAG GAPS AND COMBINE MARKS STRUCTURE INTO MANUAL FLAGS...'); +toc +%% SAVE compart_data.set FILE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% A saved copy is made before the file is purged to its ready for ICA +% version. + +tic; +logging_log('INFO', 'Saving file: ./sub-007/eeg/sub-007_task-faceFO_compart_data.set...'); +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_compart_data.set'); +logging_log('INFO', 'TIME TO: SAVE compart_data FILE...'); +toc; +%% SAVE compart_data_purge FILE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% All EEG parts associated with ICA need to be cleared as models seem to +% adapt based on previous data. Old and New flagged channels/time need to +% be purged again in preparation of ICA. + +% Clearing ICA Data +EEG.icaact = []; +EEG.icawinv = []; +EEG.icasphere = []; +EEG.icaweights = []; +EEG.icachansind = []; +EEG.amica = []; +logging_log('INFO', 'ICA data Reset'); + +% Removing Flagged Channels +tic; +sprintf('%s','Purging flagged channels...\n'); +EEG=pop_marks_select_data(EEG,'channel marks',[],'labels',{'manual','rank'},'remove','on'); +EEG=pop_marks_select_data(EEG,'time marks',[],'labels',{'manual'},'remove','on'); +EEG=eeg_checkset(EEG); +logging_log('INFO', 'TIME TO: PURGE DATA...'); +toc; + +% Saving File to enter next ICAs +logging_log('INFO', 'Saving the Param files'); +sprintf('%s','Saving file: ./sub-007/eeg/sub-007_task-faceFO_compart_data_purge.set...\n'); +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_compart_data_purge.set'); + +% Save diagnostic arrays +try OCTAVE_VERSION; + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_icaact_sd1_t.mat','icaact_sd1_t'); +catch + save('derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_icaact_sd1_t.mat','icaact_sd1_t'); +end + +%% SAVE AMICA PARAMETER FILES +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% A parameter file need to be made for each of the ICAs running after this +% script. In This case AMICA A, B and C are run. + +tic; +% A +% Saving AMICA Parameter File +swapstr=file_strswap('derivatives/BIDS-Lossless-EEG/code/misc/amica_default.param', ... + '[repstr_fdt_fname]','derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_compart_data_purge.fdt', ... + '[repstr_outpath]','derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_amicaout_A', ... + '[repstr_nbchan]', num2str(EEG.nbchan), ... + '[repstr_pnts]', sprintf('%12.0f',EEG.pnts), ... + '[repstr_tpp]', '8', ... + '[repstr_pca]', num2str(EEG.nbchan)); +fidparam=fopen('derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_A.param','w'); +fprintf(fidparam,'%s',swapstr); +fclose(fidparam); + +% B +% Saving AMICA Parameter File +swapstr=file_strswap('derivatives/BIDS-Lossless-EEG/code/misc/amica_default.param', ... + '[repstr_fdt_fname]','derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_compart_data_purge.fdt', ... + '[repstr_outpath]','derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_amicaout_B', ... + '[repstr_nbchan]', num2str(EEG.nbchan), ... + '[repstr_pnts]', sprintf('%12.0f',EEG.pnts), ... + '[repstr_tpp]', '8', ... + '[repstr_pca]', num2str(EEG.nbchan)); +fidparam=fopen('derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_B.param','w'); +fprintf(fidparam,'%s',swapstr); +fclose(fidparam); + +% C +% Saving AMICA Parameter File +swapstr=file_strswap('derivatives/BIDS-Lossless-EEG/code/misc/amica_default.param', ... + '[repstr_fdt_fname]','derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_compart_data_purge.fdt', ... + '[repstr_outpath]','derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_amicaout_C', ... + '[repstr_nbchan]', num2str(EEG.nbchan), ... + '[repstr_pnts]', sprintf('%12.0f',EEG.pnts), ... + '[repstr_tpp]', '8', ... + '[repstr_pca]', num2str(EEG.nbchan)); +fidparam=fopen('derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_C.param','w'); +fprintf(fidparam,'%s',swapstr); +fclose(fidparam); + +%% Create the Amica Output Folder +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Needed for MPI jobs on cluster. Otherwise race effect will occur. + +[~] = rmdir('derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_amicaout_A','s'); +[~] = rmdir('derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_amicaout_B','s'); +[~] = rmdir('derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_amicaout_C','s'); + + +mkdir 'derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_amicaout_A'; +mkdir 'derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_amicaout_B'; +mkdir 'derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_amicaout_C'; + +logging_log('INFO', 'TIME TO: CREATE AMICA PARAMETER FILES...'); +toc + +% FINISH +logging_log('INFO', '***********COMPART DATA COMPLETE!**********'); +logging_log('INFO', 'Scheduler: sbatch'); +print_chan_sample(EEG); diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/s03_compart_data_sub_008_task_faceFO_eeg.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/s03_compart_data_sub_008_task_faceFO_eeg.m new file mode 100644 index 000000000..c1c9bf1c3 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/s03_compart_data_sub_008_task_faceFO_eeg.m @@ -0,0 +1,285 @@ +warning off; + +pkg load signal; +pkg load parallel; +pkg load io; + +file_path = fileparts(mfilename('fullpath')); + +addpath([file_path '/../../code/dependencies/eeglab_lossless'], ... + [file_path '/../../code/dependencies/matlog'], ... + [file_path '/../../code/tools']); +cd '/scratch/tk11br/Face13/'; + +% eeglab auto loads the path correctly instead of shotgun blasting with +% addpath(genpath(<>)) as we were doing prior +if ~exist('EEG', 'var') + eeglab; +end +% Use matlog disp function override +logging_override_disp(true); +% Use matlog INFO level debugging +logging_set_log_level('INFO') + +% Don't run with traditional but enable some of the options +save_default_options ('-mat-binary'); +confirm_recursive_rmdir(false); + +% This makes | and & || and && in earlier version of Octave where +% | and & dont short curcuit by default +if exist('do_braindead_shortcircuit_evaluation', 'builtin') + do_braindead_shortcircuit_evaluation(true); +end + +% Options +pop_editoptions('option_savetwofiles', 1,'option_single',0); + +%% SCRIPT DESCRIPTION +% This script is loads the results of a preliminary ICA and uses the +% standard deviation of the components to flag and remove data time that +% does not fit a well behaved model. This will help make the next +% components stronger. The log likelihood of the model is also flagged for +% reference. Sections marked for high SD often have low log likelihoods +% as well. It then prepares the required parameter files for the parrallel amicas ABC. +% +%% From Config key_strswap Description +%----------------------------------------------------------------------------------------------------------------------------- +% data_path = derivatives/BIDS-Lossless-EEG Path to input and output data files assuming cd = work_path +% recur_sec = 1 Recurrence (sec) for artifact detection epoching (e.g. .3) +% limit_sec = [0 1] Limits (sec) for artifact detection epoching (e.g. [-.3 .3]) +% sd_t_meth = q Method used for flagging epochs (e.g. 'q' (quantiles), or 'na' (default)) +% sd_t_vals = [.3 .7] Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7]) +% sd_t_o = 6 z threshold for flagging epochs during standard deviation criteria (e.g. 6) +% sd_t_f_meth = fixed Fixed method used for flagging epochs (should be 'fixed') +% sd_t_f_vals = Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% sd_t_f_o = .2 z threshold for flagging epochs during fixed standard deviation criteria (e.g. .2) +% out_path = derivatives/BIDS-Lossless-EEG Relative path to output data files assuming cd = work_path +% min_gap_ms = 2000 Minimum time (ms) to allow between periods marked for rejection +% amica_param_file = derivatives/BIDS-Lossless-EEG/code/misc/amica_default.param template amicadefs.param file to modify +% amica_threads_s04 = 8 number of threads to use for running s04a, s04b, s04c amica scripts (Default: 8) + +%% LOAD SA DATASET +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Loads the batched EEG datafile and the AMICA output files. Since ICA is +% done on a channel subset the components need to be returned to the +% correct indicies. + +% Load the EEG datafile +logging_log('INFO', 'Loading set file: ./sub-008/eeg/sub-008_task-faceFO_sa.set...'); +tic; +EEG = pop_loadset('filepath','derivatives/BIDS-Lossless-EEG','filename','./sub-008/eeg/sub-008_task-faceFO_sa.set'); +EEG = eeg_checkset( EEG ); +logging_log('INFO', 'TIME TO: LOAD DATASET...'); +toc + +% Prepare returning components +EEG.icachansind = marks_label2index(EEG.marks.chan_info,{'manual','rank'},'indices','invert','on'); +icatimeind = find(~EEG.marks.time_info(1).flags); + +% Load AMICA Model 1 +tic; +EEG.amica(1).models=loadmodout15('derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_amicaout_init'); +EEG.icaweights=EEG.amica(1).models.W; +EEG.icasphere=EEG.amica(1).models.S(1:EEG.amica(1).models.num_pcs,:); +EEG=eeg_checkset(EEG); +logging_log('INFO', 'TIME TO: LOAD ICA INFO...'); +toc +%% PASS EEGDATA THROUGH THE AMICA MATRIX +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%Uses the ICA weights and the channel data to recreate the ICA components. + +tic; +% In place of EEG checkset +tmpdata = EEG.data(EEG.icachansind,:); +tmpindices = find(~sum(isnan(tmpdata))); +EEG.icaact = zeros(size(EEG.icaweights,1), size(tmpdata,2)); EEG.icaact(:) = NaN; +EEG.icaact(:,tmpindices) = (EEG.icaweights*EEG.icasphere)*tmpdata(:,tmpindices); +clear tmpdata; + +%% LOG LIKELIHOOD FLAGS +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%Creates marks based off of the log likelihood of the ICA model to the data. +% Create initial log likelihood marks + +logl_init_flags=zeros(size(EEG.data(1,:))); +logl_init_flag_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indices','invert','on'); +logl_init_flags(logl_init_flag_inds)=(EEG.amica(1).models.Lt-min(EEG.amica(1).models.Lt))/(max(EEG.amica(1).models.Lt)-min(EEG.amica(1).models.Lt)); + +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'logl_init',[1,0.45,0],logl_init_flags}); +logging_log('INFO', 'TIME TO: RECREATE ICA COMPONENTS...'); +toc + +% Window the continous Data +logging_log('INFO', 'Windowing the continous data...'); +tic; +EEG=marks_continuous2epochs(EEG,'recurrence',1,'limits',[0 1]); +logging_log('INFO', 'TIME TO: WINDOW THE CONTINUOUS DATA...'); +toc +%% CALCULATE ICAACT SD +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates the IC standard Deviation by epoch window. Flags windows with +% too much standard deviation. + +% Calculate IC sd by window +logging_log('INFO', 'Calculating the IC sd array for window criteria...'); +tic; +epoch_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indexes','invert','on'); +[EEG,icaact_sd1_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'plot_figs','off'); + +% Create the windowing sd criteria +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_t_inds]=marks_array2flags(icaact_sd1_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +% Update marks rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +icsd_epoch_flags=zeros(size(EEG.data(1,:,:))); +icsd_epoch_flags(1,:,epoch_inds(flag_t_inds))=1; +icsd_epoch_flags=padflags(EEG,icsd_epoch_flags,1,'value',.5); +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_sd1',[.4,.6,.6],icsd_epoch_flags}); +logging_log('INFO', 'TIME TO: UPDATE REJECTION STRUCTURE WITH IC SD MARKS...'); +toc + +% Concat the windowed data back to continuous +logging_log('INFO', 'Concatenating windowed data...'); +tic; +EEG=marks_epochs2continuous(EEG); +EEG=eeg_checkset(EEG,'eventconsistency'); +logging_log('INFO', 'TIME TO: CONCATENATE THE WINDOWED DATA INTO CONTINUOUS DATA...'); +toc +%% FLAG GAPS +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Flags small gaps between rejection flags so that there are not as many +% short segments to be analyzed + +% Mark any flag gaps < 2 sec. +tic; +EEG=pop_marks_flag_gap(EEG,{'manual','mark_gap','ic_sd1'}, ... + 2000,'mark_gap',[.8,.8,.8],'offsets',[0 0],'ref_point','both'); + +%%COMBINE MARKS STRUCTURE INTO MANUAL FLAGS... +EEG=pop_marks_merge_labels(EEG,'time_info',{'manual','ic_sd1','mark_gap'},'target_label','manual'); +logging_log('INFO', 'TIME TO: FLAG GAPS AND COMBINE MARKS STRUCTURE INTO MANUAL FLAGS...'); +toc +%% SAVE compart_data.set FILE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% A saved copy is made before the file is purged to its ready for ICA +% version. + +tic; +logging_log('INFO', 'Saving file: ./sub-008/eeg/sub-008_task-faceFO_compart_data.set...'); +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_compart_data.set'); +logging_log('INFO', 'TIME TO: SAVE compart_data FILE...'); +toc; +%% SAVE compart_data_purge FILE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% All EEG parts associated with ICA need to be cleared as models seem to +% adapt based on previous data. Old and New flagged channels/time need to +% be purged again in preparation of ICA. + +% Clearing ICA Data +EEG.icaact = []; +EEG.icawinv = []; +EEG.icasphere = []; +EEG.icaweights = []; +EEG.icachansind = []; +EEG.amica = []; +logging_log('INFO', 'ICA data Reset'); + +% Removing Flagged Channels +tic; +sprintf('%s','Purging flagged channels...\n'); +EEG=pop_marks_select_data(EEG,'channel marks',[],'labels',{'manual','rank'},'remove','on'); +EEG=pop_marks_select_data(EEG,'time marks',[],'labels',{'manual'},'remove','on'); +EEG=eeg_checkset(EEG); +logging_log('INFO', 'TIME TO: PURGE DATA...'); +toc; + +% Saving File to enter next ICAs +logging_log('INFO', 'Saving the Param files'); +sprintf('%s','Saving file: ./sub-008/eeg/sub-008_task-faceFO_compart_data_purge.set...\n'); +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_compart_data_purge.set'); + +% Save diagnostic arrays +try OCTAVE_VERSION; + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_icaact_sd1_t.mat','icaact_sd1_t'); +catch + save('derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_icaact_sd1_t.mat','icaact_sd1_t'); +end + +%% SAVE AMICA PARAMETER FILES +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% A parameter file need to be made for each of the ICAs running after this +% script. In This case AMICA A, B and C are run. + +tic; +% A +% Saving AMICA Parameter File +swapstr=file_strswap('derivatives/BIDS-Lossless-EEG/code/misc/amica_default.param', ... + '[repstr_fdt_fname]','derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_compart_data_purge.fdt', ... + '[repstr_outpath]','derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_amicaout_A', ... + '[repstr_nbchan]', num2str(EEG.nbchan), ... + '[repstr_pnts]', sprintf('%12.0f',EEG.pnts), ... + '[repstr_tpp]', '8', ... + '[repstr_pca]', num2str(EEG.nbchan)); +fidparam=fopen('derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_A.param','w'); +fprintf(fidparam,'%s',swapstr); +fclose(fidparam); + +% B +% Saving AMICA Parameter File +swapstr=file_strswap('derivatives/BIDS-Lossless-EEG/code/misc/amica_default.param', ... + '[repstr_fdt_fname]','derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_compart_data_purge.fdt', ... + '[repstr_outpath]','derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_amicaout_B', ... + '[repstr_nbchan]', num2str(EEG.nbchan), ... + '[repstr_pnts]', sprintf('%12.0f',EEG.pnts), ... + '[repstr_tpp]', '8', ... + '[repstr_pca]', num2str(EEG.nbchan)); +fidparam=fopen('derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_B.param','w'); +fprintf(fidparam,'%s',swapstr); +fclose(fidparam); + +% C +% Saving AMICA Parameter File +swapstr=file_strswap('derivatives/BIDS-Lossless-EEG/code/misc/amica_default.param', ... + '[repstr_fdt_fname]','derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_compart_data_purge.fdt', ... + '[repstr_outpath]','derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_amicaout_C', ... + '[repstr_nbchan]', num2str(EEG.nbchan), ... + '[repstr_pnts]', sprintf('%12.0f',EEG.pnts), ... + '[repstr_tpp]', '8', ... + '[repstr_pca]', num2str(EEG.nbchan)); +fidparam=fopen('derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_C.param','w'); +fprintf(fidparam,'%s',swapstr); +fclose(fidparam); + +%% Create the Amica Output Folder +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Needed for MPI jobs on cluster. Otherwise race effect will occur. + +[~] = rmdir('derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_amicaout_A','s'); +[~] = rmdir('derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_amicaout_B','s'); +[~] = rmdir('derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_amicaout_C','s'); + + +mkdir 'derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_amicaout_A'; +mkdir 'derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_amicaout_B'; +mkdir 'derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_amicaout_C'; + +logging_log('INFO', 'TIME TO: CREATE AMICA PARAMETER FILES...'); +toc + +% FINISH +logging_log('INFO', '***********COMPART DATA COMPLETE!**********'); +logging_log('INFO', 'Scheduler: sbatch'); +print_chan_sample(EEG); diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/s03_compart_data_sub_009_task_faceFO_eeg.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/s03_compart_data_sub_009_task_faceFO_eeg.m new file mode 100644 index 000000000..4ea5c806d --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/s03_compart_data_sub_009_task_faceFO_eeg.m @@ -0,0 +1,285 @@ +warning off; + +pkg load signal; +pkg load parallel; +pkg load io; + +file_path = fileparts(mfilename('fullpath')); + +addpath([file_path '/../../code/dependencies/eeglab_lossless'], ... + [file_path '/../../code/dependencies/matlog'], ... + [file_path '/../../code/tools']); +cd '/scratch/tk11br/Face13/'; + +% eeglab auto loads the path correctly instead of shotgun blasting with +% addpath(genpath(<>)) as we were doing prior +if ~exist('EEG', 'var') + eeglab; +end +% Use matlog disp function override +logging_override_disp(true); +% Use matlog INFO level debugging +logging_set_log_level('INFO') + +% Don't run with traditional but enable some of the options +save_default_options ('-mat-binary'); +confirm_recursive_rmdir(false); + +% This makes | and & || and && in earlier version of Octave where +% | and & dont short curcuit by default +if exist('do_braindead_shortcircuit_evaluation', 'builtin') + do_braindead_shortcircuit_evaluation(true); +end + +% Options +pop_editoptions('option_savetwofiles', 1,'option_single',0); + +%% SCRIPT DESCRIPTION +% This script is loads the results of a preliminary ICA and uses the +% standard deviation of the components to flag and remove data time that +% does not fit a well behaved model. This will help make the next +% components stronger. The log likelihood of the model is also flagged for +% reference. Sections marked for high SD often have low log likelihoods +% as well. It then prepares the required parameter files for the parrallel amicas ABC. +% +%% From Config key_strswap Description +%----------------------------------------------------------------------------------------------------------------------------- +% data_path = derivatives/BIDS-Lossless-EEG Path to input and output data files assuming cd = work_path +% recur_sec = 1 Recurrence (sec) for artifact detection epoching (e.g. .3) +% limit_sec = [0 1] Limits (sec) for artifact detection epoching (e.g. [-.3 .3]) +% sd_t_meth = q Method used for flagging epochs (e.g. 'q' (quantiles), or 'na' (default)) +% sd_t_vals = [.3 .7] Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7]) +% sd_t_o = 6 z threshold for flagging epochs during standard deviation criteria (e.g. 6) +% sd_t_f_meth = fixed Fixed method used for flagging epochs (should be 'fixed') +% sd_t_f_vals = Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% sd_t_f_o = .2 z threshold for flagging epochs during fixed standard deviation criteria (e.g. .2) +% out_path = derivatives/BIDS-Lossless-EEG Relative path to output data files assuming cd = work_path +% min_gap_ms = 2000 Minimum time (ms) to allow between periods marked for rejection +% amica_param_file = derivatives/BIDS-Lossless-EEG/code/misc/amica_default.param template amicadefs.param file to modify +% amica_threads_s04 = 8 number of threads to use for running s04a, s04b, s04c amica scripts (Default: 8) + +%% LOAD SA DATASET +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Loads the batched EEG datafile and the AMICA output files. Since ICA is +% done on a channel subset the components need to be returned to the +% correct indicies. + +% Load the EEG datafile +logging_log('INFO', 'Loading set file: ./sub-009/eeg/sub-009_task-faceFO_sa.set...'); +tic; +EEG = pop_loadset('filepath','derivatives/BIDS-Lossless-EEG','filename','./sub-009/eeg/sub-009_task-faceFO_sa.set'); +EEG = eeg_checkset( EEG ); +logging_log('INFO', 'TIME TO: LOAD DATASET...'); +toc + +% Prepare returning components +EEG.icachansind = marks_label2index(EEG.marks.chan_info,{'manual','rank'},'indices','invert','on'); +icatimeind = find(~EEG.marks.time_info(1).flags); + +% Load AMICA Model 1 +tic; +EEG.amica(1).models=loadmodout15('derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_amicaout_init'); +EEG.icaweights=EEG.amica(1).models.W; +EEG.icasphere=EEG.amica(1).models.S(1:EEG.amica(1).models.num_pcs,:); +EEG=eeg_checkset(EEG); +logging_log('INFO', 'TIME TO: LOAD ICA INFO...'); +toc +%% PASS EEGDATA THROUGH THE AMICA MATRIX +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%Uses the ICA weights and the channel data to recreate the ICA components. + +tic; +% In place of EEG checkset +tmpdata = EEG.data(EEG.icachansind,:); +tmpindices = find(~sum(isnan(tmpdata))); +EEG.icaact = zeros(size(EEG.icaweights,1), size(tmpdata,2)); EEG.icaact(:) = NaN; +EEG.icaact(:,tmpindices) = (EEG.icaweights*EEG.icasphere)*tmpdata(:,tmpindices); +clear tmpdata; + +%% LOG LIKELIHOOD FLAGS +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%Creates marks based off of the log likelihood of the ICA model to the data. +% Create initial log likelihood marks + +logl_init_flags=zeros(size(EEG.data(1,:))); +logl_init_flag_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indices','invert','on'); +logl_init_flags(logl_init_flag_inds)=(EEG.amica(1).models.Lt-min(EEG.amica(1).models.Lt))/(max(EEG.amica(1).models.Lt)-min(EEG.amica(1).models.Lt)); + +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'logl_init',[1,0.45,0],logl_init_flags}); +logging_log('INFO', 'TIME TO: RECREATE ICA COMPONENTS...'); +toc + +% Window the continous Data +logging_log('INFO', 'Windowing the continous data...'); +tic; +EEG=marks_continuous2epochs(EEG,'recurrence',1,'limits',[0 1]); +logging_log('INFO', 'TIME TO: WINDOW THE CONTINUOUS DATA...'); +toc +%% CALCULATE ICAACT SD +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates the IC standard Deviation by epoch window. Flags windows with +% too much standard deviation. + +% Calculate IC sd by window +logging_log('INFO', 'Calculating the IC sd array for window criteria...'); +tic; +epoch_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indexes','invert','on'); +[EEG,icaact_sd1_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'plot_figs','off'); + +% Create the windowing sd criteria +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_t_inds]=marks_array2flags(icaact_sd1_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +% Update marks rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +icsd_epoch_flags=zeros(size(EEG.data(1,:,:))); +icsd_epoch_flags(1,:,epoch_inds(flag_t_inds))=1; +icsd_epoch_flags=padflags(EEG,icsd_epoch_flags,1,'value',.5); +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_sd1',[.4,.6,.6],icsd_epoch_flags}); +logging_log('INFO', 'TIME TO: UPDATE REJECTION STRUCTURE WITH IC SD MARKS...'); +toc + +% Concat the windowed data back to continuous +logging_log('INFO', 'Concatenating windowed data...'); +tic; +EEG=marks_epochs2continuous(EEG); +EEG=eeg_checkset(EEG,'eventconsistency'); +logging_log('INFO', 'TIME TO: CONCATENATE THE WINDOWED DATA INTO CONTINUOUS DATA...'); +toc +%% FLAG GAPS +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Flags small gaps between rejection flags so that there are not as many +% short segments to be analyzed + +% Mark any flag gaps < 2 sec. +tic; +EEG=pop_marks_flag_gap(EEG,{'manual','mark_gap','ic_sd1'}, ... + 2000,'mark_gap',[.8,.8,.8],'offsets',[0 0],'ref_point','both'); + +%%COMBINE MARKS STRUCTURE INTO MANUAL FLAGS... +EEG=pop_marks_merge_labels(EEG,'time_info',{'manual','ic_sd1','mark_gap'},'target_label','manual'); +logging_log('INFO', 'TIME TO: FLAG GAPS AND COMBINE MARKS STRUCTURE INTO MANUAL FLAGS...'); +toc +%% SAVE compart_data.set FILE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% A saved copy is made before the file is purged to its ready for ICA +% version. + +tic; +logging_log('INFO', 'Saving file: ./sub-009/eeg/sub-009_task-faceFO_compart_data.set...'); +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_compart_data.set'); +logging_log('INFO', 'TIME TO: SAVE compart_data FILE...'); +toc; +%% SAVE compart_data_purge FILE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% All EEG parts associated with ICA need to be cleared as models seem to +% adapt based on previous data. Old and New flagged channels/time need to +% be purged again in preparation of ICA. + +% Clearing ICA Data +EEG.icaact = []; +EEG.icawinv = []; +EEG.icasphere = []; +EEG.icaweights = []; +EEG.icachansind = []; +EEG.amica = []; +logging_log('INFO', 'ICA data Reset'); + +% Removing Flagged Channels +tic; +sprintf('%s','Purging flagged channels...\n'); +EEG=pop_marks_select_data(EEG,'channel marks',[],'labels',{'manual','rank'},'remove','on'); +EEG=pop_marks_select_data(EEG,'time marks',[],'labels',{'manual'},'remove','on'); +EEG=eeg_checkset(EEG); +logging_log('INFO', 'TIME TO: PURGE DATA...'); +toc; + +% Saving File to enter next ICAs +logging_log('INFO', 'Saving the Param files'); +sprintf('%s','Saving file: ./sub-009/eeg/sub-009_task-faceFO_compart_data_purge.set...\n'); +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_compart_data_purge.set'); + +% Save diagnostic arrays +try OCTAVE_VERSION; + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_icaact_sd1_t.mat','icaact_sd1_t'); +catch + save('derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_icaact_sd1_t.mat','icaact_sd1_t'); +end + +%% SAVE AMICA PARAMETER FILES +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% A parameter file need to be made for each of the ICAs running after this +% script. In This case AMICA A, B and C are run. + +tic; +% A +% Saving AMICA Parameter File +swapstr=file_strswap('derivatives/BIDS-Lossless-EEG/code/misc/amica_default.param', ... + '[repstr_fdt_fname]','derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_compart_data_purge.fdt', ... + '[repstr_outpath]','derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_amicaout_A', ... + '[repstr_nbchan]', num2str(EEG.nbchan), ... + '[repstr_pnts]', sprintf('%12.0f',EEG.pnts), ... + '[repstr_tpp]', '8', ... + '[repstr_pca]', num2str(EEG.nbchan)); +fidparam=fopen('derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_A.param','w'); +fprintf(fidparam,'%s',swapstr); +fclose(fidparam); + +% B +% Saving AMICA Parameter File +swapstr=file_strswap('derivatives/BIDS-Lossless-EEG/code/misc/amica_default.param', ... + '[repstr_fdt_fname]','derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_compart_data_purge.fdt', ... + '[repstr_outpath]','derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_amicaout_B', ... + '[repstr_nbchan]', num2str(EEG.nbchan), ... + '[repstr_pnts]', sprintf('%12.0f',EEG.pnts), ... + '[repstr_tpp]', '8', ... + '[repstr_pca]', num2str(EEG.nbchan)); +fidparam=fopen('derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_B.param','w'); +fprintf(fidparam,'%s',swapstr); +fclose(fidparam); + +% C +% Saving AMICA Parameter File +swapstr=file_strswap('derivatives/BIDS-Lossless-EEG/code/misc/amica_default.param', ... + '[repstr_fdt_fname]','derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_compart_data_purge.fdt', ... + '[repstr_outpath]','derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_amicaout_C', ... + '[repstr_nbchan]', num2str(EEG.nbchan), ... + '[repstr_pnts]', sprintf('%12.0f',EEG.pnts), ... + '[repstr_tpp]', '8', ... + '[repstr_pca]', num2str(EEG.nbchan)); +fidparam=fopen('derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_C.param','w'); +fprintf(fidparam,'%s',swapstr); +fclose(fidparam); + +%% Create the Amica Output Folder +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Needed for MPI jobs on cluster. Otherwise race effect will occur. + +[~] = rmdir('derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_amicaout_A','s'); +[~] = rmdir('derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_amicaout_B','s'); +[~] = rmdir('derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_amicaout_C','s'); + + +mkdir 'derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_amicaout_A'; +mkdir 'derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_amicaout_B'; +mkdir 'derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_amicaout_C'; + +logging_log('INFO', 'TIME TO: CREATE AMICA PARAMETER FILES...'); +toc + +% FINISH +logging_log('INFO', '***********COMPART DATA COMPLETE!**********'); +logging_log('INFO', 'Scheduler: sbatch'); +print_chan_sample(EEG); diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/s03_compart_data_sub_010_task_faceFO_eeg.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/s03_compart_data_sub_010_task_faceFO_eeg.m new file mode 100644 index 000000000..7699a1b2d --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/s03_compart_data_sub_010_task_faceFO_eeg.m @@ -0,0 +1,285 @@ +warning off; + +pkg load signal; +pkg load parallel; +pkg load io; + +file_path = fileparts(mfilename('fullpath')); + +addpath([file_path '/../../code/dependencies/eeglab_lossless'], ... + [file_path '/../../code/dependencies/matlog'], ... + [file_path '/../../code/tools']); +cd '/scratch/tk11br/Face13/'; + +% eeglab auto loads the path correctly instead of shotgun blasting with +% addpath(genpath(<>)) as we were doing prior +if ~exist('EEG', 'var') + eeglab; +end +% Use matlog disp function override +logging_override_disp(true); +% Use matlog INFO level debugging +logging_set_log_level('INFO') + +% Don't run with traditional but enable some of the options +save_default_options ('-mat-binary'); +confirm_recursive_rmdir(false); + +% This makes | and & || and && in earlier version of Octave where +% | and & dont short curcuit by default +if exist('do_braindead_shortcircuit_evaluation', 'builtin') + do_braindead_shortcircuit_evaluation(true); +end + +% Options +pop_editoptions('option_savetwofiles', 1,'option_single',0); + +%% SCRIPT DESCRIPTION +% This script is loads the results of a preliminary ICA and uses the +% standard deviation of the components to flag and remove data time that +% does not fit a well behaved model. This will help make the next +% components stronger. The log likelihood of the model is also flagged for +% reference. Sections marked for high SD often have low log likelihoods +% as well. It then prepares the required parameter files for the parrallel amicas ABC. +% +%% From Config key_strswap Description +%----------------------------------------------------------------------------------------------------------------------------- +% data_path = derivatives/BIDS-Lossless-EEG Path to input and output data files assuming cd = work_path +% recur_sec = 1 Recurrence (sec) for artifact detection epoching (e.g. .3) +% limit_sec = [0 1] Limits (sec) for artifact detection epoching (e.g. [-.3 .3]) +% sd_t_meth = q Method used for flagging epochs (e.g. 'q' (quantiles), or 'na' (default)) +% sd_t_vals = [.3 .7] Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7]) +% sd_t_o = 6 z threshold for flagging epochs during standard deviation criteria (e.g. 6) +% sd_t_f_meth = fixed Fixed method used for flagging epochs (should be 'fixed') +% sd_t_f_vals = Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% sd_t_f_o = .2 z threshold for flagging epochs during fixed standard deviation criteria (e.g. .2) +% out_path = derivatives/BIDS-Lossless-EEG Relative path to output data files assuming cd = work_path +% min_gap_ms = 2000 Minimum time (ms) to allow between periods marked for rejection +% amica_param_file = derivatives/BIDS-Lossless-EEG/code/misc/amica_default.param template amicadefs.param file to modify +% amica_threads_s04 = 8 number of threads to use for running s04a, s04b, s04c amica scripts (Default: 8) + +%% LOAD SA DATASET +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Loads the batched EEG datafile and the AMICA output files. Since ICA is +% done on a channel subset the components need to be returned to the +% correct indicies. + +% Load the EEG datafile +logging_log('INFO', 'Loading set file: ./sub-010/eeg/sub-010_task-faceFO_sa.set...'); +tic; +EEG = pop_loadset('filepath','derivatives/BIDS-Lossless-EEG','filename','./sub-010/eeg/sub-010_task-faceFO_sa.set'); +EEG = eeg_checkset( EEG ); +logging_log('INFO', 'TIME TO: LOAD DATASET...'); +toc + +% Prepare returning components +EEG.icachansind = marks_label2index(EEG.marks.chan_info,{'manual','rank'},'indices','invert','on'); +icatimeind = find(~EEG.marks.time_info(1).flags); + +% Load AMICA Model 1 +tic; +EEG.amica(1).models=loadmodout15('derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_amicaout_init'); +EEG.icaweights=EEG.amica(1).models.W; +EEG.icasphere=EEG.amica(1).models.S(1:EEG.amica(1).models.num_pcs,:); +EEG=eeg_checkset(EEG); +logging_log('INFO', 'TIME TO: LOAD ICA INFO...'); +toc +%% PASS EEGDATA THROUGH THE AMICA MATRIX +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%Uses the ICA weights and the channel data to recreate the ICA components. + +tic; +% In place of EEG checkset +tmpdata = EEG.data(EEG.icachansind,:); +tmpindices = find(~sum(isnan(tmpdata))); +EEG.icaact = zeros(size(EEG.icaweights,1), size(tmpdata,2)); EEG.icaact(:) = NaN; +EEG.icaact(:,tmpindices) = (EEG.icaweights*EEG.icasphere)*tmpdata(:,tmpindices); +clear tmpdata; + +%% LOG LIKELIHOOD FLAGS +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%Creates marks based off of the log likelihood of the ICA model to the data. +% Create initial log likelihood marks + +logl_init_flags=zeros(size(EEG.data(1,:))); +logl_init_flag_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indices','invert','on'); +logl_init_flags(logl_init_flag_inds)=(EEG.amica(1).models.Lt-min(EEG.amica(1).models.Lt))/(max(EEG.amica(1).models.Lt)-min(EEG.amica(1).models.Lt)); + +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'logl_init',[1,0.45,0],logl_init_flags}); +logging_log('INFO', 'TIME TO: RECREATE ICA COMPONENTS...'); +toc + +% Window the continous Data +logging_log('INFO', 'Windowing the continous data...'); +tic; +EEG=marks_continuous2epochs(EEG,'recurrence',1,'limits',[0 1]); +logging_log('INFO', 'TIME TO: WINDOW THE CONTINUOUS DATA...'); +toc +%% CALCULATE ICAACT SD +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates the IC standard Deviation by epoch window. Flags windows with +% too much standard deviation. + +% Calculate IC sd by window +logging_log('INFO', 'Calculating the IC sd array for window criteria...'); +tic; +epoch_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indexes','invert','on'); +[EEG,icaact_sd1_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'plot_figs','off'); + +% Create the windowing sd criteria +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_t_inds]=marks_array2flags(icaact_sd1_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +% Update marks rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +icsd_epoch_flags=zeros(size(EEG.data(1,:,:))); +icsd_epoch_flags(1,:,epoch_inds(flag_t_inds))=1; +icsd_epoch_flags=padflags(EEG,icsd_epoch_flags,1,'value',.5); +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_sd1',[.4,.6,.6],icsd_epoch_flags}); +logging_log('INFO', 'TIME TO: UPDATE REJECTION STRUCTURE WITH IC SD MARKS...'); +toc + +% Concat the windowed data back to continuous +logging_log('INFO', 'Concatenating windowed data...'); +tic; +EEG=marks_epochs2continuous(EEG); +EEG=eeg_checkset(EEG,'eventconsistency'); +logging_log('INFO', 'TIME TO: CONCATENATE THE WINDOWED DATA INTO CONTINUOUS DATA...'); +toc +%% FLAG GAPS +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Flags small gaps between rejection flags so that there are not as many +% short segments to be analyzed + +% Mark any flag gaps < 2 sec. +tic; +EEG=pop_marks_flag_gap(EEG,{'manual','mark_gap','ic_sd1'}, ... + 2000,'mark_gap',[.8,.8,.8],'offsets',[0 0],'ref_point','both'); + +%%COMBINE MARKS STRUCTURE INTO MANUAL FLAGS... +EEG=pop_marks_merge_labels(EEG,'time_info',{'manual','ic_sd1','mark_gap'},'target_label','manual'); +logging_log('INFO', 'TIME TO: FLAG GAPS AND COMBINE MARKS STRUCTURE INTO MANUAL FLAGS...'); +toc +%% SAVE compart_data.set FILE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% A saved copy is made before the file is purged to its ready for ICA +% version. + +tic; +logging_log('INFO', 'Saving file: ./sub-010/eeg/sub-010_task-faceFO_compart_data.set...'); +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_compart_data.set'); +logging_log('INFO', 'TIME TO: SAVE compart_data FILE...'); +toc; +%% SAVE compart_data_purge FILE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% All EEG parts associated with ICA need to be cleared as models seem to +% adapt based on previous data. Old and New flagged channels/time need to +% be purged again in preparation of ICA. + +% Clearing ICA Data +EEG.icaact = []; +EEG.icawinv = []; +EEG.icasphere = []; +EEG.icaweights = []; +EEG.icachansind = []; +EEG.amica = []; +logging_log('INFO', 'ICA data Reset'); + +% Removing Flagged Channels +tic; +sprintf('%s','Purging flagged channels...\n'); +EEG=pop_marks_select_data(EEG,'channel marks',[],'labels',{'manual','rank'},'remove','on'); +EEG=pop_marks_select_data(EEG,'time marks',[],'labels',{'manual'},'remove','on'); +EEG=eeg_checkset(EEG); +logging_log('INFO', 'TIME TO: PURGE DATA...'); +toc; + +% Saving File to enter next ICAs +logging_log('INFO', 'Saving the Param files'); +sprintf('%s','Saving file: ./sub-010/eeg/sub-010_task-faceFO_compart_data_purge.set...\n'); +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_compart_data_purge.set'); + +% Save diagnostic arrays +try OCTAVE_VERSION; + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_icaact_sd1_t.mat','icaact_sd1_t'); +catch + save('derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_icaact_sd1_t.mat','icaact_sd1_t'); +end + +%% SAVE AMICA PARAMETER FILES +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% A parameter file need to be made for each of the ICAs running after this +% script. In This case AMICA A, B and C are run. + +tic; +% A +% Saving AMICA Parameter File +swapstr=file_strswap('derivatives/BIDS-Lossless-EEG/code/misc/amica_default.param', ... + '[repstr_fdt_fname]','derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_compart_data_purge.fdt', ... + '[repstr_outpath]','derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_amicaout_A', ... + '[repstr_nbchan]', num2str(EEG.nbchan), ... + '[repstr_pnts]', sprintf('%12.0f',EEG.pnts), ... + '[repstr_tpp]', '8', ... + '[repstr_pca]', num2str(EEG.nbchan)); +fidparam=fopen('derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_A.param','w'); +fprintf(fidparam,'%s',swapstr); +fclose(fidparam); + +% B +% Saving AMICA Parameter File +swapstr=file_strswap('derivatives/BIDS-Lossless-EEG/code/misc/amica_default.param', ... + '[repstr_fdt_fname]','derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_compart_data_purge.fdt', ... + '[repstr_outpath]','derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_amicaout_B', ... + '[repstr_nbchan]', num2str(EEG.nbchan), ... + '[repstr_pnts]', sprintf('%12.0f',EEG.pnts), ... + '[repstr_tpp]', '8', ... + '[repstr_pca]', num2str(EEG.nbchan)); +fidparam=fopen('derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_B.param','w'); +fprintf(fidparam,'%s',swapstr); +fclose(fidparam); + +% C +% Saving AMICA Parameter File +swapstr=file_strswap('derivatives/BIDS-Lossless-EEG/code/misc/amica_default.param', ... + '[repstr_fdt_fname]','derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_compart_data_purge.fdt', ... + '[repstr_outpath]','derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_amicaout_C', ... + '[repstr_nbchan]', num2str(EEG.nbchan), ... + '[repstr_pnts]', sprintf('%12.0f',EEG.pnts), ... + '[repstr_tpp]', '8', ... + '[repstr_pca]', num2str(EEG.nbchan)); +fidparam=fopen('derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_C.param','w'); +fprintf(fidparam,'%s',swapstr); +fclose(fidparam); + +%% Create the Amica Output Folder +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Needed for MPI jobs on cluster. Otherwise race effect will occur. + +[~] = rmdir('derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_amicaout_A','s'); +[~] = rmdir('derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_amicaout_B','s'); +[~] = rmdir('derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_amicaout_C','s'); + + +mkdir 'derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_amicaout_A'; +mkdir 'derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_amicaout_B'; +mkdir 'derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_amicaout_C'; + +logging_log('INFO', 'TIME TO: CREATE AMICA PARAMETER FILES...'); +toc + +% FINISH +logging_log('INFO', '***********COMPART DATA COMPLETE!**********'); +logging_log('INFO', 'Scheduler: sbatch'); +print_chan_sample(EEG); diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/submit.sh b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/submit.sh new file mode 100644 index 000000000..d33bb59ab --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/submit.sh @@ -0,0 +1,24 @@ +#!/bin/bash +set -e +module load gcc/7.3.0; +module load octave/4.4.1; + +sbatch --job-name=s03_compart_data_sub-001_task-faceFO_eeg --output=derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/s03_compart_data_sub-001_task-faceFO_eeg.log --dependency=afterok:19977179 --mem-per-cpu=16000m --time=00:30:00 --account=def-bucanl <(echo -e '#!/bin/bash\noctave derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/batch_context/batch/exec_func/octave_exit_wrapper.m derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/s03_compart_data_sub_001_task_faceFO_eeg.m') + +sbatch --job-name=s03_compart_data_sub-002_task-faceFO_eeg --output=derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/s03_compart_data_sub-002_task-faceFO_eeg.log --dependency=afterok:19977180 --mem-per-cpu=16000m --time=00:30:00 --account=def-bucanl <(echo -e '#!/bin/bash\noctave derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/batch_context/batch/exec_func/octave_exit_wrapper.m derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/s03_compart_data_sub_002_task_faceFO_eeg.m') + +sbatch --job-name=s03_compart_data_sub-003_task-faceFO_eeg --output=derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/s03_compart_data_sub-003_task-faceFO_eeg.log --dependency=afterok:19977181 --mem-per-cpu=16000m --time=00:30:00 --account=def-bucanl <(echo -e '#!/bin/bash\noctave derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/batch_context/batch/exec_func/octave_exit_wrapper.m derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/s03_compart_data_sub_003_task_faceFO_eeg.m') + +sbatch --job-name=s03_compart_data_sub-004_task-faceFO_eeg --output=derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/s03_compart_data_sub-004_task-faceFO_eeg.log --dependency=afterok:19977182 --mem-per-cpu=16000m --time=00:30:00 --account=def-bucanl <(echo -e '#!/bin/bash\noctave derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/batch_context/batch/exec_func/octave_exit_wrapper.m derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/s03_compart_data_sub_004_task_faceFO_eeg.m') + +sbatch --job-name=s03_compart_data_sub-005_task-faceFO_eeg --output=derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/s03_compart_data_sub-005_task-faceFO_eeg.log --dependency=afterok:19977183 --mem-per-cpu=16000m --time=00:30:00 --account=def-bucanl <(echo -e '#!/bin/bash\noctave derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/batch_context/batch/exec_func/octave_exit_wrapper.m derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/s03_compart_data_sub_005_task_faceFO_eeg.m') + +sbatch --job-name=s03_compart_data_sub-006_task-faceFO_eeg --output=derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/s03_compart_data_sub-006_task-faceFO_eeg.log --dependency=afterok:19977184 --mem-per-cpu=16000m --time=00:30:00 --account=def-bucanl <(echo -e '#!/bin/bash\noctave derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/batch_context/batch/exec_func/octave_exit_wrapper.m derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/s03_compart_data_sub_006_task_faceFO_eeg.m') + +sbatch --job-name=s03_compart_data_sub-007_task-faceFO_eeg --output=derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/s03_compart_data_sub-007_task-faceFO_eeg.log --dependency=afterok:19977185 --mem-per-cpu=16000m --time=00:30:00 --account=def-bucanl <(echo -e '#!/bin/bash\noctave derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/batch_context/batch/exec_func/octave_exit_wrapper.m derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/s03_compart_data_sub_007_task_faceFO_eeg.m') + +sbatch --job-name=s03_compart_data_sub-008_task-faceFO_eeg --output=derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/s03_compart_data_sub-008_task-faceFO_eeg.log --dependency=afterok:19977186 --mem-per-cpu=16000m --time=00:30:00 --account=def-bucanl <(echo -e '#!/bin/bash\noctave derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/batch_context/batch/exec_func/octave_exit_wrapper.m derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/s03_compart_data_sub_008_task_faceFO_eeg.m') + +sbatch --job-name=s03_compart_data_sub-009_task-faceFO_eeg --output=derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/s03_compart_data_sub-009_task-faceFO_eeg.log --dependency=afterok:19977187 --mem-per-cpu=16000m --time=00:30:00 --account=def-bucanl <(echo -e '#!/bin/bash\noctave derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/batch_context/batch/exec_func/octave_exit_wrapper.m derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/s03_compart_data_sub_009_task_faceFO_eeg.m') + +sbatch --job-name=s03_compart_data_sub-010_task-faceFO_eeg --output=derivatives/BIDS-Lossless-EEG/log/s03_compart_data-2019-10-01T14-35-11/s03_compart_data_sub-010_task-faceFO_eeg.log --dependency=afterok:19977188 --mem-per-cpu=16000m --time=00:30:00 --account=def-bucanl <(echo -e '#!/bin/bash\noctave derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/batch_context/batch/exec_func/octave_exit_wrapper.m 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literal 0 HcmV?d00001 diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s04b_amica-2019-10-01T14-35-11/submit.sh b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s04b_amica-2019-10-01T14-35-11/submit.sh new file mode 100644 index 000000000..60a6b7870 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s04b_amica-2019-10-01T14-35-11/submit.sh @@ -0,0 +1,29 @@ +#!/bin/bash +set -e +#OMPI_MCA_mpi_cuda_support=0 +#export OMPI_MCA_mpi_cuda_support + +module unload intel +module load intel/2017.1 +module load imkl/2017.1.132 + +sbatch --job-name=s04b_amica_sub-001_task-faceFO_eeg --output=derivatives/BIDS-Lossless-EEG/log/s04b_amica-2019-10-01T14-35-11/sub-001_task-faceFO_eeg.log --dependency=afterok:19977194 --mem-per-cpu=2000m --time=02:00:00 --cpus-per-task=8 --account=def-bucanl <(echo -e '#!/bin/bash\nsrun derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/amica/amica15 derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_B.param +') +sbatch --job-name=s04b_amica_sub-002_task-faceFO_eeg --output=derivatives/BIDS-Lossless-EEG/log/s04b_amica-2019-10-01T14-35-11/sub-002_task-faceFO_eeg.log --dependency=afterok:19977195 --mem-per-cpu=2000m --time=02:00:00 --cpus-per-task=8 --account=def-bucanl <(echo -e '#!/bin/bash\nsrun derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/amica/amica15 derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_B.param +') +sbatch --job-name=s04b_amica_sub-003_task-faceFO_eeg --output=derivatives/BIDS-Lossless-EEG/log/s04b_amica-2019-10-01T14-35-11/sub-003_task-faceFO_eeg.log --dependency=afterok:19977196 --mem-per-cpu=2000m --time=02:00:00 --cpus-per-task=8 --account=def-bucanl <(echo -e '#!/bin/bash\nsrun derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/amica/amica15 derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_B.param +') +sbatch --job-name=s04b_amica_sub-004_task-faceFO_eeg --output=derivatives/BIDS-Lossless-EEG/log/s04b_amica-2019-10-01T14-35-11/sub-004_task-faceFO_eeg.log --dependency=afterok:19977197 --mem-per-cpu=2000m --time=02:00:00 --cpus-per-task=8 --account=def-bucanl <(echo -e '#!/bin/bash\nsrun derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/amica/amica15 derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_B.param +') +sbatch --job-name=s04b_amica_sub-005_task-faceFO_eeg --output=derivatives/BIDS-Lossless-EEG/log/s04b_amica-2019-10-01T14-35-11/sub-005_task-faceFO_eeg.log --dependency=afterok:19977198 --mem-per-cpu=2000m --time=02:00:00 --cpus-per-task=8 --account=def-bucanl <(echo -e '#!/bin/bash\nsrun derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/amica/amica15 derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_B.param +') +sbatch --job-name=s04b_amica_sub-006_task-faceFO_eeg --output=derivatives/BIDS-Lossless-EEG/log/s04b_amica-2019-10-01T14-35-11/sub-006_task-faceFO_eeg.log --dependency=afterok:19977199 --mem-per-cpu=2000m --time=02:00:00 --cpus-per-task=8 --account=def-bucanl <(echo -e '#!/bin/bash\nsrun derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/amica/amica15 derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_B.param +') +sbatch --job-name=s04b_amica_sub-007_task-faceFO_eeg --output=derivatives/BIDS-Lossless-EEG/log/s04b_amica-2019-10-01T14-35-11/sub-007_task-faceFO_eeg.log --dependency=afterok:19977200 --mem-per-cpu=2000m --time=02:00:00 --cpus-per-task=8 --account=def-bucanl <(echo -e '#!/bin/bash\nsrun derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/amica/amica15 derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_B.param +') +sbatch --job-name=s04b_amica_sub-008_task-faceFO_eeg --output=derivatives/BIDS-Lossless-EEG/log/s04b_amica-2019-10-01T14-35-11/sub-008_task-faceFO_eeg.log --dependency=afterok:19977201 --mem-per-cpu=2000m --time=02:00:00 --cpus-per-task=8 --account=def-bucanl <(echo -e '#!/bin/bash\nsrun derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/amica/amica15 derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_B.param +') +sbatch --job-name=s04b_amica_sub-009_task-faceFO_eeg --output=derivatives/BIDS-Lossless-EEG/log/s04b_amica-2019-10-01T14-35-11/sub-009_task-faceFO_eeg.log --dependency=afterok:19977202 --mem-per-cpu=2000m --time=02:00:00 --cpus-per-task=8 --account=def-bucanl <(echo -e '#!/bin/bash\nsrun derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/amica/amica15 derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_B.param +') +sbatch --job-name=s04b_amica_sub-010_task-faceFO_eeg 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HcmV?d00001 diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s04c_amica-2019-10-01T14-35-11/submit.sh b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s04c_amica-2019-10-01T14-35-11/submit.sh new file mode 100644 index 000000000..7ac429374 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s04c_amica-2019-10-01T14-35-11/submit.sh @@ -0,0 +1,29 @@ +#!/bin/bash +set -e +#OMPI_MCA_mpi_cuda_support=0 +#export OMPI_MCA_mpi_cuda_support + +module unload intel +module load intel/2017.1 +module load imkl/2017.1.132 + +sbatch --job-name=s04c_amica_sub-001_task-faceFO_eeg --output=derivatives/BIDS-Lossless-EEG/log/s04c_amica-2019-10-01T14-35-11/sub-001_task-faceFO_eeg.log --dependency=afterok:19977194 --mem-per-cpu=2000m --time=02:00:00 --cpus-per-task=8 --account=def-bucanl <(echo -e '#!/bin/bash\nsrun derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/amica/amica15 derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_C.param +') +sbatch --job-name=s04c_amica_sub-002_task-faceFO_eeg --output=derivatives/BIDS-Lossless-EEG/log/s04c_amica-2019-10-01T14-35-11/sub-002_task-faceFO_eeg.log --dependency=afterok:19977195 --mem-per-cpu=2000m --time=02:00:00 --cpus-per-task=8 --account=def-bucanl <(echo -e '#!/bin/bash\nsrun derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/amica/amica15 derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_C.param +') +sbatch --job-name=s04c_amica_sub-003_task-faceFO_eeg --output=derivatives/BIDS-Lossless-EEG/log/s04c_amica-2019-10-01T14-35-11/sub-003_task-faceFO_eeg.log --dependency=afterok:19977196 --mem-per-cpu=2000m --time=02:00:00 --cpus-per-task=8 --account=def-bucanl <(echo -e '#!/bin/bash\nsrun derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/amica/amica15 derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_C.param +') +sbatch --job-name=s04c_amica_sub-004_task-faceFO_eeg --output=derivatives/BIDS-Lossless-EEG/log/s04c_amica-2019-10-01T14-35-11/sub-004_task-faceFO_eeg.log --dependency=afterok:19977197 --mem-per-cpu=2000m --time=02:00:00 --cpus-per-task=8 --account=def-bucanl <(echo -e '#!/bin/bash\nsrun derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/amica/amica15 derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_C.param +') +sbatch --job-name=s04c_amica_sub-005_task-faceFO_eeg --output=derivatives/BIDS-Lossless-EEG/log/s04c_amica-2019-10-01T14-35-11/sub-005_task-faceFO_eeg.log --dependency=afterok:19977198 --mem-per-cpu=2000m --time=02:00:00 --cpus-per-task=8 --account=def-bucanl <(echo -e '#!/bin/bash\nsrun derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/amica/amica15 derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_C.param +') +sbatch --job-name=s04c_amica_sub-006_task-faceFO_eeg 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zc2xGZLz>oMFq~42%FZLIZ2-(;eC1l`r=MbN5L`(6ND7q~a30ux5x90R5{z@a(v4~h z=`4lz<*?&IboHyl{x6RdeR`3mkIH*9$B(ESw$41xlR0t5vG1=MH9FD)O9r2juxANlWCgs(@2e;%9F^u5?`EcE?l?AP}<{5)2A?t8J{m_Ghx?AI^GKaagQ|4!_; vZk70Dti)H(K)) as we were doing prior +if ~exist('EEG', 'var') + eeglab; +end +% Use matlog disp function override +logging_override_disp(true); +% Use matlog INFO level debugging +logging_set_log_level('INFO') + +% Don't run with traditional but enable some of the options +save_default_options ('-mat-binary'); +confirm_recursive_rmdir(false); + +% This makes | and & || and && in earlier version of Octave where +% | and & dont short curcuit by default +if exist('do_braindead_shortcircuit_evaluation', 'builtin') + do_braindead_shortcircuit_evaluation(true); +end + +% Options +pop_editoptions('option_savetwofiles', 1,'option_single',0); + +%% SCRIPT DESCRIPTION +% This script combineds the results of the amicas A,B and C. It takes this information and using ICAtest it determines which components were +% and were not replicated across and flags them, along with time sections with high standard deviation. Beta,theta and Alpha power is calculated +% arcoss time and is marked but not primed for removal. +% +%% From Config key_strswap Description +%----------------------------------------------------------------------------------------------------------------------------- +% in_path = derivatives/BIDS-Lossless-EEG Relative path to input data files assuming cd = work_path +% recur_sec = 1 Recurrence (sec) for the current artifact detection epoching (e.g. 1) +% limit_sec = [0 1] Limits (sec) for the current artifact detection epoching (e.g. [0 1]) +% sd_t_meth = q Method used for flagging epochs (e.g. 'q' (quantiles), or 'na' (default)) +% sd_t_vals = [.3 .7] Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7]) +% sd_t_o = 6 z threshold for flagging epochs during standard deviation criteria (e.g. 6) +% sd_t_f_meth = fixed Fixed method used for flagging epochs (should be 'fixed') +% sd_t_f_vals = Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% sd_t_f_o = .2 z threshold for flagging epochs during fixed standard deviation criteria (e.g. .2) +% min_gap_ms = 2000 Minimum time (ms) to allow between periods marked for rejection +% icr_crit = 0.05 False discovery rate of component replicatablilty, measure of sensitivity % +% dip_vol = derivatives/BIDS-Lossless-EEG/code/misc/standard_vol.mat file path/name of the standard volume file used in dipole fitting (e.g. derivatives/lossless/code/misc/standard_vol.mat). +% dip_mri = derivatives/BIDS-Lossless-EEG/code/misc/standard_mri.mat file path/name of the standard mri file used in dipole fitting (e.g. derivatives/lossless/code/misc/standard_mri.mat). +% dip_elc = derivatives/BIDS-Lossless-EEG/code/misc/standard_1020.elc file path/name of the standard electrode file used in dipole fitting (derivatives/lossless/code/misc/standard_1020.elc). +% dip_transmat = [0 0 0 0 0 0 1 1 1] Transformation matrix to apply during dipole fit (e.g. [0 0 0 0 0 0 1 1 1]) +% cor_transmat = [0,0,0,0,0,1.57,1,1,1] The transformation matrix to apply between dipole fitting to the standard head and performing ICLabel to the EEGLAB default orientation (e.g.[0,0,0,0,0,1.57,1,1,1]) +% cor_nosedir = +X Nose direction of montage to apply between dipole fitting to the standard head and performing ICLabel to the EEGLAB default orientation (e.g. +Y) +% out_path = derivatives/BIDS-Lossless-EEG Relative path to output data files assuming cd = work_path + +%% LOAD COMPART DATASET +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Loads the data file and the first amica model. + +logging_log('INFO', 'Loading set file: ./sub-001/eeg/sub-001_task-faceFO_compart_data.set...'); +tic; +EEG = pop_loadset('filepath','derivatives/BIDS-Lossless-EEG','filename','./sub-001/eeg/sub-001_task-faceFO_compart_data.set'); +EEG = eeg_checkset( EEG ); +logging_log('INFO', 'TIME TO: LOAD DATASET...'); +toc + +% Set up the ICA channels +EEG.icachansind = marks_label2index(EEG.marks.chan_info,{'manual','rank'},'indices','invert','on'); + +% Load Amica A +logging_log('INFO', 'Loading Amica ABC...'); +tic; +EEG.amica(2).models=loadmodout15('derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_amicaout_A'); +EEG.icaweights=EEG.amica(2).models.W; +EEG.icasphere=EEG.amica(2).models.S(1:EEG.amica(2).models.num_pcs,:); + +%% PASS EEG DATA THROUGH THE AMICA MATRIX +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Loads the B and C amica models. Amica A is used to fill in the ICA information. + +% Instead of EEG=eeg_checkset(EEG); +tmpdata = EEG.data(EEG.icachansind,:); +tmpindices = find(~sum(isnan(tmpdata))); +EEG.icaact = zeros(size(EEG.icaweights,1), size(tmpdata,2)); EEG.icaact(:) = NaN; +EEG.icaact(:,tmpindices) = (EEG.icaweights*EEG.icasphere)*tmpdata(:,tmpindices); +clear tmpdata; + +% Create winv for topo plots manually +EEG.icawinv = []; +EEG.icawinv = pinv(EEG.icaweights * EEG.icasphere); + +% Load Amica B +EEG.amica(3).models=loadmodout15('derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_amicaout_B'); +% Load Amica C +EEG.amica(4).models=loadmodout15('derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_amicaout_C'); +logging_log('INFO', 'TIME TO: LOAD ICA ABC INFO...'); +toc + +%% UPDATE REJECTION STRUCTURE... +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Creating flag marks to based on the log liklyhodd models found for each of the ICA models + +% Flags for amicas A B and C +logging_log('INFO', 'Updating latflaginfo structure...'); +tic; + +% A +logl_A_flags=zeros(size(EEG.data(1,:))); +logl_A_flag_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indices','invert','on'); + +% Check to see if the overwrite will be successful. If the overwirte is a different size then some of the old data will be retained. +if length(EEG.amica(2).models.Lt) ~= length(logl_A_flag_inds) + EEG.amica(2).models.Lt = EEG.amica(2).models.Lt(1:length(logl_A_flag_inds)); + warning('YOU FORGOT TO DELETE YOUR OLD AMICA FILES AND THE NEW CHANNEL SIZES DO NOT MATCH UP'); +end + +logl_A_flags(logl_A_flag_inds)=(EEG.amica(2).models.Lt-min(EEG.amica(2).models.Lt))/(max(EEG.amica(2).models.Lt)-min(EEG.amica(2).models.Lt)); +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'logl_A',[1,0.45,0],logl_A_flags}); +clear logl_A_flags; +% B +logl_B_flags=zeros(size(EEG.data(1,:))); +logl_B_flag_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indices','invert','on'); +logl_B_flags(logl_B_flag_inds)=(EEG.amica(3).models.Lt-min(EEG.amica(3).models.Lt))/(max(EEG.amica(3).models.Lt)-min(EEG.amica(3).models.Lt)); +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'logl_B',[1,0.45,0],logl_B_flags}); +clear logl_B_flags; +% C +logl_C_flags=zeros(size(EEG.data(1,:))); +logl_C_flag_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indices','invert','on'); +logl_C_flags(logl_C_flag_inds)=(EEG.amica(4).models.Lt-min(EEG.amica(4).models.Lt))/(max(EEG.amica(4).models.Lt)-min(EEG.amica(4).models.Lt)); +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'logl_C',[1,0.45,0],logl_C_flags}); +clear logl_C_flags; + +logging_log('INFO', 'TIME TO: UPDATE REJECTION STRUCTURE...'); +toc + +%% IDENTIFY RELIABLE COMPONENTS USING ISCTEST +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Gives the A variable of each of the amicas to the function icatest. Returned are the indecies of the +% Components that were not replicated across all 3 of the models. + +tic; +% Clear a in case of local models are saved already +a = []; +for i=2:length(EEG.amica); + a(:,:,i-1)=EEG.amica(i).models(1).A; +end + +% Calculating the replicated component flag +% Replicate criteria changed from 0.05,0.05 to 0.1,0.1 to be less sensitive +ca=isctest(a,0.05,0.05,'mixing'); +if isempty(ca) + mess = ['ISCTest failed to return reasonable values. ISCTest is ' ... + 'meant to find duplicate channels in amica_{A,B,C} if this ' ... + 'failed consider investigating the datafile']; + logging_log('ERROR', mess); + error(mess); +end +icr_inds=setdiff(1:min(size(EEG.amica(2).models(1).A)),ca(:,1)); + +% Creating the replicated component flag +logging_log('INFO', 'Updating chflaginfo structure...'); +icr_comp_flags=zeros(min(size(EEG.amica(2).models(1).A)),1); +icr_comp_flags(icr_inds)=1; +EEG.marks=marks_add_label(EEG.marks,'comp_info', ... + {'ic_rt',[0,0,1],[0,0,1],-1,icr_comp_flags}); +logging_log('INFO', 'TIME TO: EDIT FIND REPLICATED COMPONENTS AND UPDATE COMPINFO STRUCT...'); +toc + +% Window the continuous data +logging_log('INFO', 'Windowing the continous data...'); +tic; +EEG=marks_continuous2epochs(EEG,'recurrence',1,'limits',[0 1]); +logging_log('INFO', 'TIME TO: WINDOW THE CONTINUOUS DATA...'); +toc + +%% CALCULATE ICAACT SD +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Similarly to the IC_SD flag created in compart. Marks sections of time where the components +% have an abnormally high standard deviation. + +% Calculating the replicated component flag +logging_log('INFO', 'Calculating the IC sd array for window criteria...'); +tic; +epoch_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indexes','invert','on'); +[EEG,icaact_sd2_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'varmeasure','absmean', ... + 'detrend','off', ... + 'plot_figs','off'); + +% Creating the ic_sd component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_sd_t_inds]=marks_array2flags(icaact_sd2_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); + +icsd_epoch_flags=zeros(size(EEG.data(1,:,:))); +icsd_epoch_flags(1,:,epoch_inds(flag_sd_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_sd2',[.4,.6,.6],icsd_epoch_flags}); +logging_log('INFO', 'TIME TO: EDIT FIND IC_SD AND UPDATE TIMEINFO REJECTION STRUCT...'); +toc +clear icsd_epoch_flags; + +%% CALCULATE DELTA/THETA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high Theta/Alpha Power + +% Calculating the Delta/Theta component flag +logging_log('INFO', 'Calculating the delta/theta IC power array for window criteria...'); +tic; +[EEG,icaact_dt_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[0 7], ... + 'plot_figs','off'); + +% Creating the Delta/Theta component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_dt_t_inds]=marks_array2flags(icaact_dt_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +icdt_epoch_flags=zeros(size(EEG.data(1,:,:))); +icdt_epoch_flags(1,:,epoch_inds(flag_dt_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_dt',[1,1,0],icdt_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE DELTA/THETA ICAACT POWER AND UPDATE MARKS...'); +toc +clear icdt_epoch_flags; + +%% CALCULATE ALPHA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high Alpha Power + +% Calculating the Alpha component flag +logging_log('INFO', 'Calculating the alpha IC power array for window criteria...'); +tic; +[EEG,icaact_a_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[8 13], ... + 'plot_figs','off'); + +% Creating the Alpha component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_a_t_inds]=marks_array2flags(icaact_a_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +ica_epoch_flags=zeros(size(EEG.data(1,:,:))); +ica_epoch_flags(1,:,epoch_inds(flag_a_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_a',[1,1,0],ica_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE ALPHA ICAACT POWER AND UPDATE MARKS...'); +toc +clear ica_epoch_flags; + +%% CALCULATE BETA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high Beta Power. +% Note that at the moment this mark is used for removals. + +% Calculating the Beta component flag +logging_log('INFO', 'Calculating the beta IC power array for window criteria...'); +tic; +[EEG,icaact_b_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[14 30], ... + 'plot_figs','off'); +logging_log('INFO', 'TIME TO: CALCULATE BETA ICAACT POWER...'); + +% Creating the Beta component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_b_t_inds]=marks_array2flags(icaact_b_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +logging_log('INFO', 'TIME TO: CREATE WINDOW CRITERIA VECTOR...'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +icb_epoch_flags=zeros(size(EEG.data(1,:,:))); +icb_epoch_flags(1,:,epoch_inds(flag_b_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_b',[0,1,1],icb_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE BETA ICAACT POWER AND UPDATE MARKS...'); +toc +clear icb_epoch_flags; + +%% CALCULATE LOW GAMMA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high Low-Gamma Power. +% Note that at the moment this mark is used for removals. + +% Calculating the Beta component flag +logging_log('INFO', 'Calculating the low-gamma IC power array for window criteria...'); +tic; +[EEG,icaact_lg_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[31 60], ... + 'plot_figs','off'); +logging_log('INFO', 'TIME TO: CALCULATE LOW-GAMMA ICAACT POWER...'); + +% Creating the LOW-GAMMA component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_lg_t_inds]=marks_array2flags(icaact_lg_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +logging_log('INFO', 'TIME TO: CREATE WINDOW CRITERIA VECTOR...'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +iclg_epoch_flags=zeros(size(EEG.data(1,:,:))); +iclg_epoch_flags(1,:,epoch_inds(flag_lg_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_lg',[0,1,1],iclg_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE LOW-GAMMA ICAACT POWER AND UPDATE MARKS...'); +toc +clear iclg_epoch_flags; + + +%% CALCULATE HIGH GAMMA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high High-Gamma Power. +% Note that at the moment this mark is used for removals. + +% Calculating the Beta component flag +logging_log('INFO', 'Calculating the high-gamma IC power array for window criteria...'); +tic; +[EEG,icaact_hg_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[61 100], ... + 'plot_figs','off'); +logging_log('INFO', 'TIME TO: CALCULATE HIGH-GAMMA ICAACT POWER...'); + +% Creating the HIGH-GAMMA component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_hg_t_inds]=marks_array2flags(icaact_hg_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +logging_log('INFO', 'TIME TO: CREATE WINDOW CRITERIA VECTOR...'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +ichg_epoch_flags=zeros(size(EEG.data(1,:,:))); +ichg_epoch_flags(1,:,epoch_inds(flag_hg_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_hg',[0,1,1],ichg_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE HIGH-GAMMA ICAACT POWER AND UPDATE MARKS...'); +toc +clear ichg_epoch_flags; + +%% MARKS UPDATE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Merges the epochs back to continuous data. Flags marks gaps and combines time +% the new time marks into the manual marks. + +% Concat epochs back into continuous data +logging_log('INFO', 'Concatenating windowed data...'); +tic; +EEG=marks_epochs2continuous(EEG); +EEG=eeg_checkset(EEG,'eventconsistency'); +logging_log('INFO', 'TIME TO: CONCATENATE THE WINDOWED DATA INTO CONTINUOUS DATA...'); +toc + +% Mark flag gaps between time marks +tic; +EEG=pop_marks_flag_gap(EEG,{'manual','mark_gap','ic_sd2'}, ... + 2000,'mark_gap',[.8,.8,.8],'offsets',[0 0],'ref_point','both'); +logging_log('INFO', 'TIME TO: MARK FLAG GAPS THAT ARE < 2 SECONDS...'); +toc + +% Combine the removal marks into manual +tic; +EEG=pop_marks_merge_labels(EEG,'time_info',{'manual','ic_sd2','mark_gap'},'target_label','manual'); +logging_log('INFO', 'TIME TO: COMBINE MARKS STRUCTURE INTO MANUAL FLAGS...'); +toc + +% begin prep for dipole fit +logging_log('INFO','Checking for JVM...'); +javachk('jvm'); + +% TODO(mae) this isn't added for some reason +addpath([pwd '/derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/Fieldtrip-lite141209/src']) + +%% DIPFIT +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Does the dipole fitting procedures. This takes a substantial amount of +% time. +% TODO: Make sure this is all needed, seems to be repeating things +logging_log('INFO','FITTING DIPOLES...'); +tic; +EEG = pop_dipfit_settings( EEG, 'hdmfile','derivatives/BIDS-Lossless-EEG/code/misc/standard_vol.mat', ... + 'coordformat','MNI', ... + 'mrifile','derivatives/BIDS-Lossless-EEG/code/misc/standard_mri.mat', ... + 'chanfile','derivatives/BIDS-Lossless-EEG/code/misc/standard_1020.elc', ... + 'coord_transform',[0 0 0 0 0 0 1 1 1], ... + 'chansel',[1:EEG.nbchan] ); + +% OLD SINGLE THREADED SETUP +% EEG = pop_multifit(EEG, [1:EEG.nbchan] ,'threshold',100,'plotopt',{'normlen' 'on'}); + +% NEW MULTI-THREADED SETUP +EEG = pop_multifit_bucanl_parallel(EEG, [1:EEG.nbchan], 8,'threshold',100,'plotopt',{'normlen' 'on'}); + +EEG=eeg_checkset(EEG); +logging_log('INFO','TIME TO: FIT DIPOLES...'); +toc + +%% MARK ICs FOR REJECTION BASED ON RV +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Creates marks based on dipfit results. Dipfit marks are added +% for rejection while ICMARC are just for reference. +% IC RESIDUAL VARIANCE FLAG +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Residuals greater than 15% are flagged - can be done here as it only references +% dipoles and their values. +%logging_log('INFO','UPDATE MARKS STRUCTURE WITH IC_RV...'); +%tic; +% Dipole criteria changed from 0.15 to 0.2 to be less sensitive +%EEG.reject.gcompreject(find([EEG.dipfit.model.rv]>.15))=1; +%EEG.reject.gcompreject(find(isnan([EEG.dipfit.model.rv])))=1; +%EEG.marks=marks_add_label(EEG.marks,'comp_info', ... +% {'ic_rv',[1,0,0],[1,0,0],-1,EEG.reject.gcompreject'}); +%logging_log('INFO','TIME TO: UPDATE MARKS STRUCTURE...'); +%toc + +% COMBINE MARKS STRUCTURE INTO MANUAL FLAGS... +%tic; +%EEG=pop_marks_merge_labels(EEG,'comp_info',{'manual','ic_rv'},'target_label','manual'); +%logging_log('INFO','TIME TO: COMBINE MARKS STRUCTURE INTO MANUAL FLAGS...'); +%toc + +% Adjust montage +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% This will pretty much always be dataset specific - so be careful +EEG = warp_locs( EEG, 'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020.elc', ... + 'transform',[0,0,0,0,0,1.57,1,1,1], ... + 'manual','off'); +EEG.chaninfo.nosedir='+X'; +EEG=eeg_checkset(EEG); + +%% SAVE LOSSLESS DERIVATIVE *_eeg_ll.set FILE (same name as the input file from the +% BIDS root directory [but located in the derivatives/lossless directory]). +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +logging_log('INFO','Saving set file: derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_eeg_ll.set...'); +tic; +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_eeg_ll.set'); +logging_log('INFO','TIME TO: SAVE DIP DATA SET...'); +toc + +% Save diagnostic arrays +try OCTAVE_VERSION; + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_icaact_sd2_t.mat','icaact_sd2_t'); +catch + save('derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_icaact_sd2_t.mat','icaact_sd2_t'); +end + +logging_log('INFO', 'Scheduler: sbatch'); +print_chan_sample(EEG); + + +if ~find(strcmpi({EEG.marks.chan_info.label},'ch_s_sd')); + EEG.marks=marks_add_label(EEG.marks,'chan_info',{'ch_s_sd',[1,0,0],[]}); +end; + +EEG = eeg_checkset(EEG); + +% CHECK FOR IC CLASSIFICATION MARKS +%% Use the ICLabel plugin +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% ICLabel is a database style plugin that classifies ICA components into the +% following seven categories + +% (Brain)(Muscle)(Eye)(Heart)(Line Noise)(Channel Noise)(Other) +% ( 1 )( 2 )( 3 )( 4 )( 5 )( 6 )( 7 ) + +% Colour structure and groupings are as follows: +% ---------- +% Brain -> [0, 0.4, 0] +% ---------- +% Muscle -> [0.4, 0, 0] +% Eye -> [0.8, 0, 0] +% Heart -> [0.8, 0.4, 0] +% ---------- +% Line Noise -> [0.2, 0.4, 0.8] +% Channel noise -> [0.4, 0, 0.8] +% ---------- +% Other -> [0.8, 0.8, 0] +% ---------- + +% Create lookup table of the above information. +pairs = {'brain',[0, 0.4, 0]; +'muscle',[0.4,0,0]; +'eye',[0.8,0,0]; +'heart',[0.8,0.4,0]; +'line_noise',[0.2,0.4,0.8]; +'chan_noise',[0.4,0,0.8]; +'other',[.7 .4 0]}; +lookupTable = cell2struct(pairs, {'name', 'colour'}, 2); + +EEG = pop_marks_select_data(EEG,'time marks',[],'labels',{'manual'},'remove','on'); + +% Run ICLabel +%EEG = iclabel(EEG); + +% extract features +disp 'ICLabel: extracting features...' +features = ICL_feature_extractor(EEG, 40); + +% run ICL +disp 'ICLabel: calculating labels...' +labels = run_ICL('default',features{:}); + +% save into EEG +disp 'ICLabel: saving results...' +EEG.etc.ic_classification.ICLabel.classes = ... + {'Brain', 'Muscle', 'Eye', 'Heart', 'Line', 'Channel', 'Other'}; +EEG.etc.ic_classification.ICLabel.classifications = labels; + +tmp=EEG.etc.ic_classification; + +% Reload dataset: +EEG = pop_loadset('filepath','derivatives/BIDS-Lossless-EEG','filename','./sub-001/eeg/sub-001_task-faceFO_eeg_ll.set'); +EEG = eeg_checkset(EEG); + +EEG.etc.ic_classification=tmp; + +% Extract labels from the structure. +rawLabels = zeros([1,length(EEG.etc.ic_classification.ICLabel.classifications)]); +for index = 1:length(EEG.etc.ic_classification.ICLabel.classifications) + [p,i] = max(EEG.etc.ic_classification.ICLabel.classifications(index,1:end-1)); + if p<.3; + i=7; + end + rawLabels(index) = i; +end + +%ICLabel Exporting to marks +%%%%%%%%%%%%%%%%%%%%%%%%%%%% +logging_log('INFO','UPDATE MARKS STRUCTURE WITH ICLABEL BRAIN COMPS...'); +for compStage = 1:7 + ICLabel_pass= zeros([1,length(rawLabels)]); + ICLabel_pass(find(rawLabels==compStage)) = 1; + ICLabel_pass = ICLabel_pass'; + EEG.marks=marks_add_label(EEG.marks,'comp_info', ... + {lookupTable(compStage).name,lookupTable(compStage).colour, ... + lookupTable(compStage).colour,1,ICLabel_pass}); +end +% make any brain components always on top: +EEG.marks.comp_info(1,find(strcmp({EEG.marks.comp_info.label},'brain'))).order=2; + + +%RESET/INITIATE CHAN MARKS... +manual_ind=find(strcmp('manual',{EEG.marks.chan_info.label})); + + +%RESET/INITIATE IC MARKS... +EEG.marks=marks_add_label(EEG.marks,'comp_info',{'ambig',[.3,.3,1],[.3,.3,1],3,zeros(min(size(EEG.icaweights)),1)}); +manual_ind=find(strcmp('manual',{EEG.marks.comp_info.label})); +ch_s_sd_ind=find(strcmp('ch_s_sd',{EEG.marks.chan_info.label})); +ch_sd_ind=find(strcmp('ch_sd',{EEG.marks.chan_info.label})); +low_r_ind=find(strcmp('low_r',{EEG.marks.chan_info.label})); +bridge_ind=find(strcmp('bridge',{EEG.marks.chan_info.label})); +rank_ind=find(strcmp('rank',{EEG.marks.chan_info.label})); +icrt_ind=find(strcmp('ic_rt',{EEG.marks.comp_info.label})); +brain_ind=find(strcmp('brain',{EEG.marks.comp_info.label})); +muscle_ind=find(strcmp('muscle',{EEG.marks.comp_info.label})); +eye_ind=find(strcmp('eye',{EEG.marks.comp_info.label})); +heart_ind=find(strcmp('heart',{EEG.marks.comp_info.label})); +line_noise_ind=find(strcmp('line_noise',{EEG.marks.comp_info.label})); +chan_noise_ind=find(strcmp('chan_noise',{EEG.marks.comp_info.label})); +other_ind=find(strcmp('other',{EEG.marks.comp_info.label})); +ambig_ind=find(strcmp('ambig',{EEG.marks.comp_info.label})); + +EEG.marks.comp_info(manual_ind).order=1; +if ~isempty(ch_s_sd_ind); % skip study-wide criteria if not used + EEG.marks.comp_info(ch_s_sd_ind).order=2; +end; +EEG.marks.comp_info(ch_sd_ind).order=2; +EEG.marks.comp_info(low_r_ind).order=2; +EEG.marks.comp_info(bridge_ind).order=2; +EEG.marks.comp_info(rank_ind).order=2; +EEG.marks.comp_info(icrt_ind).order=1; + +EEG.marks.comp_info(brain_ind).order=2; +EEG.marks.comp_info(muscle_ind).order=2; +EEG.marks.comp_info(eye_ind).order=2; +EEG.marks.comp_info(heart_ind).order=2; +EEG.marks.comp_info(line_noise_ind).order=2; +EEG.marks.comp_info(chan_noise_ind).order=2; +EEG.marks.comp_info(other_ind).order=2; +EEG.marks.comp_info(ambig_ind).order=3; + +bioa_inds = marks_label2index(EEG.marks.comp_info,{'muscle','eye','heart','chan_noise'},'indexes'); +EEG.marks.comp_info(manual_ind).flags=zeros(min(size(EEG.icaweights)),1); +EEG.marks.comp_info(manual_ind).flags(bioa_inds)=1; + +% BIDS Export calls +% Dot for path is on purpose. Don't worry - it's safe. +fName = './derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_eeg.edf'; +pop_writeeeg(EEG, fName, 'TYPE','EDF'); +dlmwrite(strrep(fName,'_eeg.edf','_icaweights.tsv'),EEG.icaweights,'\t'); +dlmwrite(strrep(fName,'_eeg.edf','_icasphere.tsv'),EEG.icasphere,'\t'); +s = {}; +s.algorithm = 'amica15'; +s.icachansind = EEG.icachansind; +s.intendedFor = fName; +savejson('',s,strrep(fName,'_eeg.edf','_icaweights.json')); +export_anno(EEG,fName,''); + +% ICLabel quick export +iclFname = strrep(fName,'_eeg.edf','_iclabel.mat'); +save(iclFname, 'tmp'); % see above ICLabel section for tmp definition diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/s05_concat_data_sub_002_task_faceFO_eeg.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/s05_concat_data_sub_002_task_faceFO_eeg.m new file mode 100644 index 000000000..27ea4e178 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/s05_concat_data_sub_002_task_faceFO_eeg.m @@ -0,0 +1,678 @@ +warning off; + +pkg load signal; +pkg load parallel; +pkg load io; + +file_path = fileparts(mfilename('fullpath')); + +addpath([file_path '/../../code/dependencies/eeglab_lossless'], ... + [file_path '/../../code/dependencies/matlog'], ... + [file_path '/../../code/tools']); +cd '/scratch/tk11br/Face13/'; + +% eeglab auto loads the path correctly instead of shotgun blasting with +% addpath(genpath(<>)) as we were doing prior +if ~exist('EEG', 'var') + eeglab; +end +% Use matlog disp function override +logging_override_disp(true); +% Use matlog INFO level debugging +logging_set_log_level('INFO') + +% Don't run with traditional but enable some of the options +save_default_options ('-mat-binary'); +confirm_recursive_rmdir(false); + +% This makes | and & || and && in earlier version of Octave where +% | and & dont short curcuit by default +if exist('do_braindead_shortcircuit_evaluation', 'builtin') + do_braindead_shortcircuit_evaluation(true); +end + +% Options +pop_editoptions('option_savetwofiles', 1,'option_single',0); + +%% SCRIPT DESCRIPTION +% This script combineds the results of the amicas A,B and C. It takes this information and using ICAtest it determines which components were +% and were not replicated across and flags them, along with time sections with high standard deviation. Beta,theta and Alpha power is calculated +% arcoss time and is marked but not primed for removal. +% +%% From Config key_strswap Description +%----------------------------------------------------------------------------------------------------------------------------- +% in_path = derivatives/BIDS-Lossless-EEG Relative path to input data files assuming cd = work_path +% recur_sec = 1 Recurrence (sec) for the current artifact detection epoching (e.g. 1) +% limit_sec = [0 1] Limits (sec) for the current artifact detection epoching (e.g. [0 1]) +% sd_t_meth = q Method used for flagging epochs (e.g. 'q' (quantiles), or 'na' (default)) +% sd_t_vals = [.3 .7] Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7]) +% sd_t_o = 6 z threshold for flagging epochs during standard deviation criteria (e.g. 6) +% sd_t_f_meth = fixed Fixed method used for flagging epochs (should be 'fixed') +% sd_t_f_vals = Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% sd_t_f_o = .2 z threshold for flagging epochs during fixed standard deviation criteria (e.g. .2) +% min_gap_ms = 2000 Minimum time (ms) to allow between periods marked for rejection +% icr_crit = 0.05 False discovery rate of component replicatablilty, measure of sensitivity % +% dip_vol = derivatives/BIDS-Lossless-EEG/code/misc/standard_vol.mat file path/name of the standard volume file used in dipole fitting (e.g. derivatives/lossless/code/misc/standard_vol.mat). +% dip_mri = derivatives/BIDS-Lossless-EEG/code/misc/standard_mri.mat file path/name of the standard mri file used in dipole fitting (e.g. derivatives/lossless/code/misc/standard_mri.mat). +% dip_elc = derivatives/BIDS-Lossless-EEG/code/misc/standard_1020.elc file path/name of the standard electrode file used in dipole fitting (derivatives/lossless/code/misc/standard_1020.elc). +% dip_transmat = [0 0 0 0 0 0 1 1 1] Transformation matrix to apply during dipole fit (e.g. [0 0 0 0 0 0 1 1 1]) +% cor_transmat = [0,0,0,0,0,1.57,1,1,1] The transformation matrix to apply between dipole fitting to the standard head and performing ICLabel to the EEGLAB default orientation (e.g.[0,0,0,0,0,1.57,1,1,1]) +% cor_nosedir = +X Nose direction of montage to apply between dipole fitting to the standard head and performing ICLabel to the EEGLAB default orientation (e.g. +Y) +% out_path = derivatives/BIDS-Lossless-EEG Relative path to output data files assuming cd = work_path + +%% LOAD COMPART DATASET +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Loads the data file and the first amica model. + +logging_log('INFO', 'Loading set file: ./sub-002/eeg/sub-002_task-faceFO_compart_data.set...'); +tic; +EEG = pop_loadset('filepath','derivatives/BIDS-Lossless-EEG','filename','./sub-002/eeg/sub-002_task-faceFO_compart_data.set'); +EEG = eeg_checkset( EEG ); +logging_log('INFO', 'TIME TO: LOAD DATASET...'); +toc + +% Set up the ICA channels +EEG.icachansind = marks_label2index(EEG.marks.chan_info,{'manual','rank'},'indices','invert','on'); + +% Load Amica A +logging_log('INFO', 'Loading Amica ABC...'); +tic; +EEG.amica(2).models=loadmodout15('derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_amicaout_A'); +EEG.icaweights=EEG.amica(2).models.W; +EEG.icasphere=EEG.amica(2).models.S(1:EEG.amica(2).models.num_pcs,:); + +%% PASS EEG DATA THROUGH THE AMICA MATRIX +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Loads the B and C amica models. Amica A is used to fill in the ICA information. + +% Instead of EEG=eeg_checkset(EEG); +tmpdata = EEG.data(EEG.icachansind,:); +tmpindices = find(~sum(isnan(tmpdata))); +EEG.icaact = zeros(size(EEG.icaweights,1), size(tmpdata,2)); EEG.icaact(:) = NaN; +EEG.icaact(:,tmpindices) = (EEG.icaweights*EEG.icasphere)*tmpdata(:,tmpindices); +clear tmpdata; + +% Create winv for topo plots manually +EEG.icawinv = []; +EEG.icawinv = pinv(EEG.icaweights * EEG.icasphere); + +% Load Amica B +EEG.amica(3).models=loadmodout15('derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_amicaout_B'); +% Load Amica C +EEG.amica(4).models=loadmodout15('derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_amicaout_C'); +logging_log('INFO', 'TIME TO: LOAD ICA ABC INFO...'); +toc + +%% UPDATE REJECTION STRUCTURE... +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Creating flag marks to based on the log liklyhodd models found for each of the ICA models + +% Flags for amicas A B and C +logging_log('INFO', 'Updating latflaginfo structure...'); +tic; + +% A +logl_A_flags=zeros(size(EEG.data(1,:))); +logl_A_flag_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indices','invert','on'); + +% Check to see if the overwrite will be successful. If the overwirte is a different size then some of the old data will be retained. +if length(EEG.amica(2).models.Lt) ~= length(logl_A_flag_inds) + EEG.amica(2).models.Lt = EEG.amica(2).models.Lt(1:length(logl_A_flag_inds)); + warning('YOU FORGOT TO DELETE YOUR OLD AMICA FILES AND THE NEW CHANNEL SIZES DO NOT MATCH UP'); +end + +logl_A_flags(logl_A_flag_inds)=(EEG.amica(2).models.Lt-min(EEG.amica(2).models.Lt))/(max(EEG.amica(2).models.Lt)-min(EEG.amica(2).models.Lt)); +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'logl_A',[1,0.45,0],logl_A_flags}); +clear logl_A_flags; +% B +logl_B_flags=zeros(size(EEG.data(1,:))); +logl_B_flag_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indices','invert','on'); +logl_B_flags(logl_B_flag_inds)=(EEG.amica(3).models.Lt-min(EEG.amica(3).models.Lt))/(max(EEG.amica(3).models.Lt)-min(EEG.amica(3).models.Lt)); +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'logl_B',[1,0.45,0],logl_B_flags}); +clear logl_B_flags; +% C +logl_C_flags=zeros(size(EEG.data(1,:))); +logl_C_flag_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indices','invert','on'); +logl_C_flags(logl_C_flag_inds)=(EEG.amica(4).models.Lt-min(EEG.amica(4).models.Lt))/(max(EEG.amica(4).models.Lt)-min(EEG.amica(4).models.Lt)); +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'logl_C',[1,0.45,0],logl_C_flags}); +clear logl_C_flags; + +logging_log('INFO', 'TIME TO: UPDATE REJECTION STRUCTURE...'); +toc + +%% IDENTIFY RELIABLE COMPONENTS USING ISCTEST +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Gives the A variable of each of the amicas to the function icatest. Returned are the indecies of the +% Components that were not replicated across all 3 of the models. + +tic; +% Clear a in case of local models are saved already +a = []; +for i=2:length(EEG.amica); + a(:,:,i-1)=EEG.amica(i).models(1).A; +end + +% Calculating the replicated component flag +% Replicate criteria changed from 0.05,0.05 to 0.1,0.1 to be less sensitive +ca=isctest(a,0.05,0.05,'mixing'); +if isempty(ca) + mess = ['ISCTest failed to return reasonable values. ISCTest is ' ... + 'meant to find duplicate channels in amica_{A,B,C} if this ' ... + 'failed consider investigating the datafile']; + logging_log('ERROR', mess); + error(mess); +end +icr_inds=setdiff(1:min(size(EEG.amica(2).models(1).A)),ca(:,1)); + +% Creating the replicated component flag +logging_log('INFO', 'Updating chflaginfo structure...'); +icr_comp_flags=zeros(min(size(EEG.amica(2).models(1).A)),1); +icr_comp_flags(icr_inds)=1; +EEG.marks=marks_add_label(EEG.marks,'comp_info', ... + {'ic_rt',[0,0,1],[0,0,1],-1,icr_comp_flags}); +logging_log('INFO', 'TIME TO: EDIT FIND REPLICATED COMPONENTS AND UPDATE COMPINFO STRUCT...'); +toc + +% Window the continuous data +logging_log('INFO', 'Windowing the continous data...'); +tic; +EEG=marks_continuous2epochs(EEG,'recurrence',1,'limits',[0 1]); +logging_log('INFO', 'TIME TO: WINDOW THE CONTINUOUS DATA...'); +toc + +%% CALCULATE ICAACT SD +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Similarly to the IC_SD flag created in compart. Marks sections of time where the components +% have an abnormally high standard deviation. + +% Calculating the replicated component flag +logging_log('INFO', 'Calculating the IC sd array for window criteria...'); +tic; +epoch_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indexes','invert','on'); +[EEG,icaact_sd2_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'varmeasure','absmean', ... + 'detrend','off', ... + 'plot_figs','off'); + +% Creating the ic_sd component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_sd_t_inds]=marks_array2flags(icaact_sd2_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); + +icsd_epoch_flags=zeros(size(EEG.data(1,:,:))); +icsd_epoch_flags(1,:,epoch_inds(flag_sd_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_sd2',[.4,.6,.6],icsd_epoch_flags}); +logging_log('INFO', 'TIME TO: EDIT FIND IC_SD AND UPDATE TIMEINFO REJECTION STRUCT...'); +toc +clear icsd_epoch_flags; + +%% CALCULATE DELTA/THETA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high Theta/Alpha Power + +% Calculating the Delta/Theta component flag +logging_log('INFO', 'Calculating the delta/theta IC power array for window criteria...'); +tic; +[EEG,icaact_dt_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[0 7], ... + 'plot_figs','off'); + +% Creating the Delta/Theta component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_dt_t_inds]=marks_array2flags(icaact_dt_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +icdt_epoch_flags=zeros(size(EEG.data(1,:,:))); +icdt_epoch_flags(1,:,epoch_inds(flag_dt_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_dt',[1,1,0],icdt_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE DELTA/THETA ICAACT POWER AND UPDATE MARKS...'); +toc +clear icdt_epoch_flags; + +%% CALCULATE ALPHA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high Alpha Power + +% Calculating the Alpha component flag +logging_log('INFO', 'Calculating the alpha IC power array for window criteria...'); +tic; +[EEG,icaact_a_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[8 13], ... + 'plot_figs','off'); + +% Creating the Alpha component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_a_t_inds]=marks_array2flags(icaact_a_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +ica_epoch_flags=zeros(size(EEG.data(1,:,:))); +ica_epoch_flags(1,:,epoch_inds(flag_a_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_a',[1,1,0],ica_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE ALPHA ICAACT POWER AND UPDATE MARKS...'); +toc +clear ica_epoch_flags; + +%% CALCULATE BETA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high Beta Power. +% Note that at the moment this mark is used for removals. + +% Calculating the Beta component flag +logging_log('INFO', 'Calculating the beta IC power array for window criteria...'); +tic; +[EEG,icaact_b_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[14 30], ... + 'plot_figs','off'); +logging_log('INFO', 'TIME TO: CALCULATE BETA ICAACT POWER...'); + +% Creating the Beta component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_b_t_inds]=marks_array2flags(icaact_b_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +logging_log('INFO', 'TIME TO: CREATE WINDOW CRITERIA VECTOR...'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +icb_epoch_flags=zeros(size(EEG.data(1,:,:))); +icb_epoch_flags(1,:,epoch_inds(flag_b_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_b',[0,1,1],icb_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE BETA ICAACT POWER AND UPDATE MARKS...'); +toc +clear icb_epoch_flags; + +%% CALCULATE LOW GAMMA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high Low-Gamma Power. +% Note that at the moment this mark is used for removals. + +% Calculating the Beta component flag +logging_log('INFO', 'Calculating the low-gamma IC power array for window criteria...'); +tic; +[EEG,icaact_lg_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[31 60], ... + 'plot_figs','off'); +logging_log('INFO', 'TIME TO: CALCULATE LOW-GAMMA ICAACT POWER...'); + +% Creating the LOW-GAMMA component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_lg_t_inds]=marks_array2flags(icaact_lg_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +logging_log('INFO', 'TIME TO: CREATE WINDOW CRITERIA VECTOR...'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +iclg_epoch_flags=zeros(size(EEG.data(1,:,:))); +iclg_epoch_flags(1,:,epoch_inds(flag_lg_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_lg',[0,1,1],iclg_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE LOW-GAMMA ICAACT POWER AND UPDATE MARKS...'); +toc +clear iclg_epoch_flags; + + +%% CALCULATE HIGH GAMMA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high High-Gamma Power. +% Note that at the moment this mark is used for removals. + +% Calculating the Beta component flag +logging_log('INFO', 'Calculating the high-gamma IC power array for window criteria...'); +tic; +[EEG,icaact_hg_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[61 100], ... + 'plot_figs','off'); +logging_log('INFO', 'TIME TO: CALCULATE HIGH-GAMMA ICAACT POWER...'); + +% Creating the HIGH-GAMMA component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_hg_t_inds]=marks_array2flags(icaact_hg_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +logging_log('INFO', 'TIME TO: CREATE WINDOW CRITERIA VECTOR...'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +ichg_epoch_flags=zeros(size(EEG.data(1,:,:))); +ichg_epoch_flags(1,:,epoch_inds(flag_hg_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_hg',[0,1,1],ichg_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE HIGH-GAMMA ICAACT POWER AND UPDATE MARKS...'); +toc +clear ichg_epoch_flags; + +%% MARKS UPDATE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Merges the epochs back to continuous data. Flags marks gaps and combines time +% the new time marks into the manual marks. + +% Concat epochs back into continuous data +logging_log('INFO', 'Concatenating windowed data...'); +tic; +EEG=marks_epochs2continuous(EEG); +EEG=eeg_checkset(EEG,'eventconsistency'); +logging_log('INFO', 'TIME TO: CONCATENATE THE WINDOWED DATA INTO CONTINUOUS DATA...'); +toc + +% Mark flag gaps between time marks +tic; +EEG=pop_marks_flag_gap(EEG,{'manual','mark_gap','ic_sd2'}, ... + 2000,'mark_gap',[.8,.8,.8],'offsets',[0 0],'ref_point','both'); +logging_log('INFO', 'TIME TO: MARK FLAG GAPS THAT ARE < 2 SECONDS...'); +toc + +% Combine the removal marks into manual +tic; +EEG=pop_marks_merge_labels(EEG,'time_info',{'manual','ic_sd2','mark_gap'},'target_label','manual'); +logging_log('INFO', 'TIME TO: COMBINE MARKS STRUCTURE INTO MANUAL FLAGS...'); +toc + +% begin prep for dipole fit +logging_log('INFO','Checking for JVM...'); +javachk('jvm'); + +% TODO(mae) this isn't added for some reason +addpath([pwd '/derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/Fieldtrip-lite141209/src']) + +%% DIPFIT +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Does the dipole fitting procedures. This takes a substantial amount of +% time. +% TODO: Make sure this is all needed, seems to be repeating things +logging_log('INFO','FITTING DIPOLES...'); +tic; +EEG = pop_dipfit_settings( EEG, 'hdmfile','derivatives/BIDS-Lossless-EEG/code/misc/standard_vol.mat', ... + 'coordformat','MNI', ... + 'mrifile','derivatives/BIDS-Lossless-EEG/code/misc/standard_mri.mat', ... + 'chanfile','derivatives/BIDS-Lossless-EEG/code/misc/standard_1020.elc', ... + 'coord_transform',[0 0 0 0 0 0 1 1 1], ... + 'chansel',[1:EEG.nbchan] ); + +% OLD SINGLE THREADED SETUP +% EEG = pop_multifit(EEG, [1:EEG.nbchan] ,'threshold',100,'plotopt',{'normlen' 'on'}); + +% NEW MULTI-THREADED SETUP +EEG = pop_multifit_bucanl_parallel(EEG, [1:EEG.nbchan], 8,'threshold',100,'plotopt',{'normlen' 'on'}); + +EEG=eeg_checkset(EEG); +logging_log('INFO','TIME TO: FIT DIPOLES...'); +toc + +%% MARK ICs FOR REJECTION BASED ON RV +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Creates marks based on dipfit results. Dipfit marks are added +% for rejection while ICMARC are just for reference. +% IC RESIDUAL VARIANCE FLAG +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Residuals greater than 15% are flagged - can be done here as it only references +% dipoles and their values. +%logging_log('INFO','UPDATE MARKS STRUCTURE WITH IC_RV...'); +%tic; +% Dipole criteria changed from 0.15 to 0.2 to be less sensitive +%EEG.reject.gcompreject(find([EEG.dipfit.model.rv]>.15))=1; +%EEG.reject.gcompreject(find(isnan([EEG.dipfit.model.rv])))=1; +%EEG.marks=marks_add_label(EEG.marks,'comp_info', ... +% {'ic_rv',[1,0,0],[1,0,0],-1,EEG.reject.gcompreject'}); +%logging_log('INFO','TIME TO: UPDATE MARKS STRUCTURE...'); +%toc + +% COMBINE MARKS STRUCTURE INTO MANUAL FLAGS... +%tic; +%EEG=pop_marks_merge_labels(EEG,'comp_info',{'manual','ic_rv'},'target_label','manual'); +%logging_log('INFO','TIME TO: COMBINE MARKS STRUCTURE INTO MANUAL FLAGS...'); +%toc + +% Adjust montage +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% This will pretty much always be dataset specific - so be careful +EEG = warp_locs( EEG, 'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020.elc', ... + 'transform',[0,0,0,0,0,1.57,1,1,1], ... + 'manual','off'); +EEG.chaninfo.nosedir='+X'; +EEG=eeg_checkset(EEG); + +%% SAVE LOSSLESS DERIVATIVE *_eeg_ll.set FILE (same name as the input file from the +% BIDS root directory [but located in the derivatives/lossless directory]). +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +logging_log('INFO','Saving set file: derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_eeg_ll.set...'); +tic; +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_eeg_ll.set'); +logging_log('INFO','TIME TO: SAVE DIP DATA SET...'); +toc + +% Save diagnostic arrays +try OCTAVE_VERSION; + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_icaact_sd2_t.mat','icaact_sd2_t'); +catch + save('derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_icaact_sd2_t.mat','icaact_sd2_t'); +end + +logging_log('INFO', 'Scheduler: sbatch'); +print_chan_sample(EEG); + + +if ~find(strcmpi({EEG.marks.chan_info.label},'ch_s_sd')); + EEG.marks=marks_add_label(EEG.marks,'chan_info',{'ch_s_sd',[1,0,0],[]}); +end; + +EEG = eeg_checkset(EEG); + +% CHECK FOR IC CLASSIFICATION MARKS +%% Use the ICLabel plugin +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% ICLabel is a database style plugin that classifies ICA components into the +% following seven categories + +% (Brain)(Muscle)(Eye)(Heart)(Line Noise)(Channel Noise)(Other) +% ( 1 )( 2 )( 3 )( 4 )( 5 )( 6 )( 7 ) + +% Colour structure and groupings are as follows: +% ---------- +% Brain -> [0, 0.4, 0] +% ---------- +% Muscle -> [0.4, 0, 0] +% Eye -> [0.8, 0, 0] +% Heart -> [0.8, 0.4, 0] +% ---------- +% Line Noise -> [0.2, 0.4, 0.8] +% Channel noise -> [0.4, 0, 0.8] +% ---------- +% Other -> [0.8, 0.8, 0] +% ---------- + +% Create lookup table of the above information. +pairs = {'brain',[0, 0.4, 0]; +'muscle',[0.4,0,0]; +'eye',[0.8,0,0]; +'heart',[0.8,0.4,0]; +'line_noise',[0.2,0.4,0.8]; +'chan_noise',[0.4,0,0.8]; +'other',[.7 .4 0]}; +lookupTable = cell2struct(pairs, {'name', 'colour'}, 2); + +EEG = pop_marks_select_data(EEG,'time marks',[],'labels',{'manual'},'remove','on'); + +% Run ICLabel +%EEG = iclabel(EEG); + +% extract features +disp 'ICLabel: extracting features...' +features = ICL_feature_extractor(EEG, 40); + +% run ICL +disp 'ICLabel: calculating labels...' +labels = run_ICL('default',features{:}); + +% save into EEG +disp 'ICLabel: saving results...' +EEG.etc.ic_classification.ICLabel.classes = ... + {'Brain', 'Muscle', 'Eye', 'Heart', 'Line', 'Channel', 'Other'}; +EEG.etc.ic_classification.ICLabel.classifications = labels; + +tmp=EEG.etc.ic_classification; + +% Reload dataset: +EEG = pop_loadset('filepath','derivatives/BIDS-Lossless-EEG','filename','./sub-002/eeg/sub-002_task-faceFO_eeg_ll.set'); +EEG = eeg_checkset(EEG); + +EEG.etc.ic_classification=tmp; + +% Extract labels from the structure. +rawLabels = zeros([1,length(EEG.etc.ic_classification.ICLabel.classifications)]); +for index = 1:length(EEG.etc.ic_classification.ICLabel.classifications) + [p,i] = max(EEG.etc.ic_classification.ICLabel.classifications(index,1:end-1)); + if p<.3; + i=7; + end + rawLabels(index) = i; +end + +%ICLabel Exporting to marks +%%%%%%%%%%%%%%%%%%%%%%%%%%%% +logging_log('INFO','UPDATE MARKS STRUCTURE WITH ICLABEL BRAIN COMPS...'); +for compStage = 1:7 + ICLabel_pass= zeros([1,length(rawLabels)]); + ICLabel_pass(find(rawLabels==compStage)) = 1; + ICLabel_pass = ICLabel_pass'; + EEG.marks=marks_add_label(EEG.marks,'comp_info', ... + {lookupTable(compStage).name,lookupTable(compStage).colour, ... + lookupTable(compStage).colour,1,ICLabel_pass}); +end +% make any brain components always on top: +EEG.marks.comp_info(1,find(strcmp({EEG.marks.comp_info.label},'brain'))).order=2; + + +%RESET/INITIATE CHAN MARKS... +manual_ind=find(strcmp('manual',{EEG.marks.chan_info.label})); + + +%RESET/INITIATE IC MARKS... +EEG.marks=marks_add_label(EEG.marks,'comp_info',{'ambig',[.3,.3,1],[.3,.3,1],3,zeros(min(size(EEG.icaweights)),1)}); +manual_ind=find(strcmp('manual',{EEG.marks.comp_info.label})); +ch_s_sd_ind=find(strcmp('ch_s_sd',{EEG.marks.chan_info.label})); +ch_sd_ind=find(strcmp('ch_sd',{EEG.marks.chan_info.label})); +low_r_ind=find(strcmp('low_r',{EEG.marks.chan_info.label})); +bridge_ind=find(strcmp('bridge',{EEG.marks.chan_info.label})); +rank_ind=find(strcmp('rank',{EEG.marks.chan_info.label})); +icrt_ind=find(strcmp('ic_rt',{EEG.marks.comp_info.label})); +brain_ind=find(strcmp('brain',{EEG.marks.comp_info.label})); +muscle_ind=find(strcmp('muscle',{EEG.marks.comp_info.label})); +eye_ind=find(strcmp('eye',{EEG.marks.comp_info.label})); +heart_ind=find(strcmp('heart',{EEG.marks.comp_info.label})); +line_noise_ind=find(strcmp('line_noise',{EEG.marks.comp_info.label})); +chan_noise_ind=find(strcmp('chan_noise',{EEG.marks.comp_info.label})); +other_ind=find(strcmp('other',{EEG.marks.comp_info.label})); +ambig_ind=find(strcmp('ambig',{EEG.marks.comp_info.label})); + +EEG.marks.comp_info(manual_ind).order=1; +if ~isempty(ch_s_sd_ind); % skip study-wide criteria if not used + EEG.marks.comp_info(ch_s_sd_ind).order=2; +end; +EEG.marks.comp_info(ch_sd_ind).order=2; +EEG.marks.comp_info(low_r_ind).order=2; +EEG.marks.comp_info(bridge_ind).order=2; +EEG.marks.comp_info(rank_ind).order=2; +EEG.marks.comp_info(icrt_ind).order=1; + +EEG.marks.comp_info(brain_ind).order=2; +EEG.marks.comp_info(muscle_ind).order=2; +EEG.marks.comp_info(eye_ind).order=2; +EEG.marks.comp_info(heart_ind).order=2; +EEG.marks.comp_info(line_noise_ind).order=2; +EEG.marks.comp_info(chan_noise_ind).order=2; +EEG.marks.comp_info(other_ind).order=2; +EEG.marks.comp_info(ambig_ind).order=3; + +bioa_inds = marks_label2index(EEG.marks.comp_info,{'muscle','eye','heart','chan_noise'},'indexes'); +EEG.marks.comp_info(manual_ind).flags=zeros(min(size(EEG.icaweights)),1); +EEG.marks.comp_info(manual_ind).flags(bioa_inds)=1; + +% BIDS Export calls +% Dot for path is on purpose. Don't worry - it's safe. +fName = './derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_eeg.edf'; +pop_writeeeg(EEG, fName, 'TYPE','EDF'); +dlmwrite(strrep(fName,'_eeg.edf','_icaweights.tsv'),EEG.icaweights,'\t'); +dlmwrite(strrep(fName,'_eeg.edf','_icasphere.tsv'),EEG.icasphere,'\t'); +s = {}; +s.algorithm = 'amica15'; +s.icachansind = EEG.icachansind; +s.intendedFor = fName; +savejson('',s,strrep(fName,'_eeg.edf','_icaweights.json')); +export_anno(EEG,fName,''); + +% ICLabel quick export +iclFname = strrep(fName,'_eeg.edf','_iclabel.mat'); +save(iclFname, 'tmp'); % see above ICLabel section for tmp definition diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/s05_concat_data_sub_003_task_faceFO_eeg.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/s05_concat_data_sub_003_task_faceFO_eeg.m new file mode 100644 index 000000000..a70235f72 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/s05_concat_data_sub_003_task_faceFO_eeg.m @@ -0,0 +1,678 @@ +warning off; + +pkg load signal; +pkg load parallel; +pkg load io; + +file_path = fileparts(mfilename('fullpath')); + +addpath([file_path '/../../code/dependencies/eeglab_lossless'], ... + [file_path '/../../code/dependencies/matlog'], ... + [file_path '/../../code/tools']); +cd '/scratch/tk11br/Face13/'; + +% eeglab auto loads the path correctly instead of shotgun blasting with +% addpath(genpath(<>)) as we were doing prior +if ~exist('EEG', 'var') + eeglab; +end +% Use matlog disp function override +logging_override_disp(true); +% Use matlog INFO level debugging +logging_set_log_level('INFO') + +% Don't run with traditional but enable some of the options +save_default_options ('-mat-binary'); +confirm_recursive_rmdir(false); + +% This makes | and & || and && in earlier version of Octave where +% | and & dont short curcuit by default +if exist('do_braindead_shortcircuit_evaluation', 'builtin') + do_braindead_shortcircuit_evaluation(true); +end + +% Options +pop_editoptions('option_savetwofiles', 1,'option_single',0); + +%% SCRIPT DESCRIPTION +% This script combineds the results of the amicas A,B and C. It takes this information and using ICAtest it determines which components were +% and were not replicated across and flags them, along with time sections with high standard deviation. Beta,theta and Alpha power is calculated +% arcoss time and is marked but not primed for removal. +% +%% From Config key_strswap Description +%----------------------------------------------------------------------------------------------------------------------------- +% in_path = derivatives/BIDS-Lossless-EEG Relative path to input data files assuming cd = work_path +% recur_sec = 1 Recurrence (sec) for the current artifact detection epoching (e.g. 1) +% limit_sec = [0 1] Limits (sec) for the current artifact detection epoching (e.g. [0 1]) +% sd_t_meth = q Method used for flagging epochs (e.g. 'q' (quantiles), or 'na' (default)) +% sd_t_vals = [.3 .7] Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7]) +% sd_t_o = 6 z threshold for flagging epochs during standard deviation criteria (e.g. 6) +% sd_t_f_meth = fixed Fixed method used for flagging epochs (should be 'fixed') +% sd_t_f_vals = Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% sd_t_f_o = .2 z threshold for flagging epochs during fixed standard deviation criteria (e.g. .2) +% min_gap_ms = 2000 Minimum time (ms) to allow between periods marked for rejection +% icr_crit = 0.05 False discovery rate of component replicatablilty, measure of sensitivity % +% dip_vol = derivatives/BIDS-Lossless-EEG/code/misc/standard_vol.mat file path/name of the standard volume file used in dipole fitting (e.g. derivatives/lossless/code/misc/standard_vol.mat). +% dip_mri = derivatives/BIDS-Lossless-EEG/code/misc/standard_mri.mat file path/name of the standard mri file used in dipole fitting (e.g. derivatives/lossless/code/misc/standard_mri.mat). +% dip_elc = derivatives/BIDS-Lossless-EEG/code/misc/standard_1020.elc file path/name of the standard electrode file used in dipole fitting (derivatives/lossless/code/misc/standard_1020.elc). +% dip_transmat = [0 0 0 0 0 0 1 1 1] Transformation matrix to apply during dipole fit (e.g. [0 0 0 0 0 0 1 1 1]) +% cor_transmat = [0,0,0,0,0,1.57,1,1,1] The transformation matrix to apply between dipole fitting to the standard head and performing ICLabel to the EEGLAB default orientation (e.g.[0,0,0,0,0,1.57,1,1,1]) +% cor_nosedir = +X Nose direction of montage to apply between dipole fitting to the standard head and performing ICLabel to the EEGLAB default orientation (e.g. +Y) +% out_path = derivatives/BIDS-Lossless-EEG Relative path to output data files assuming cd = work_path + +%% LOAD COMPART DATASET +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Loads the data file and the first amica model. + +logging_log('INFO', 'Loading set file: ./sub-003/eeg/sub-003_task-faceFO_compart_data.set...'); +tic; +EEG = pop_loadset('filepath','derivatives/BIDS-Lossless-EEG','filename','./sub-003/eeg/sub-003_task-faceFO_compart_data.set'); +EEG = eeg_checkset( EEG ); +logging_log('INFO', 'TIME TO: LOAD DATASET...'); +toc + +% Set up the ICA channels +EEG.icachansind = marks_label2index(EEG.marks.chan_info,{'manual','rank'},'indices','invert','on'); + +% Load Amica A +logging_log('INFO', 'Loading Amica ABC...'); +tic; +EEG.amica(2).models=loadmodout15('derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_amicaout_A'); +EEG.icaweights=EEG.amica(2).models.W; +EEG.icasphere=EEG.amica(2).models.S(1:EEG.amica(2).models.num_pcs,:); + +%% PASS EEG DATA THROUGH THE AMICA MATRIX +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Loads the B and C amica models. Amica A is used to fill in the ICA information. + +% Instead of EEG=eeg_checkset(EEG); +tmpdata = EEG.data(EEG.icachansind,:); +tmpindices = find(~sum(isnan(tmpdata))); +EEG.icaact = zeros(size(EEG.icaweights,1), size(tmpdata,2)); EEG.icaact(:) = NaN; +EEG.icaact(:,tmpindices) = (EEG.icaweights*EEG.icasphere)*tmpdata(:,tmpindices); +clear tmpdata; + +% Create winv for topo plots manually +EEG.icawinv = []; +EEG.icawinv = pinv(EEG.icaweights * EEG.icasphere); + +% Load Amica B +EEG.amica(3).models=loadmodout15('derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_amicaout_B'); +% Load Amica C +EEG.amica(4).models=loadmodout15('derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_amicaout_C'); +logging_log('INFO', 'TIME TO: LOAD ICA ABC INFO...'); +toc + +%% UPDATE REJECTION STRUCTURE... +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Creating flag marks to based on the log liklyhodd models found for each of the ICA models + +% Flags for amicas A B and C +logging_log('INFO', 'Updating latflaginfo structure...'); +tic; + +% A +logl_A_flags=zeros(size(EEG.data(1,:))); +logl_A_flag_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indices','invert','on'); + +% Check to see if the overwrite will be successful. If the overwirte is a different size then some of the old data will be retained. +if length(EEG.amica(2).models.Lt) ~= length(logl_A_flag_inds) + EEG.amica(2).models.Lt = EEG.amica(2).models.Lt(1:length(logl_A_flag_inds)); + warning('YOU FORGOT TO DELETE YOUR OLD AMICA FILES AND THE NEW CHANNEL SIZES DO NOT MATCH UP'); +end + +logl_A_flags(logl_A_flag_inds)=(EEG.amica(2).models.Lt-min(EEG.amica(2).models.Lt))/(max(EEG.amica(2).models.Lt)-min(EEG.amica(2).models.Lt)); +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'logl_A',[1,0.45,0],logl_A_flags}); +clear logl_A_flags; +% B +logl_B_flags=zeros(size(EEG.data(1,:))); +logl_B_flag_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indices','invert','on'); +logl_B_flags(logl_B_flag_inds)=(EEG.amica(3).models.Lt-min(EEG.amica(3).models.Lt))/(max(EEG.amica(3).models.Lt)-min(EEG.amica(3).models.Lt)); +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'logl_B',[1,0.45,0],logl_B_flags}); +clear logl_B_flags; +% C +logl_C_flags=zeros(size(EEG.data(1,:))); +logl_C_flag_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indices','invert','on'); +logl_C_flags(logl_C_flag_inds)=(EEG.amica(4).models.Lt-min(EEG.amica(4).models.Lt))/(max(EEG.amica(4).models.Lt)-min(EEG.amica(4).models.Lt)); +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'logl_C',[1,0.45,0],logl_C_flags}); +clear logl_C_flags; + +logging_log('INFO', 'TIME TO: UPDATE REJECTION STRUCTURE...'); +toc + +%% IDENTIFY RELIABLE COMPONENTS USING ISCTEST +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Gives the A variable of each of the amicas to the function icatest. Returned are the indecies of the +% Components that were not replicated across all 3 of the models. + +tic; +% Clear a in case of local models are saved already +a = []; +for i=2:length(EEG.amica); + a(:,:,i-1)=EEG.amica(i).models(1).A; +end + +% Calculating the replicated component flag +% Replicate criteria changed from 0.05,0.05 to 0.1,0.1 to be less sensitive +ca=isctest(a,0.05,0.05,'mixing'); +if isempty(ca) + mess = ['ISCTest failed to return reasonable values. ISCTest is ' ... + 'meant to find duplicate channels in amica_{A,B,C} if this ' ... + 'failed consider investigating the datafile']; + logging_log('ERROR', mess); + error(mess); +end +icr_inds=setdiff(1:min(size(EEG.amica(2).models(1).A)),ca(:,1)); + +% Creating the replicated component flag +logging_log('INFO', 'Updating chflaginfo structure...'); +icr_comp_flags=zeros(min(size(EEG.amica(2).models(1).A)),1); +icr_comp_flags(icr_inds)=1; +EEG.marks=marks_add_label(EEG.marks,'comp_info', ... + {'ic_rt',[0,0,1],[0,0,1],-1,icr_comp_flags}); +logging_log('INFO', 'TIME TO: EDIT FIND REPLICATED COMPONENTS AND UPDATE COMPINFO STRUCT...'); +toc + +% Window the continuous data +logging_log('INFO', 'Windowing the continous data...'); +tic; +EEG=marks_continuous2epochs(EEG,'recurrence',1,'limits',[0 1]); +logging_log('INFO', 'TIME TO: WINDOW THE CONTINUOUS DATA...'); +toc + +%% CALCULATE ICAACT SD +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Similarly to the IC_SD flag created in compart. Marks sections of time where the components +% have an abnormally high standard deviation. + +% Calculating the replicated component flag +logging_log('INFO', 'Calculating the IC sd array for window criteria...'); +tic; +epoch_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indexes','invert','on'); +[EEG,icaact_sd2_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'varmeasure','absmean', ... + 'detrend','off', ... + 'plot_figs','off'); + +% Creating the ic_sd component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_sd_t_inds]=marks_array2flags(icaact_sd2_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); + +icsd_epoch_flags=zeros(size(EEG.data(1,:,:))); +icsd_epoch_flags(1,:,epoch_inds(flag_sd_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_sd2',[.4,.6,.6],icsd_epoch_flags}); +logging_log('INFO', 'TIME TO: EDIT FIND IC_SD AND UPDATE TIMEINFO REJECTION STRUCT...'); +toc +clear icsd_epoch_flags; + +%% CALCULATE DELTA/THETA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high Theta/Alpha Power + +% Calculating the Delta/Theta component flag +logging_log('INFO', 'Calculating the delta/theta IC power array for window criteria...'); +tic; +[EEG,icaact_dt_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[0 7], ... + 'plot_figs','off'); + +% Creating the Delta/Theta component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_dt_t_inds]=marks_array2flags(icaact_dt_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +icdt_epoch_flags=zeros(size(EEG.data(1,:,:))); +icdt_epoch_flags(1,:,epoch_inds(flag_dt_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_dt',[1,1,0],icdt_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE DELTA/THETA ICAACT POWER AND UPDATE MARKS...'); +toc +clear icdt_epoch_flags; + +%% CALCULATE ALPHA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high Alpha Power + +% Calculating the Alpha component flag +logging_log('INFO', 'Calculating the alpha IC power array for window criteria...'); +tic; +[EEG,icaact_a_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[8 13], ... + 'plot_figs','off'); + +% Creating the Alpha component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_a_t_inds]=marks_array2flags(icaact_a_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +ica_epoch_flags=zeros(size(EEG.data(1,:,:))); +ica_epoch_flags(1,:,epoch_inds(flag_a_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_a',[1,1,0],ica_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE ALPHA ICAACT POWER AND UPDATE MARKS...'); +toc +clear ica_epoch_flags; + +%% CALCULATE BETA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high Beta Power. +% Note that at the moment this mark is used for removals. + +% Calculating the Beta component flag +logging_log('INFO', 'Calculating the beta IC power array for window criteria...'); +tic; +[EEG,icaact_b_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[14 30], ... + 'plot_figs','off'); +logging_log('INFO', 'TIME TO: CALCULATE BETA ICAACT POWER...'); + +% Creating the Beta component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_b_t_inds]=marks_array2flags(icaact_b_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +logging_log('INFO', 'TIME TO: CREATE WINDOW CRITERIA VECTOR...'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +icb_epoch_flags=zeros(size(EEG.data(1,:,:))); +icb_epoch_flags(1,:,epoch_inds(flag_b_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_b',[0,1,1],icb_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE BETA ICAACT POWER AND UPDATE MARKS...'); +toc +clear icb_epoch_flags; + +%% CALCULATE LOW GAMMA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high Low-Gamma Power. +% Note that at the moment this mark is used for removals. + +% Calculating the Beta component flag +logging_log('INFO', 'Calculating the low-gamma IC power array for window criteria...'); +tic; +[EEG,icaact_lg_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[31 60], ... + 'plot_figs','off'); +logging_log('INFO', 'TIME TO: CALCULATE LOW-GAMMA ICAACT POWER...'); + +% Creating the LOW-GAMMA component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_lg_t_inds]=marks_array2flags(icaact_lg_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +logging_log('INFO', 'TIME TO: CREATE WINDOW CRITERIA VECTOR...'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +iclg_epoch_flags=zeros(size(EEG.data(1,:,:))); +iclg_epoch_flags(1,:,epoch_inds(flag_lg_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_lg',[0,1,1],iclg_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE LOW-GAMMA ICAACT POWER AND UPDATE MARKS...'); +toc +clear iclg_epoch_flags; + + +%% CALCULATE HIGH GAMMA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high High-Gamma Power. +% Note that at the moment this mark is used for removals. + +% Calculating the Beta component flag +logging_log('INFO', 'Calculating the high-gamma IC power array for window criteria...'); +tic; +[EEG,icaact_hg_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[61 100], ... + 'plot_figs','off'); +logging_log('INFO', 'TIME TO: CALCULATE HIGH-GAMMA ICAACT POWER...'); + +% Creating the HIGH-GAMMA component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_hg_t_inds]=marks_array2flags(icaact_hg_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +logging_log('INFO', 'TIME TO: CREATE WINDOW CRITERIA VECTOR...'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +ichg_epoch_flags=zeros(size(EEG.data(1,:,:))); +ichg_epoch_flags(1,:,epoch_inds(flag_hg_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_hg',[0,1,1],ichg_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE HIGH-GAMMA ICAACT POWER AND UPDATE MARKS...'); +toc +clear ichg_epoch_flags; + +%% MARKS UPDATE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Merges the epochs back to continuous data. Flags marks gaps and combines time +% the new time marks into the manual marks. + +% Concat epochs back into continuous data +logging_log('INFO', 'Concatenating windowed data...'); +tic; +EEG=marks_epochs2continuous(EEG); +EEG=eeg_checkset(EEG,'eventconsistency'); +logging_log('INFO', 'TIME TO: CONCATENATE THE WINDOWED DATA INTO CONTINUOUS DATA...'); +toc + +% Mark flag gaps between time marks +tic; +EEG=pop_marks_flag_gap(EEG,{'manual','mark_gap','ic_sd2'}, ... + 2000,'mark_gap',[.8,.8,.8],'offsets',[0 0],'ref_point','both'); +logging_log('INFO', 'TIME TO: MARK FLAG GAPS THAT ARE < 2 SECONDS...'); +toc + +% Combine the removal marks into manual +tic; +EEG=pop_marks_merge_labels(EEG,'time_info',{'manual','ic_sd2','mark_gap'},'target_label','manual'); +logging_log('INFO', 'TIME TO: COMBINE MARKS STRUCTURE INTO MANUAL FLAGS...'); +toc + +% begin prep for dipole fit +logging_log('INFO','Checking for JVM...'); +javachk('jvm'); + +% TODO(mae) this isn't added for some reason +addpath([pwd '/derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/Fieldtrip-lite141209/src']) + +%% DIPFIT +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Does the dipole fitting procedures. This takes a substantial amount of +% time. +% TODO: Make sure this is all needed, seems to be repeating things +logging_log('INFO','FITTING DIPOLES...'); +tic; +EEG = pop_dipfit_settings( EEG, 'hdmfile','derivatives/BIDS-Lossless-EEG/code/misc/standard_vol.mat', ... + 'coordformat','MNI', ... + 'mrifile','derivatives/BIDS-Lossless-EEG/code/misc/standard_mri.mat', ... + 'chanfile','derivatives/BIDS-Lossless-EEG/code/misc/standard_1020.elc', ... + 'coord_transform',[0 0 0 0 0 0 1 1 1], ... + 'chansel',[1:EEG.nbchan] ); + +% OLD SINGLE THREADED SETUP +% EEG = pop_multifit(EEG, [1:EEG.nbchan] ,'threshold',100,'plotopt',{'normlen' 'on'}); + +% NEW MULTI-THREADED SETUP +EEG = pop_multifit_bucanl_parallel(EEG, [1:EEG.nbchan], 8,'threshold',100,'plotopt',{'normlen' 'on'}); + +EEG=eeg_checkset(EEG); +logging_log('INFO','TIME TO: FIT DIPOLES...'); +toc + +%% MARK ICs FOR REJECTION BASED ON RV +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Creates marks based on dipfit results. Dipfit marks are added +% for rejection while ICMARC are just for reference. +% IC RESIDUAL VARIANCE FLAG +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Residuals greater than 15% are flagged - can be done here as it only references +% dipoles and their values. +%logging_log('INFO','UPDATE MARKS STRUCTURE WITH IC_RV...'); +%tic; +% Dipole criteria changed from 0.15 to 0.2 to be less sensitive +%EEG.reject.gcompreject(find([EEG.dipfit.model.rv]>.15))=1; +%EEG.reject.gcompreject(find(isnan([EEG.dipfit.model.rv])))=1; +%EEG.marks=marks_add_label(EEG.marks,'comp_info', ... +% {'ic_rv',[1,0,0],[1,0,0],-1,EEG.reject.gcompreject'}); +%logging_log('INFO','TIME TO: UPDATE MARKS STRUCTURE...'); +%toc + +% COMBINE MARKS STRUCTURE INTO MANUAL FLAGS... +%tic; +%EEG=pop_marks_merge_labels(EEG,'comp_info',{'manual','ic_rv'},'target_label','manual'); +%logging_log('INFO','TIME TO: COMBINE MARKS STRUCTURE INTO MANUAL FLAGS...'); +%toc + +% Adjust montage +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% This will pretty much always be dataset specific - so be careful +EEG = warp_locs( EEG, 'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020.elc', ... + 'transform',[0,0,0,0,0,1.57,1,1,1], ... + 'manual','off'); +EEG.chaninfo.nosedir='+X'; +EEG=eeg_checkset(EEG); + +%% SAVE LOSSLESS DERIVATIVE *_eeg_ll.set FILE (same name as the input file from the +% BIDS root directory [but located in the derivatives/lossless directory]). +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +logging_log('INFO','Saving set file: derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_eeg_ll.set...'); +tic; +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_eeg_ll.set'); +logging_log('INFO','TIME TO: SAVE DIP DATA SET...'); +toc + +% Save diagnostic arrays +try OCTAVE_VERSION; + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_icaact_sd2_t.mat','icaact_sd2_t'); +catch + save('derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_icaact_sd2_t.mat','icaact_sd2_t'); +end + +logging_log('INFO', 'Scheduler: sbatch'); +print_chan_sample(EEG); + + +if ~find(strcmpi({EEG.marks.chan_info.label},'ch_s_sd')); + EEG.marks=marks_add_label(EEG.marks,'chan_info',{'ch_s_sd',[1,0,0],[]}); +end; + +EEG = eeg_checkset(EEG); + +% CHECK FOR IC CLASSIFICATION MARKS +%% Use the ICLabel plugin +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% ICLabel is a database style plugin that classifies ICA components into the +% following seven categories + +% (Brain)(Muscle)(Eye)(Heart)(Line Noise)(Channel Noise)(Other) +% ( 1 )( 2 )( 3 )( 4 )( 5 )( 6 )( 7 ) + +% Colour structure and groupings are as follows: +% ---------- +% Brain -> [0, 0.4, 0] +% ---------- +% Muscle -> [0.4, 0, 0] +% Eye -> [0.8, 0, 0] +% Heart -> [0.8, 0.4, 0] +% ---------- +% Line Noise -> [0.2, 0.4, 0.8] +% Channel noise -> [0.4, 0, 0.8] +% ---------- +% Other -> [0.8, 0.8, 0] +% ---------- + +% Create lookup table of the above information. +pairs = {'brain',[0, 0.4, 0]; +'muscle',[0.4,0,0]; +'eye',[0.8,0,0]; +'heart',[0.8,0.4,0]; +'line_noise',[0.2,0.4,0.8]; +'chan_noise',[0.4,0,0.8]; +'other',[.7 .4 0]}; +lookupTable = cell2struct(pairs, {'name', 'colour'}, 2); + +EEG = pop_marks_select_data(EEG,'time marks',[],'labels',{'manual'},'remove','on'); + +% Run ICLabel +%EEG = iclabel(EEG); + +% extract features +disp 'ICLabel: extracting features...' +features = ICL_feature_extractor(EEG, 40); + +% run ICL +disp 'ICLabel: calculating labels...' +labels = run_ICL('default',features{:}); + +% save into EEG +disp 'ICLabel: saving results...' +EEG.etc.ic_classification.ICLabel.classes = ... + {'Brain', 'Muscle', 'Eye', 'Heart', 'Line', 'Channel', 'Other'}; +EEG.etc.ic_classification.ICLabel.classifications = labels; + +tmp=EEG.etc.ic_classification; + +% Reload dataset: +EEG = pop_loadset('filepath','derivatives/BIDS-Lossless-EEG','filename','./sub-003/eeg/sub-003_task-faceFO_eeg_ll.set'); +EEG = eeg_checkset(EEG); + +EEG.etc.ic_classification=tmp; + +% Extract labels from the structure. +rawLabels = zeros([1,length(EEG.etc.ic_classification.ICLabel.classifications)]); +for index = 1:length(EEG.etc.ic_classification.ICLabel.classifications) + [p,i] = max(EEG.etc.ic_classification.ICLabel.classifications(index,1:end-1)); + if p<.3; + i=7; + end + rawLabels(index) = i; +end + +%ICLabel Exporting to marks +%%%%%%%%%%%%%%%%%%%%%%%%%%%% +logging_log('INFO','UPDATE MARKS STRUCTURE WITH ICLABEL BRAIN COMPS...'); +for compStage = 1:7 + ICLabel_pass= zeros([1,length(rawLabels)]); + ICLabel_pass(find(rawLabels==compStage)) = 1; + ICLabel_pass = ICLabel_pass'; + EEG.marks=marks_add_label(EEG.marks,'comp_info', ... + {lookupTable(compStage).name,lookupTable(compStage).colour, ... + lookupTable(compStage).colour,1,ICLabel_pass}); +end +% make any brain components always on top: +EEG.marks.comp_info(1,find(strcmp({EEG.marks.comp_info.label},'brain'))).order=2; + + +%RESET/INITIATE CHAN MARKS... +manual_ind=find(strcmp('manual',{EEG.marks.chan_info.label})); + + +%RESET/INITIATE IC MARKS... +EEG.marks=marks_add_label(EEG.marks,'comp_info',{'ambig',[.3,.3,1],[.3,.3,1],3,zeros(min(size(EEG.icaweights)),1)}); +manual_ind=find(strcmp('manual',{EEG.marks.comp_info.label})); +ch_s_sd_ind=find(strcmp('ch_s_sd',{EEG.marks.chan_info.label})); +ch_sd_ind=find(strcmp('ch_sd',{EEG.marks.chan_info.label})); +low_r_ind=find(strcmp('low_r',{EEG.marks.chan_info.label})); +bridge_ind=find(strcmp('bridge',{EEG.marks.chan_info.label})); +rank_ind=find(strcmp('rank',{EEG.marks.chan_info.label})); +icrt_ind=find(strcmp('ic_rt',{EEG.marks.comp_info.label})); +brain_ind=find(strcmp('brain',{EEG.marks.comp_info.label})); +muscle_ind=find(strcmp('muscle',{EEG.marks.comp_info.label})); +eye_ind=find(strcmp('eye',{EEG.marks.comp_info.label})); +heart_ind=find(strcmp('heart',{EEG.marks.comp_info.label})); +line_noise_ind=find(strcmp('line_noise',{EEG.marks.comp_info.label})); +chan_noise_ind=find(strcmp('chan_noise',{EEG.marks.comp_info.label})); +other_ind=find(strcmp('other',{EEG.marks.comp_info.label})); +ambig_ind=find(strcmp('ambig',{EEG.marks.comp_info.label})); + +EEG.marks.comp_info(manual_ind).order=1; +if ~isempty(ch_s_sd_ind); % skip study-wide criteria if not used + EEG.marks.comp_info(ch_s_sd_ind).order=2; +end; +EEG.marks.comp_info(ch_sd_ind).order=2; +EEG.marks.comp_info(low_r_ind).order=2; +EEG.marks.comp_info(bridge_ind).order=2; +EEG.marks.comp_info(rank_ind).order=2; +EEG.marks.comp_info(icrt_ind).order=1; + +EEG.marks.comp_info(brain_ind).order=2; +EEG.marks.comp_info(muscle_ind).order=2; +EEG.marks.comp_info(eye_ind).order=2; +EEG.marks.comp_info(heart_ind).order=2; +EEG.marks.comp_info(line_noise_ind).order=2; +EEG.marks.comp_info(chan_noise_ind).order=2; +EEG.marks.comp_info(other_ind).order=2; +EEG.marks.comp_info(ambig_ind).order=3; + +bioa_inds = marks_label2index(EEG.marks.comp_info,{'muscle','eye','heart','chan_noise'},'indexes'); +EEG.marks.comp_info(manual_ind).flags=zeros(min(size(EEG.icaweights)),1); +EEG.marks.comp_info(manual_ind).flags(bioa_inds)=1; + +% BIDS Export calls +% Dot for path is on purpose. Don't worry - it's safe. +fName = './derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_eeg.edf'; +pop_writeeeg(EEG, fName, 'TYPE','EDF'); +dlmwrite(strrep(fName,'_eeg.edf','_icaweights.tsv'),EEG.icaweights,'\t'); +dlmwrite(strrep(fName,'_eeg.edf','_icasphere.tsv'),EEG.icasphere,'\t'); +s = {}; +s.algorithm = 'amica15'; +s.icachansind = EEG.icachansind; +s.intendedFor = fName; +savejson('',s,strrep(fName,'_eeg.edf','_icaweights.json')); +export_anno(EEG,fName,''); + +% ICLabel quick export +iclFname = strrep(fName,'_eeg.edf','_iclabel.mat'); +save(iclFname, 'tmp'); % see above ICLabel section for tmp definition diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/s05_concat_data_sub_004_task_faceFO_eeg.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/s05_concat_data_sub_004_task_faceFO_eeg.m new file mode 100644 index 000000000..f8ccc7c9e --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/s05_concat_data_sub_004_task_faceFO_eeg.m @@ -0,0 +1,678 @@ +warning off; + +pkg load signal; +pkg load parallel; +pkg load io; + +file_path = fileparts(mfilename('fullpath')); + +addpath([file_path '/../../code/dependencies/eeglab_lossless'], ... + [file_path '/../../code/dependencies/matlog'], ... + [file_path '/../../code/tools']); +cd '/scratch/tk11br/Face13/'; + +% eeglab auto loads the path correctly instead of shotgun blasting with +% addpath(genpath(<>)) as we were doing prior +if ~exist('EEG', 'var') + eeglab; +end +% Use matlog disp function override +logging_override_disp(true); +% Use matlog INFO level debugging +logging_set_log_level('INFO') + +% Don't run with traditional but enable some of the options +save_default_options ('-mat-binary'); +confirm_recursive_rmdir(false); + +% This makes | and & || and && in earlier version of Octave where +% | and & dont short curcuit by default +if exist('do_braindead_shortcircuit_evaluation', 'builtin') + do_braindead_shortcircuit_evaluation(true); +end + +% Options +pop_editoptions('option_savetwofiles', 1,'option_single',0); + +%% SCRIPT DESCRIPTION +% This script combineds the results of the amicas A,B and C. It takes this information and using ICAtest it determines which components were +% and were not replicated across and flags them, along with time sections with high standard deviation. Beta,theta and Alpha power is calculated +% arcoss time and is marked but not primed for removal. +% +%% From Config key_strswap Description +%----------------------------------------------------------------------------------------------------------------------------- +% in_path = derivatives/BIDS-Lossless-EEG Relative path to input data files assuming cd = work_path +% recur_sec = 1 Recurrence (sec) for the current artifact detection epoching (e.g. 1) +% limit_sec = [0 1] Limits (sec) for the current artifact detection epoching (e.g. [0 1]) +% sd_t_meth = q Method used for flagging epochs (e.g. 'q' (quantiles), or 'na' (default)) +% sd_t_vals = [.3 .7] Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7]) +% sd_t_o = 6 z threshold for flagging epochs during standard deviation criteria (e.g. 6) +% sd_t_f_meth = fixed Fixed method used for flagging epochs (should be 'fixed') +% sd_t_f_vals = Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% sd_t_f_o = .2 z threshold for flagging epochs during fixed standard deviation criteria (e.g. .2) +% min_gap_ms = 2000 Minimum time (ms) to allow between periods marked for rejection +% icr_crit = 0.05 False discovery rate of component replicatablilty, measure of sensitivity % +% dip_vol = derivatives/BIDS-Lossless-EEG/code/misc/standard_vol.mat file path/name of the standard volume file used in dipole fitting (e.g. derivatives/lossless/code/misc/standard_vol.mat). +% dip_mri = derivatives/BIDS-Lossless-EEG/code/misc/standard_mri.mat file path/name of the standard mri file used in dipole fitting (e.g. derivatives/lossless/code/misc/standard_mri.mat). +% dip_elc = derivatives/BIDS-Lossless-EEG/code/misc/standard_1020.elc file path/name of the standard electrode file used in dipole fitting (derivatives/lossless/code/misc/standard_1020.elc). +% dip_transmat = [0 0 0 0 0 0 1 1 1] Transformation matrix to apply during dipole fit (e.g. [0 0 0 0 0 0 1 1 1]) +% cor_transmat = [0,0,0,0,0,1.57,1,1,1] The transformation matrix to apply between dipole fitting to the standard head and performing ICLabel to the EEGLAB default orientation (e.g.[0,0,0,0,0,1.57,1,1,1]) +% cor_nosedir = +X Nose direction of montage to apply between dipole fitting to the standard head and performing ICLabel to the EEGLAB default orientation (e.g. +Y) +% out_path = derivatives/BIDS-Lossless-EEG Relative path to output data files assuming cd = work_path + +%% LOAD COMPART DATASET +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Loads the data file and the first amica model. + +logging_log('INFO', 'Loading set file: ./sub-004/eeg/sub-004_task-faceFO_compart_data.set...'); +tic; +EEG = pop_loadset('filepath','derivatives/BIDS-Lossless-EEG','filename','./sub-004/eeg/sub-004_task-faceFO_compart_data.set'); +EEG = eeg_checkset( EEG ); +logging_log('INFO', 'TIME TO: LOAD DATASET...'); +toc + +% Set up the ICA channels +EEG.icachansind = marks_label2index(EEG.marks.chan_info,{'manual','rank'},'indices','invert','on'); + +% Load Amica A +logging_log('INFO', 'Loading Amica ABC...'); +tic; +EEG.amica(2).models=loadmodout15('derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_amicaout_A'); +EEG.icaweights=EEG.amica(2).models.W; +EEG.icasphere=EEG.amica(2).models.S(1:EEG.amica(2).models.num_pcs,:); + +%% PASS EEG DATA THROUGH THE AMICA MATRIX +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Loads the B and C amica models. Amica A is used to fill in the ICA information. + +% Instead of EEG=eeg_checkset(EEG); +tmpdata = EEG.data(EEG.icachansind,:); +tmpindices = find(~sum(isnan(tmpdata))); +EEG.icaact = zeros(size(EEG.icaweights,1), size(tmpdata,2)); EEG.icaact(:) = NaN; +EEG.icaact(:,tmpindices) = (EEG.icaweights*EEG.icasphere)*tmpdata(:,tmpindices); +clear tmpdata; + +% Create winv for topo plots manually +EEG.icawinv = []; +EEG.icawinv = pinv(EEG.icaweights * EEG.icasphere); + +% Load Amica B +EEG.amica(3).models=loadmodout15('derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_amicaout_B'); +% Load Amica C +EEG.amica(4).models=loadmodout15('derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_amicaout_C'); +logging_log('INFO', 'TIME TO: LOAD ICA ABC INFO...'); +toc + +%% UPDATE REJECTION STRUCTURE... +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Creating flag marks to based on the log liklyhodd models found for each of the ICA models + +% Flags for amicas A B and C +logging_log('INFO', 'Updating latflaginfo structure...'); +tic; + +% A +logl_A_flags=zeros(size(EEG.data(1,:))); +logl_A_flag_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indices','invert','on'); + +% Check to see if the overwrite will be successful. If the overwirte is a different size then some of the old data will be retained. +if length(EEG.amica(2).models.Lt) ~= length(logl_A_flag_inds) + EEG.amica(2).models.Lt = EEG.amica(2).models.Lt(1:length(logl_A_flag_inds)); + warning('YOU FORGOT TO DELETE YOUR OLD AMICA FILES AND THE NEW CHANNEL SIZES DO NOT MATCH UP'); +end + +logl_A_flags(logl_A_flag_inds)=(EEG.amica(2).models.Lt-min(EEG.amica(2).models.Lt))/(max(EEG.amica(2).models.Lt)-min(EEG.amica(2).models.Lt)); +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'logl_A',[1,0.45,0],logl_A_flags}); +clear logl_A_flags; +% B +logl_B_flags=zeros(size(EEG.data(1,:))); +logl_B_flag_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indices','invert','on'); +logl_B_flags(logl_B_flag_inds)=(EEG.amica(3).models.Lt-min(EEG.amica(3).models.Lt))/(max(EEG.amica(3).models.Lt)-min(EEG.amica(3).models.Lt)); +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'logl_B',[1,0.45,0],logl_B_flags}); +clear logl_B_flags; +% C +logl_C_flags=zeros(size(EEG.data(1,:))); +logl_C_flag_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indices','invert','on'); +logl_C_flags(logl_C_flag_inds)=(EEG.amica(4).models.Lt-min(EEG.amica(4).models.Lt))/(max(EEG.amica(4).models.Lt)-min(EEG.amica(4).models.Lt)); +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'logl_C',[1,0.45,0],logl_C_flags}); +clear logl_C_flags; + +logging_log('INFO', 'TIME TO: UPDATE REJECTION STRUCTURE...'); +toc + +%% IDENTIFY RELIABLE COMPONENTS USING ISCTEST +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Gives the A variable of each of the amicas to the function icatest. Returned are the indecies of the +% Components that were not replicated across all 3 of the models. + +tic; +% Clear a in case of local models are saved already +a = []; +for i=2:length(EEG.amica); + a(:,:,i-1)=EEG.amica(i).models(1).A; +end + +% Calculating the replicated component flag +% Replicate criteria changed from 0.05,0.05 to 0.1,0.1 to be less sensitive +ca=isctest(a,0.05,0.05,'mixing'); +if isempty(ca) + mess = ['ISCTest failed to return reasonable values. ISCTest is ' ... + 'meant to find duplicate channels in amica_{A,B,C} if this ' ... + 'failed consider investigating the datafile']; + logging_log('ERROR', mess); + error(mess); +end +icr_inds=setdiff(1:min(size(EEG.amica(2).models(1).A)),ca(:,1)); + +% Creating the replicated component flag +logging_log('INFO', 'Updating chflaginfo structure...'); +icr_comp_flags=zeros(min(size(EEG.amica(2).models(1).A)),1); +icr_comp_flags(icr_inds)=1; +EEG.marks=marks_add_label(EEG.marks,'comp_info', ... + {'ic_rt',[0,0,1],[0,0,1],-1,icr_comp_flags}); +logging_log('INFO', 'TIME TO: EDIT FIND REPLICATED COMPONENTS AND UPDATE COMPINFO STRUCT...'); +toc + +% Window the continuous data +logging_log('INFO', 'Windowing the continous data...'); +tic; +EEG=marks_continuous2epochs(EEG,'recurrence',1,'limits',[0 1]); +logging_log('INFO', 'TIME TO: WINDOW THE CONTINUOUS DATA...'); +toc + +%% CALCULATE ICAACT SD +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Similarly to the IC_SD flag created in compart. Marks sections of time where the components +% have an abnormally high standard deviation. + +% Calculating the replicated component flag +logging_log('INFO', 'Calculating the IC sd array for window criteria...'); +tic; +epoch_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indexes','invert','on'); +[EEG,icaact_sd2_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'varmeasure','absmean', ... + 'detrend','off', ... + 'plot_figs','off'); + +% Creating the ic_sd component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_sd_t_inds]=marks_array2flags(icaact_sd2_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); + +icsd_epoch_flags=zeros(size(EEG.data(1,:,:))); +icsd_epoch_flags(1,:,epoch_inds(flag_sd_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_sd2',[.4,.6,.6],icsd_epoch_flags}); +logging_log('INFO', 'TIME TO: EDIT FIND IC_SD AND UPDATE TIMEINFO REJECTION STRUCT...'); +toc +clear icsd_epoch_flags; + +%% CALCULATE DELTA/THETA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high Theta/Alpha Power + +% Calculating the Delta/Theta component flag +logging_log('INFO', 'Calculating the delta/theta IC power array for window criteria...'); +tic; +[EEG,icaact_dt_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[0 7], ... + 'plot_figs','off'); + +% Creating the Delta/Theta component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_dt_t_inds]=marks_array2flags(icaact_dt_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +icdt_epoch_flags=zeros(size(EEG.data(1,:,:))); +icdt_epoch_flags(1,:,epoch_inds(flag_dt_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_dt',[1,1,0],icdt_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE DELTA/THETA ICAACT POWER AND UPDATE MARKS...'); +toc +clear icdt_epoch_flags; + +%% CALCULATE ALPHA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high Alpha Power + +% Calculating the Alpha component flag +logging_log('INFO', 'Calculating the alpha IC power array for window criteria...'); +tic; +[EEG,icaact_a_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[8 13], ... + 'plot_figs','off'); + +% Creating the Alpha component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_a_t_inds]=marks_array2flags(icaact_a_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +ica_epoch_flags=zeros(size(EEG.data(1,:,:))); +ica_epoch_flags(1,:,epoch_inds(flag_a_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_a',[1,1,0],ica_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE ALPHA ICAACT POWER AND UPDATE MARKS...'); +toc +clear ica_epoch_flags; + +%% CALCULATE BETA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high Beta Power. +% Note that at the moment this mark is used for removals. + +% Calculating the Beta component flag +logging_log('INFO', 'Calculating the beta IC power array for window criteria...'); +tic; +[EEG,icaact_b_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[14 30], ... + 'plot_figs','off'); +logging_log('INFO', 'TIME TO: CALCULATE BETA ICAACT POWER...'); + +% Creating the Beta component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_b_t_inds]=marks_array2flags(icaact_b_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +logging_log('INFO', 'TIME TO: CREATE WINDOW CRITERIA VECTOR...'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +icb_epoch_flags=zeros(size(EEG.data(1,:,:))); +icb_epoch_flags(1,:,epoch_inds(flag_b_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_b',[0,1,1],icb_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE BETA ICAACT POWER AND UPDATE MARKS...'); +toc +clear icb_epoch_flags; + +%% CALCULATE LOW GAMMA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high Low-Gamma Power. +% Note that at the moment this mark is used for removals. + +% Calculating the Beta component flag +logging_log('INFO', 'Calculating the low-gamma IC power array for window criteria...'); +tic; +[EEG,icaact_lg_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[31 60], ... + 'plot_figs','off'); +logging_log('INFO', 'TIME TO: CALCULATE LOW-GAMMA ICAACT POWER...'); + +% Creating the LOW-GAMMA component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_lg_t_inds]=marks_array2flags(icaact_lg_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +logging_log('INFO', 'TIME TO: CREATE WINDOW CRITERIA VECTOR...'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +iclg_epoch_flags=zeros(size(EEG.data(1,:,:))); +iclg_epoch_flags(1,:,epoch_inds(flag_lg_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_lg',[0,1,1],iclg_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE LOW-GAMMA ICAACT POWER AND UPDATE MARKS...'); +toc +clear iclg_epoch_flags; + + +%% CALCULATE HIGH GAMMA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high High-Gamma Power. +% Note that at the moment this mark is used for removals. + +% Calculating the Beta component flag +logging_log('INFO', 'Calculating the high-gamma IC power array for window criteria...'); +tic; +[EEG,icaact_hg_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[61 100], ... + 'plot_figs','off'); +logging_log('INFO', 'TIME TO: CALCULATE HIGH-GAMMA ICAACT POWER...'); + +% Creating the HIGH-GAMMA component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_hg_t_inds]=marks_array2flags(icaact_hg_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +logging_log('INFO', 'TIME TO: CREATE WINDOW CRITERIA VECTOR...'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +ichg_epoch_flags=zeros(size(EEG.data(1,:,:))); +ichg_epoch_flags(1,:,epoch_inds(flag_hg_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_hg',[0,1,1],ichg_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE HIGH-GAMMA ICAACT POWER AND UPDATE MARKS...'); +toc +clear ichg_epoch_flags; + +%% MARKS UPDATE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Merges the epochs back to continuous data. Flags marks gaps and combines time +% the new time marks into the manual marks. + +% Concat epochs back into continuous data +logging_log('INFO', 'Concatenating windowed data...'); +tic; +EEG=marks_epochs2continuous(EEG); +EEG=eeg_checkset(EEG,'eventconsistency'); +logging_log('INFO', 'TIME TO: CONCATENATE THE WINDOWED DATA INTO CONTINUOUS DATA...'); +toc + +% Mark flag gaps between time marks +tic; +EEG=pop_marks_flag_gap(EEG,{'manual','mark_gap','ic_sd2'}, ... + 2000,'mark_gap',[.8,.8,.8],'offsets',[0 0],'ref_point','both'); +logging_log('INFO', 'TIME TO: MARK FLAG GAPS THAT ARE < 2 SECONDS...'); +toc + +% Combine the removal marks into manual +tic; +EEG=pop_marks_merge_labels(EEG,'time_info',{'manual','ic_sd2','mark_gap'},'target_label','manual'); +logging_log('INFO', 'TIME TO: COMBINE MARKS STRUCTURE INTO MANUAL FLAGS...'); +toc + +% begin prep for dipole fit +logging_log('INFO','Checking for JVM...'); +javachk('jvm'); + +% TODO(mae) this isn't added for some reason +addpath([pwd '/derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/Fieldtrip-lite141209/src']) + +%% DIPFIT +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Does the dipole fitting procedures. This takes a substantial amount of +% time. +% TODO: Make sure this is all needed, seems to be repeating things +logging_log('INFO','FITTING DIPOLES...'); +tic; +EEG = pop_dipfit_settings( EEG, 'hdmfile','derivatives/BIDS-Lossless-EEG/code/misc/standard_vol.mat', ... + 'coordformat','MNI', ... + 'mrifile','derivatives/BIDS-Lossless-EEG/code/misc/standard_mri.mat', ... + 'chanfile','derivatives/BIDS-Lossless-EEG/code/misc/standard_1020.elc', ... + 'coord_transform',[0 0 0 0 0 0 1 1 1], ... + 'chansel',[1:EEG.nbchan] ); + +% OLD SINGLE THREADED SETUP +% EEG = pop_multifit(EEG, [1:EEG.nbchan] ,'threshold',100,'plotopt',{'normlen' 'on'}); + +% NEW MULTI-THREADED SETUP +EEG = pop_multifit_bucanl_parallel(EEG, [1:EEG.nbchan], 8,'threshold',100,'plotopt',{'normlen' 'on'}); + +EEG=eeg_checkset(EEG); +logging_log('INFO','TIME TO: FIT DIPOLES...'); +toc + +%% MARK ICs FOR REJECTION BASED ON RV +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Creates marks based on dipfit results. Dipfit marks are added +% for rejection while ICMARC are just for reference. +% IC RESIDUAL VARIANCE FLAG +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Residuals greater than 15% are flagged - can be done here as it only references +% dipoles and their values. +%logging_log('INFO','UPDATE MARKS STRUCTURE WITH IC_RV...'); +%tic; +% Dipole criteria changed from 0.15 to 0.2 to be less sensitive +%EEG.reject.gcompreject(find([EEG.dipfit.model.rv]>.15))=1; +%EEG.reject.gcompreject(find(isnan([EEG.dipfit.model.rv])))=1; +%EEG.marks=marks_add_label(EEG.marks,'comp_info', ... +% {'ic_rv',[1,0,0],[1,0,0],-1,EEG.reject.gcompreject'}); +%logging_log('INFO','TIME TO: UPDATE MARKS STRUCTURE...'); +%toc + +% COMBINE MARKS STRUCTURE INTO MANUAL FLAGS... +%tic; +%EEG=pop_marks_merge_labels(EEG,'comp_info',{'manual','ic_rv'},'target_label','manual'); +%logging_log('INFO','TIME TO: COMBINE MARKS STRUCTURE INTO MANUAL FLAGS...'); +%toc + +% Adjust montage +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% This will pretty much always be dataset specific - so be careful +EEG = warp_locs( EEG, 'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020.elc', ... + 'transform',[0,0,0,0,0,1.57,1,1,1], ... + 'manual','off'); +EEG.chaninfo.nosedir='+X'; +EEG=eeg_checkset(EEG); + +%% SAVE LOSSLESS DERIVATIVE *_eeg_ll.set FILE (same name as the input file from the +% BIDS root directory [but located in the derivatives/lossless directory]). +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +logging_log('INFO','Saving set file: derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_eeg_ll.set...'); +tic; +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_eeg_ll.set'); +logging_log('INFO','TIME TO: SAVE DIP DATA SET...'); +toc + +% Save diagnostic arrays +try OCTAVE_VERSION; + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_icaact_sd2_t.mat','icaact_sd2_t'); +catch + save('derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_icaact_sd2_t.mat','icaact_sd2_t'); +end + +logging_log('INFO', 'Scheduler: sbatch'); +print_chan_sample(EEG); + + +if ~find(strcmpi({EEG.marks.chan_info.label},'ch_s_sd')); + EEG.marks=marks_add_label(EEG.marks,'chan_info',{'ch_s_sd',[1,0,0],[]}); +end; + +EEG = eeg_checkset(EEG); + +% CHECK FOR IC CLASSIFICATION MARKS +%% Use the ICLabel plugin +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% ICLabel is a database style plugin that classifies ICA components into the +% following seven categories + +% (Brain)(Muscle)(Eye)(Heart)(Line Noise)(Channel Noise)(Other) +% ( 1 )( 2 )( 3 )( 4 )( 5 )( 6 )( 7 ) + +% Colour structure and groupings are as follows: +% ---------- +% Brain -> [0, 0.4, 0] +% ---------- +% Muscle -> [0.4, 0, 0] +% Eye -> [0.8, 0, 0] +% Heart -> [0.8, 0.4, 0] +% ---------- +% Line Noise -> [0.2, 0.4, 0.8] +% Channel noise -> [0.4, 0, 0.8] +% ---------- +% Other -> [0.8, 0.8, 0] +% ---------- + +% Create lookup table of the above information. +pairs = {'brain',[0, 0.4, 0]; +'muscle',[0.4,0,0]; +'eye',[0.8,0,0]; +'heart',[0.8,0.4,0]; +'line_noise',[0.2,0.4,0.8]; +'chan_noise',[0.4,0,0.8]; +'other',[.7 .4 0]}; +lookupTable = cell2struct(pairs, {'name', 'colour'}, 2); + +EEG = pop_marks_select_data(EEG,'time marks',[],'labels',{'manual'},'remove','on'); + +% Run ICLabel +%EEG = iclabel(EEG); + +% extract features +disp 'ICLabel: extracting features...' +features = ICL_feature_extractor(EEG, 40); + +% run ICL +disp 'ICLabel: calculating labels...' +labels = run_ICL('default',features{:}); + +% save into EEG +disp 'ICLabel: saving results...' +EEG.etc.ic_classification.ICLabel.classes = ... + {'Brain', 'Muscle', 'Eye', 'Heart', 'Line', 'Channel', 'Other'}; +EEG.etc.ic_classification.ICLabel.classifications = labels; + +tmp=EEG.etc.ic_classification; + +% Reload dataset: +EEG = pop_loadset('filepath','derivatives/BIDS-Lossless-EEG','filename','./sub-004/eeg/sub-004_task-faceFO_eeg_ll.set'); +EEG = eeg_checkset(EEG); + +EEG.etc.ic_classification=tmp; + +% Extract labels from the structure. +rawLabels = zeros([1,length(EEG.etc.ic_classification.ICLabel.classifications)]); +for index = 1:length(EEG.etc.ic_classification.ICLabel.classifications) + [p,i] = max(EEG.etc.ic_classification.ICLabel.classifications(index,1:end-1)); + if p<.3; + i=7; + end + rawLabels(index) = i; +end + +%ICLabel Exporting to marks +%%%%%%%%%%%%%%%%%%%%%%%%%%%% +logging_log('INFO','UPDATE MARKS STRUCTURE WITH ICLABEL BRAIN COMPS...'); +for compStage = 1:7 + ICLabel_pass= zeros([1,length(rawLabels)]); + ICLabel_pass(find(rawLabels==compStage)) = 1; + ICLabel_pass = ICLabel_pass'; + EEG.marks=marks_add_label(EEG.marks,'comp_info', ... + {lookupTable(compStage).name,lookupTable(compStage).colour, ... + lookupTable(compStage).colour,1,ICLabel_pass}); +end +% make any brain components always on top: +EEG.marks.comp_info(1,find(strcmp({EEG.marks.comp_info.label},'brain'))).order=2; + + +%RESET/INITIATE CHAN MARKS... +manual_ind=find(strcmp('manual',{EEG.marks.chan_info.label})); + + +%RESET/INITIATE IC MARKS... +EEG.marks=marks_add_label(EEG.marks,'comp_info',{'ambig',[.3,.3,1],[.3,.3,1],3,zeros(min(size(EEG.icaweights)),1)}); +manual_ind=find(strcmp('manual',{EEG.marks.comp_info.label})); +ch_s_sd_ind=find(strcmp('ch_s_sd',{EEG.marks.chan_info.label})); +ch_sd_ind=find(strcmp('ch_sd',{EEG.marks.chan_info.label})); +low_r_ind=find(strcmp('low_r',{EEG.marks.chan_info.label})); +bridge_ind=find(strcmp('bridge',{EEG.marks.chan_info.label})); +rank_ind=find(strcmp('rank',{EEG.marks.chan_info.label})); +icrt_ind=find(strcmp('ic_rt',{EEG.marks.comp_info.label})); +brain_ind=find(strcmp('brain',{EEG.marks.comp_info.label})); +muscle_ind=find(strcmp('muscle',{EEG.marks.comp_info.label})); +eye_ind=find(strcmp('eye',{EEG.marks.comp_info.label})); +heart_ind=find(strcmp('heart',{EEG.marks.comp_info.label})); +line_noise_ind=find(strcmp('line_noise',{EEG.marks.comp_info.label})); +chan_noise_ind=find(strcmp('chan_noise',{EEG.marks.comp_info.label})); +other_ind=find(strcmp('other',{EEG.marks.comp_info.label})); +ambig_ind=find(strcmp('ambig',{EEG.marks.comp_info.label})); + +EEG.marks.comp_info(manual_ind).order=1; +if ~isempty(ch_s_sd_ind); % skip study-wide criteria if not used + EEG.marks.comp_info(ch_s_sd_ind).order=2; +end; +EEG.marks.comp_info(ch_sd_ind).order=2; +EEG.marks.comp_info(low_r_ind).order=2; +EEG.marks.comp_info(bridge_ind).order=2; +EEG.marks.comp_info(rank_ind).order=2; +EEG.marks.comp_info(icrt_ind).order=1; + +EEG.marks.comp_info(brain_ind).order=2; +EEG.marks.comp_info(muscle_ind).order=2; +EEG.marks.comp_info(eye_ind).order=2; +EEG.marks.comp_info(heart_ind).order=2; +EEG.marks.comp_info(line_noise_ind).order=2; +EEG.marks.comp_info(chan_noise_ind).order=2; +EEG.marks.comp_info(other_ind).order=2; +EEG.marks.comp_info(ambig_ind).order=3; + +bioa_inds = marks_label2index(EEG.marks.comp_info,{'muscle','eye','heart','chan_noise'},'indexes'); +EEG.marks.comp_info(manual_ind).flags=zeros(min(size(EEG.icaweights)),1); +EEG.marks.comp_info(manual_ind).flags(bioa_inds)=1; + +% BIDS Export calls +% Dot for path is on purpose. Don't worry - it's safe. +fName = './derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_eeg.edf'; +pop_writeeeg(EEG, fName, 'TYPE','EDF'); +dlmwrite(strrep(fName,'_eeg.edf','_icaweights.tsv'),EEG.icaweights,'\t'); +dlmwrite(strrep(fName,'_eeg.edf','_icasphere.tsv'),EEG.icasphere,'\t'); +s = {}; +s.algorithm = 'amica15'; +s.icachansind = EEG.icachansind; +s.intendedFor = fName; +savejson('',s,strrep(fName,'_eeg.edf','_icaweights.json')); +export_anno(EEG,fName,''); + +% ICLabel quick export +iclFname = strrep(fName,'_eeg.edf','_iclabel.mat'); +save(iclFname, 'tmp'); % see above ICLabel section for tmp definition diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/s05_concat_data_sub_005_task_faceFO_eeg.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/s05_concat_data_sub_005_task_faceFO_eeg.m new file mode 100644 index 000000000..463be8f79 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/s05_concat_data_sub_005_task_faceFO_eeg.m @@ -0,0 +1,678 @@ +warning off; + +pkg load signal; +pkg load parallel; +pkg load io; + +file_path = fileparts(mfilename('fullpath')); + +addpath([file_path '/../../code/dependencies/eeglab_lossless'], ... + [file_path '/../../code/dependencies/matlog'], ... + [file_path '/../../code/tools']); +cd '/scratch/tk11br/Face13/'; + +% eeglab auto loads the path correctly instead of shotgun blasting with +% addpath(genpath(<>)) as we were doing prior +if ~exist('EEG', 'var') + eeglab; +end +% Use matlog disp function override +logging_override_disp(true); +% Use matlog INFO level debugging +logging_set_log_level('INFO') + +% Don't run with traditional but enable some of the options +save_default_options ('-mat-binary'); +confirm_recursive_rmdir(false); + +% This makes | and & || and && in earlier version of Octave where +% | and & dont short curcuit by default +if exist('do_braindead_shortcircuit_evaluation', 'builtin') + do_braindead_shortcircuit_evaluation(true); +end + +% Options +pop_editoptions('option_savetwofiles', 1,'option_single',0); + +%% SCRIPT DESCRIPTION +% This script combineds the results of the amicas A,B and C. It takes this information and using ICAtest it determines which components were +% and were not replicated across and flags them, along with time sections with high standard deviation. Beta,theta and Alpha power is calculated +% arcoss time and is marked but not primed for removal. +% +%% From Config key_strswap Description +%----------------------------------------------------------------------------------------------------------------------------- +% in_path = derivatives/BIDS-Lossless-EEG Relative path to input data files assuming cd = work_path +% recur_sec = 1 Recurrence (sec) for the current artifact detection epoching (e.g. 1) +% limit_sec = [0 1] Limits (sec) for the current artifact detection epoching (e.g. [0 1]) +% sd_t_meth = q Method used for flagging epochs (e.g. 'q' (quantiles), or 'na' (default)) +% sd_t_vals = [.3 .7] Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7]) +% sd_t_o = 6 z threshold for flagging epochs during standard deviation criteria (e.g. 6) +% sd_t_f_meth = fixed Fixed method used for flagging epochs (should be 'fixed') +% sd_t_f_vals = Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% sd_t_f_o = .2 z threshold for flagging epochs during fixed standard deviation criteria (e.g. .2) +% min_gap_ms = 2000 Minimum time (ms) to allow between periods marked for rejection +% icr_crit = 0.05 False discovery rate of component replicatablilty, measure of sensitivity % +% dip_vol = derivatives/BIDS-Lossless-EEG/code/misc/standard_vol.mat file path/name of the standard volume file used in dipole fitting (e.g. derivatives/lossless/code/misc/standard_vol.mat). +% dip_mri = derivatives/BIDS-Lossless-EEG/code/misc/standard_mri.mat file path/name of the standard mri file used in dipole fitting (e.g. derivatives/lossless/code/misc/standard_mri.mat). +% dip_elc = derivatives/BIDS-Lossless-EEG/code/misc/standard_1020.elc file path/name of the standard electrode file used in dipole fitting (derivatives/lossless/code/misc/standard_1020.elc). +% dip_transmat = [0 0 0 0 0 0 1 1 1] Transformation matrix to apply during dipole fit (e.g. [0 0 0 0 0 0 1 1 1]) +% cor_transmat = [0,0,0,0,0,1.57,1,1,1] The transformation matrix to apply between dipole fitting to the standard head and performing ICLabel to the EEGLAB default orientation (e.g.[0,0,0,0,0,1.57,1,1,1]) +% cor_nosedir = +X Nose direction of montage to apply between dipole fitting to the standard head and performing ICLabel to the EEGLAB default orientation (e.g. +Y) +% out_path = derivatives/BIDS-Lossless-EEG Relative path to output data files assuming cd = work_path + +%% LOAD COMPART DATASET +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Loads the data file and the first amica model. + +logging_log('INFO', 'Loading set file: ./sub-005/eeg/sub-005_task-faceFO_compart_data.set...'); +tic; +EEG = pop_loadset('filepath','derivatives/BIDS-Lossless-EEG','filename','./sub-005/eeg/sub-005_task-faceFO_compart_data.set'); +EEG = eeg_checkset( EEG ); +logging_log('INFO', 'TIME TO: LOAD DATASET...'); +toc + +% Set up the ICA channels +EEG.icachansind = marks_label2index(EEG.marks.chan_info,{'manual','rank'},'indices','invert','on'); + +% Load Amica A +logging_log('INFO', 'Loading Amica ABC...'); +tic; +EEG.amica(2).models=loadmodout15('derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_amicaout_A'); +EEG.icaweights=EEG.amica(2).models.W; +EEG.icasphere=EEG.amica(2).models.S(1:EEG.amica(2).models.num_pcs,:); + +%% PASS EEG DATA THROUGH THE AMICA MATRIX +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Loads the B and C amica models. Amica A is used to fill in the ICA information. + +% Instead of EEG=eeg_checkset(EEG); +tmpdata = EEG.data(EEG.icachansind,:); +tmpindices = find(~sum(isnan(tmpdata))); +EEG.icaact = zeros(size(EEG.icaweights,1), size(tmpdata,2)); EEG.icaact(:) = NaN; +EEG.icaact(:,tmpindices) = (EEG.icaweights*EEG.icasphere)*tmpdata(:,tmpindices); +clear tmpdata; + +% Create winv for topo plots manually +EEG.icawinv = []; +EEG.icawinv = pinv(EEG.icaweights * EEG.icasphere); + +% Load Amica B +EEG.amica(3).models=loadmodout15('derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_amicaout_B'); +% Load Amica C +EEG.amica(4).models=loadmodout15('derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_amicaout_C'); +logging_log('INFO', 'TIME TO: LOAD ICA ABC INFO...'); +toc + +%% UPDATE REJECTION STRUCTURE... +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Creating flag marks to based on the log liklyhodd models found for each of the ICA models + +% Flags for amicas A B and C +logging_log('INFO', 'Updating latflaginfo structure...'); +tic; + +% A +logl_A_flags=zeros(size(EEG.data(1,:))); +logl_A_flag_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indices','invert','on'); + +% Check to see if the overwrite will be successful. If the overwirte is a different size then some of the old data will be retained. +if length(EEG.amica(2).models.Lt) ~= length(logl_A_flag_inds) + EEG.amica(2).models.Lt = EEG.amica(2).models.Lt(1:length(logl_A_flag_inds)); + warning('YOU FORGOT TO DELETE YOUR OLD AMICA FILES AND THE NEW CHANNEL SIZES DO NOT MATCH UP'); +end + +logl_A_flags(logl_A_flag_inds)=(EEG.amica(2).models.Lt-min(EEG.amica(2).models.Lt))/(max(EEG.amica(2).models.Lt)-min(EEG.amica(2).models.Lt)); +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'logl_A',[1,0.45,0],logl_A_flags}); +clear logl_A_flags; +% B +logl_B_flags=zeros(size(EEG.data(1,:))); +logl_B_flag_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indices','invert','on'); +logl_B_flags(logl_B_flag_inds)=(EEG.amica(3).models.Lt-min(EEG.amica(3).models.Lt))/(max(EEG.amica(3).models.Lt)-min(EEG.amica(3).models.Lt)); +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'logl_B',[1,0.45,0],logl_B_flags}); +clear logl_B_flags; +% C +logl_C_flags=zeros(size(EEG.data(1,:))); +logl_C_flag_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indices','invert','on'); +logl_C_flags(logl_C_flag_inds)=(EEG.amica(4).models.Lt-min(EEG.amica(4).models.Lt))/(max(EEG.amica(4).models.Lt)-min(EEG.amica(4).models.Lt)); +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'logl_C',[1,0.45,0],logl_C_flags}); +clear logl_C_flags; + +logging_log('INFO', 'TIME TO: UPDATE REJECTION STRUCTURE...'); +toc + +%% IDENTIFY RELIABLE COMPONENTS USING ISCTEST +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Gives the A variable of each of the amicas to the function icatest. Returned are the indecies of the +% Components that were not replicated across all 3 of the models. + +tic; +% Clear a in case of local models are saved already +a = []; +for i=2:length(EEG.amica); + a(:,:,i-1)=EEG.amica(i).models(1).A; +end + +% Calculating the replicated component flag +% Replicate criteria changed from 0.05,0.05 to 0.1,0.1 to be less sensitive +ca=isctest(a,0.05,0.05,'mixing'); +if isempty(ca) + mess = ['ISCTest failed to return reasonable values. ISCTest is ' ... + 'meant to find duplicate channels in amica_{A,B,C} if this ' ... + 'failed consider investigating the datafile']; + logging_log('ERROR', mess); + error(mess); +end +icr_inds=setdiff(1:min(size(EEG.amica(2).models(1).A)),ca(:,1)); + +% Creating the replicated component flag +logging_log('INFO', 'Updating chflaginfo structure...'); +icr_comp_flags=zeros(min(size(EEG.amica(2).models(1).A)),1); +icr_comp_flags(icr_inds)=1; +EEG.marks=marks_add_label(EEG.marks,'comp_info', ... + {'ic_rt',[0,0,1],[0,0,1],-1,icr_comp_flags}); +logging_log('INFO', 'TIME TO: EDIT FIND REPLICATED COMPONENTS AND UPDATE COMPINFO STRUCT...'); +toc + +% Window the continuous data +logging_log('INFO', 'Windowing the continous data...'); +tic; +EEG=marks_continuous2epochs(EEG,'recurrence',1,'limits',[0 1]); +logging_log('INFO', 'TIME TO: WINDOW THE CONTINUOUS DATA...'); +toc + +%% CALCULATE ICAACT SD +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Similarly to the IC_SD flag created in compart. Marks sections of time where the components +% have an abnormally high standard deviation. + +% Calculating the replicated component flag +logging_log('INFO', 'Calculating the IC sd array for window criteria...'); +tic; +epoch_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indexes','invert','on'); +[EEG,icaact_sd2_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'varmeasure','absmean', ... + 'detrend','off', ... + 'plot_figs','off'); + +% Creating the ic_sd component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_sd_t_inds]=marks_array2flags(icaact_sd2_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); + +icsd_epoch_flags=zeros(size(EEG.data(1,:,:))); +icsd_epoch_flags(1,:,epoch_inds(flag_sd_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_sd2',[.4,.6,.6],icsd_epoch_flags}); +logging_log('INFO', 'TIME TO: EDIT FIND IC_SD AND UPDATE TIMEINFO REJECTION STRUCT...'); +toc +clear icsd_epoch_flags; + +%% CALCULATE DELTA/THETA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high Theta/Alpha Power + +% Calculating the Delta/Theta component flag +logging_log('INFO', 'Calculating the delta/theta IC power array for window criteria...'); +tic; +[EEG,icaact_dt_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[0 7], ... + 'plot_figs','off'); + +% Creating the Delta/Theta component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_dt_t_inds]=marks_array2flags(icaact_dt_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +icdt_epoch_flags=zeros(size(EEG.data(1,:,:))); +icdt_epoch_flags(1,:,epoch_inds(flag_dt_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_dt',[1,1,0],icdt_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE DELTA/THETA ICAACT POWER AND UPDATE MARKS...'); +toc +clear icdt_epoch_flags; + +%% CALCULATE ALPHA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high Alpha Power + +% Calculating the Alpha component flag +logging_log('INFO', 'Calculating the alpha IC power array for window criteria...'); +tic; +[EEG,icaact_a_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[8 13], ... + 'plot_figs','off'); + +% Creating the Alpha component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_a_t_inds]=marks_array2flags(icaact_a_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +ica_epoch_flags=zeros(size(EEG.data(1,:,:))); +ica_epoch_flags(1,:,epoch_inds(flag_a_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_a',[1,1,0],ica_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE ALPHA ICAACT POWER AND UPDATE MARKS...'); +toc +clear ica_epoch_flags; + +%% CALCULATE BETA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high Beta Power. +% Note that at the moment this mark is used for removals. + +% Calculating the Beta component flag +logging_log('INFO', 'Calculating the beta IC power array for window criteria...'); +tic; +[EEG,icaact_b_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[14 30], ... + 'plot_figs','off'); +logging_log('INFO', 'TIME TO: CALCULATE BETA ICAACT POWER...'); + +% Creating the Beta component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_b_t_inds]=marks_array2flags(icaact_b_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +logging_log('INFO', 'TIME TO: CREATE WINDOW CRITERIA VECTOR...'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +icb_epoch_flags=zeros(size(EEG.data(1,:,:))); +icb_epoch_flags(1,:,epoch_inds(flag_b_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_b',[0,1,1],icb_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE BETA ICAACT POWER AND UPDATE MARKS...'); +toc +clear icb_epoch_flags; + +%% CALCULATE LOW GAMMA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high Low-Gamma Power. +% Note that at the moment this mark is used for removals. + +% Calculating the Beta component flag +logging_log('INFO', 'Calculating the low-gamma IC power array for window criteria...'); +tic; +[EEG,icaact_lg_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[31 60], ... + 'plot_figs','off'); +logging_log('INFO', 'TIME TO: CALCULATE LOW-GAMMA ICAACT POWER...'); + +% Creating the LOW-GAMMA component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_lg_t_inds]=marks_array2flags(icaact_lg_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +logging_log('INFO', 'TIME TO: CREATE WINDOW CRITERIA VECTOR...'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +iclg_epoch_flags=zeros(size(EEG.data(1,:,:))); +iclg_epoch_flags(1,:,epoch_inds(flag_lg_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_lg',[0,1,1],iclg_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE LOW-GAMMA ICAACT POWER AND UPDATE MARKS...'); +toc +clear iclg_epoch_flags; + + +%% CALCULATE HIGH GAMMA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high High-Gamma Power. +% Note that at the moment this mark is used for removals. + +% Calculating the Beta component flag +logging_log('INFO', 'Calculating the high-gamma IC power array for window criteria...'); +tic; +[EEG,icaact_hg_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[61 100], ... + 'plot_figs','off'); +logging_log('INFO', 'TIME TO: CALCULATE HIGH-GAMMA ICAACT POWER...'); + +% Creating the HIGH-GAMMA component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_hg_t_inds]=marks_array2flags(icaact_hg_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +logging_log('INFO', 'TIME TO: CREATE WINDOW CRITERIA VECTOR...'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +ichg_epoch_flags=zeros(size(EEG.data(1,:,:))); +ichg_epoch_flags(1,:,epoch_inds(flag_hg_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_hg',[0,1,1],ichg_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE HIGH-GAMMA ICAACT POWER AND UPDATE MARKS...'); +toc +clear ichg_epoch_flags; + +%% MARKS UPDATE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Merges the epochs back to continuous data. Flags marks gaps and combines time +% the new time marks into the manual marks. + +% Concat epochs back into continuous data +logging_log('INFO', 'Concatenating windowed data...'); +tic; +EEG=marks_epochs2continuous(EEG); +EEG=eeg_checkset(EEG,'eventconsistency'); +logging_log('INFO', 'TIME TO: CONCATENATE THE WINDOWED DATA INTO CONTINUOUS DATA...'); +toc + +% Mark flag gaps between time marks +tic; +EEG=pop_marks_flag_gap(EEG,{'manual','mark_gap','ic_sd2'}, ... + 2000,'mark_gap',[.8,.8,.8],'offsets',[0 0],'ref_point','both'); +logging_log('INFO', 'TIME TO: MARK FLAG GAPS THAT ARE < 2 SECONDS...'); +toc + +% Combine the removal marks into manual +tic; +EEG=pop_marks_merge_labels(EEG,'time_info',{'manual','ic_sd2','mark_gap'},'target_label','manual'); +logging_log('INFO', 'TIME TO: COMBINE MARKS STRUCTURE INTO MANUAL FLAGS...'); +toc + +% begin prep for dipole fit +logging_log('INFO','Checking for JVM...'); +javachk('jvm'); + +% TODO(mae) this isn't added for some reason +addpath([pwd '/derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/Fieldtrip-lite141209/src']) + +%% DIPFIT +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Does the dipole fitting procedures. This takes a substantial amount of +% time. +% TODO: Make sure this is all needed, seems to be repeating things +logging_log('INFO','FITTING DIPOLES...'); +tic; +EEG = pop_dipfit_settings( EEG, 'hdmfile','derivatives/BIDS-Lossless-EEG/code/misc/standard_vol.mat', ... + 'coordformat','MNI', ... + 'mrifile','derivatives/BIDS-Lossless-EEG/code/misc/standard_mri.mat', ... + 'chanfile','derivatives/BIDS-Lossless-EEG/code/misc/standard_1020.elc', ... + 'coord_transform',[0 0 0 0 0 0 1 1 1], ... + 'chansel',[1:EEG.nbchan] ); + +% OLD SINGLE THREADED SETUP +% EEG = pop_multifit(EEG, [1:EEG.nbchan] ,'threshold',100,'plotopt',{'normlen' 'on'}); + +% NEW MULTI-THREADED SETUP +EEG = pop_multifit_bucanl_parallel(EEG, [1:EEG.nbchan], 8,'threshold',100,'plotopt',{'normlen' 'on'}); + +EEG=eeg_checkset(EEG); +logging_log('INFO','TIME TO: FIT DIPOLES...'); +toc + +%% MARK ICs FOR REJECTION BASED ON RV +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Creates marks based on dipfit results. Dipfit marks are added +% for rejection while ICMARC are just for reference. +% IC RESIDUAL VARIANCE FLAG +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Residuals greater than 15% are flagged - can be done here as it only references +% dipoles and their values. +%logging_log('INFO','UPDATE MARKS STRUCTURE WITH IC_RV...'); +%tic; +% Dipole criteria changed from 0.15 to 0.2 to be less sensitive +%EEG.reject.gcompreject(find([EEG.dipfit.model.rv]>.15))=1; +%EEG.reject.gcompreject(find(isnan([EEG.dipfit.model.rv])))=1; +%EEG.marks=marks_add_label(EEG.marks,'comp_info', ... +% {'ic_rv',[1,0,0],[1,0,0],-1,EEG.reject.gcompreject'}); +%logging_log('INFO','TIME TO: UPDATE MARKS STRUCTURE...'); +%toc + +% COMBINE MARKS STRUCTURE INTO MANUAL FLAGS... +%tic; +%EEG=pop_marks_merge_labels(EEG,'comp_info',{'manual','ic_rv'},'target_label','manual'); +%logging_log('INFO','TIME TO: COMBINE MARKS STRUCTURE INTO MANUAL FLAGS...'); +%toc + +% Adjust montage +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% This will pretty much always be dataset specific - so be careful +EEG = warp_locs( EEG, 'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020.elc', ... + 'transform',[0,0,0,0,0,1.57,1,1,1], ... + 'manual','off'); +EEG.chaninfo.nosedir='+X'; +EEG=eeg_checkset(EEG); + +%% SAVE LOSSLESS DERIVATIVE *_eeg_ll.set FILE (same name as the input file from the +% BIDS root directory [but located in the derivatives/lossless directory]). +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +logging_log('INFO','Saving set file: derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_eeg_ll.set...'); +tic; +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_eeg_ll.set'); +logging_log('INFO','TIME TO: SAVE DIP DATA SET...'); +toc + +% Save diagnostic arrays +try OCTAVE_VERSION; + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_icaact_sd2_t.mat','icaact_sd2_t'); +catch + save('derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_icaact_sd2_t.mat','icaact_sd2_t'); +end + +logging_log('INFO', 'Scheduler: sbatch'); +print_chan_sample(EEG); + + +if ~find(strcmpi({EEG.marks.chan_info.label},'ch_s_sd')); + EEG.marks=marks_add_label(EEG.marks,'chan_info',{'ch_s_sd',[1,0,0],[]}); +end; + +EEG = eeg_checkset(EEG); + +% CHECK FOR IC CLASSIFICATION MARKS +%% Use the ICLabel plugin +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% ICLabel is a database style plugin that classifies ICA components into the +% following seven categories + +% (Brain)(Muscle)(Eye)(Heart)(Line Noise)(Channel Noise)(Other) +% ( 1 )( 2 )( 3 )( 4 )( 5 )( 6 )( 7 ) + +% Colour structure and groupings are as follows: +% ---------- +% Brain -> [0, 0.4, 0] +% ---------- +% Muscle -> [0.4, 0, 0] +% Eye -> [0.8, 0, 0] +% Heart -> [0.8, 0.4, 0] +% ---------- +% Line Noise -> [0.2, 0.4, 0.8] +% Channel noise -> [0.4, 0, 0.8] +% ---------- +% Other -> [0.8, 0.8, 0] +% ---------- + +% Create lookup table of the above information. +pairs = {'brain',[0, 0.4, 0]; +'muscle',[0.4,0,0]; +'eye',[0.8,0,0]; +'heart',[0.8,0.4,0]; +'line_noise',[0.2,0.4,0.8]; +'chan_noise',[0.4,0,0.8]; +'other',[.7 .4 0]}; +lookupTable = cell2struct(pairs, {'name', 'colour'}, 2); + +EEG = pop_marks_select_data(EEG,'time marks',[],'labels',{'manual'},'remove','on'); + +% Run ICLabel +%EEG = iclabel(EEG); + +% extract features +disp 'ICLabel: extracting features...' +features = ICL_feature_extractor(EEG, 40); + +% run ICL +disp 'ICLabel: calculating labels...' +labels = run_ICL('default',features{:}); + +% save into EEG +disp 'ICLabel: saving results...' +EEG.etc.ic_classification.ICLabel.classes = ... + {'Brain', 'Muscle', 'Eye', 'Heart', 'Line', 'Channel', 'Other'}; +EEG.etc.ic_classification.ICLabel.classifications = labels; + +tmp=EEG.etc.ic_classification; + +% Reload dataset: +EEG = pop_loadset('filepath','derivatives/BIDS-Lossless-EEG','filename','./sub-005/eeg/sub-005_task-faceFO_eeg_ll.set'); +EEG = eeg_checkset(EEG); + +EEG.etc.ic_classification=tmp; + +% Extract labels from the structure. +rawLabels = zeros([1,length(EEG.etc.ic_classification.ICLabel.classifications)]); +for index = 1:length(EEG.etc.ic_classification.ICLabel.classifications) + [p,i] = max(EEG.etc.ic_classification.ICLabel.classifications(index,1:end-1)); + if p<.3; + i=7; + end + rawLabels(index) = i; +end + +%ICLabel Exporting to marks +%%%%%%%%%%%%%%%%%%%%%%%%%%%% +logging_log('INFO','UPDATE MARKS STRUCTURE WITH ICLABEL BRAIN COMPS...'); +for compStage = 1:7 + ICLabel_pass= zeros([1,length(rawLabels)]); + ICLabel_pass(find(rawLabels==compStage)) = 1; + ICLabel_pass = ICLabel_pass'; + EEG.marks=marks_add_label(EEG.marks,'comp_info', ... + {lookupTable(compStage).name,lookupTable(compStage).colour, ... + lookupTable(compStage).colour,1,ICLabel_pass}); +end +% make any brain components always on top: +EEG.marks.comp_info(1,find(strcmp({EEG.marks.comp_info.label},'brain'))).order=2; + + +%RESET/INITIATE CHAN MARKS... +manual_ind=find(strcmp('manual',{EEG.marks.chan_info.label})); + + +%RESET/INITIATE IC MARKS... +EEG.marks=marks_add_label(EEG.marks,'comp_info',{'ambig',[.3,.3,1],[.3,.3,1],3,zeros(min(size(EEG.icaweights)),1)}); +manual_ind=find(strcmp('manual',{EEG.marks.comp_info.label})); +ch_s_sd_ind=find(strcmp('ch_s_sd',{EEG.marks.chan_info.label})); +ch_sd_ind=find(strcmp('ch_sd',{EEG.marks.chan_info.label})); +low_r_ind=find(strcmp('low_r',{EEG.marks.chan_info.label})); +bridge_ind=find(strcmp('bridge',{EEG.marks.chan_info.label})); +rank_ind=find(strcmp('rank',{EEG.marks.chan_info.label})); +icrt_ind=find(strcmp('ic_rt',{EEG.marks.comp_info.label})); +brain_ind=find(strcmp('brain',{EEG.marks.comp_info.label})); +muscle_ind=find(strcmp('muscle',{EEG.marks.comp_info.label})); +eye_ind=find(strcmp('eye',{EEG.marks.comp_info.label})); +heart_ind=find(strcmp('heart',{EEG.marks.comp_info.label})); +line_noise_ind=find(strcmp('line_noise',{EEG.marks.comp_info.label})); +chan_noise_ind=find(strcmp('chan_noise',{EEG.marks.comp_info.label})); +other_ind=find(strcmp('other',{EEG.marks.comp_info.label})); +ambig_ind=find(strcmp('ambig',{EEG.marks.comp_info.label})); + +EEG.marks.comp_info(manual_ind).order=1; +if ~isempty(ch_s_sd_ind); % skip study-wide criteria if not used + EEG.marks.comp_info(ch_s_sd_ind).order=2; +end; +EEG.marks.comp_info(ch_sd_ind).order=2; +EEG.marks.comp_info(low_r_ind).order=2; +EEG.marks.comp_info(bridge_ind).order=2; +EEG.marks.comp_info(rank_ind).order=2; +EEG.marks.comp_info(icrt_ind).order=1; + +EEG.marks.comp_info(brain_ind).order=2; +EEG.marks.comp_info(muscle_ind).order=2; +EEG.marks.comp_info(eye_ind).order=2; +EEG.marks.comp_info(heart_ind).order=2; +EEG.marks.comp_info(line_noise_ind).order=2; +EEG.marks.comp_info(chan_noise_ind).order=2; +EEG.marks.comp_info(other_ind).order=2; +EEG.marks.comp_info(ambig_ind).order=3; + +bioa_inds = marks_label2index(EEG.marks.comp_info,{'muscle','eye','heart','chan_noise'},'indexes'); +EEG.marks.comp_info(manual_ind).flags=zeros(min(size(EEG.icaweights)),1); +EEG.marks.comp_info(manual_ind).flags(bioa_inds)=1; + +% BIDS Export calls +% Dot for path is on purpose. Don't worry - it's safe. +fName = './derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_eeg.edf'; +pop_writeeeg(EEG, fName, 'TYPE','EDF'); +dlmwrite(strrep(fName,'_eeg.edf','_icaweights.tsv'),EEG.icaweights,'\t'); +dlmwrite(strrep(fName,'_eeg.edf','_icasphere.tsv'),EEG.icasphere,'\t'); +s = {}; +s.algorithm = 'amica15'; +s.icachansind = EEG.icachansind; +s.intendedFor = fName; +savejson('',s,strrep(fName,'_eeg.edf','_icaweights.json')); +export_anno(EEG,fName,''); + +% ICLabel quick export +iclFname = strrep(fName,'_eeg.edf','_iclabel.mat'); +save(iclFname, 'tmp'); % see above ICLabel section for tmp definition diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/s05_concat_data_sub_006_task_faceFO_eeg.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/s05_concat_data_sub_006_task_faceFO_eeg.m new file mode 100644 index 000000000..7e1751ea5 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/s05_concat_data_sub_006_task_faceFO_eeg.m @@ -0,0 +1,678 @@ +warning off; + +pkg load signal; +pkg load parallel; +pkg load io; + +file_path = fileparts(mfilename('fullpath')); + +addpath([file_path '/../../code/dependencies/eeglab_lossless'], ... + [file_path '/../../code/dependencies/matlog'], ... + [file_path '/../../code/tools']); +cd '/scratch/tk11br/Face13/'; + +% eeglab auto loads the path correctly instead of shotgun blasting with +% addpath(genpath(<>)) as we were doing prior +if ~exist('EEG', 'var') + eeglab; +end +% Use matlog disp function override +logging_override_disp(true); +% Use matlog INFO level debugging +logging_set_log_level('INFO') + +% Don't run with traditional but enable some of the options +save_default_options ('-mat-binary'); +confirm_recursive_rmdir(false); + +% This makes | and & || and && in earlier version of Octave where +% | and & dont short curcuit by default +if exist('do_braindead_shortcircuit_evaluation', 'builtin') + do_braindead_shortcircuit_evaluation(true); +end + +% Options +pop_editoptions('option_savetwofiles', 1,'option_single',0); + +%% SCRIPT DESCRIPTION +% This script combineds the results of the amicas A,B and C. It takes this information and using ICAtest it determines which components were +% and were not replicated across and flags them, along with time sections with high standard deviation. Beta,theta and Alpha power is calculated +% arcoss time and is marked but not primed for removal. +% +%% From Config key_strswap Description +%----------------------------------------------------------------------------------------------------------------------------- +% in_path = derivatives/BIDS-Lossless-EEG Relative path to input data files assuming cd = work_path +% recur_sec = 1 Recurrence (sec) for the current artifact detection epoching (e.g. 1) +% limit_sec = [0 1] Limits (sec) for the current artifact detection epoching (e.g. [0 1]) +% sd_t_meth = q Method used for flagging epochs (e.g. 'q' (quantiles), or 'na' (default)) +% sd_t_vals = [.3 .7] Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7]) +% sd_t_o = 6 z threshold for flagging epochs during standard deviation criteria (e.g. 6) +% sd_t_f_meth = fixed Fixed method used for flagging epochs (should be 'fixed') +% sd_t_f_vals = Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% sd_t_f_o = .2 z threshold for flagging epochs during fixed standard deviation criteria (e.g. .2) +% min_gap_ms = 2000 Minimum time (ms) to allow between periods marked for rejection +% icr_crit = 0.05 False discovery rate of component replicatablilty, measure of sensitivity % +% dip_vol = derivatives/BIDS-Lossless-EEG/code/misc/standard_vol.mat file path/name of the standard volume file used in dipole fitting (e.g. derivatives/lossless/code/misc/standard_vol.mat). +% dip_mri = derivatives/BIDS-Lossless-EEG/code/misc/standard_mri.mat file path/name of the standard mri file used in dipole fitting (e.g. derivatives/lossless/code/misc/standard_mri.mat). +% dip_elc = derivatives/BIDS-Lossless-EEG/code/misc/standard_1020.elc file path/name of the standard electrode file used in dipole fitting (derivatives/lossless/code/misc/standard_1020.elc). +% dip_transmat = [0 0 0 0 0 0 1 1 1] Transformation matrix to apply during dipole fit (e.g. [0 0 0 0 0 0 1 1 1]) +% cor_transmat = [0,0,0,0,0,1.57,1,1,1] The transformation matrix to apply between dipole fitting to the standard head and performing ICLabel to the EEGLAB default orientation (e.g.[0,0,0,0,0,1.57,1,1,1]) +% cor_nosedir = +X Nose direction of montage to apply between dipole fitting to the standard head and performing ICLabel to the EEGLAB default orientation (e.g. +Y) +% out_path = derivatives/BIDS-Lossless-EEG Relative path to output data files assuming cd = work_path + +%% LOAD COMPART DATASET +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Loads the data file and the first amica model. + +logging_log('INFO', 'Loading set file: ./sub-006/eeg/sub-006_task-faceFO_compart_data.set...'); +tic; +EEG = pop_loadset('filepath','derivatives/BIDS-Lossless-EEG','filename','./sub-006/eeg/sub-006_task-faceFO_compart_data.set'); +EEG = eeg_checkset( EEG ); +logging_log('INFO', 'TIME TO: LOAD DATASET...'); +toc + +% Set up the ICA channels +EEG.icachansind = marks_label2index(EEG.marks.chan_info,{'manual','rank'},'indices','invert','on'); + +% Load Amica A +logging_log('INFO', 'Loading Amica ABC...'); +tic; +EEG.amica(2).models=loadmodout15('derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_amicaout_A'); +EEG.icaweights=EEG.amica(2).models.W; +EEG.icasphere=EEG.amica(2).models.S(1:EEG.amica(2).models.num_pcs,:); + +%% PASS EEG DATA THROUGH THE AMICA MATRIX +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Loads the B and C amica models. Amica A is used to fill in the ICA information. + +% Instead of EEG=eeg_checkset(EEG); +tmpdata = EEG.data(EEG.icachansind,:); +tmpindices = find(~sum(isnan(tmpdata))); +EEG.icaact = zeros(size(EEG.icaweights,1), size(tmpdata,2)); EEG.icaact(:) = NaN; +EEG.icaact(:,tmpindices) = (EEG.icaweights*EEG.icasphere)*tmpdata(:,tmpindices); +clear tmpdata; + +% Create winv for topo plots manually +EEG.icawinv = []; +EEG.icawinv = pinv(EEG.icaweights * EEG.icasphere); + +% Load Amica B +EEG.amica(3).models=loadmodout15('derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_amicaout_B'); +% Load Amica C +EEG.amica(4).models=loadmodout15('derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_amicaout_C'); +logging_log('INFO', 'TIME TO: LOAD ICA ABC INFO...'); +toc + +%% UPDATE REJECTION STRUCTURE... +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Creating flag marks to based on the log liklyhodd models found for each of the ICA models + +% Flags for amicas A B and C +logging_log('INFO', 'Updating latflaginfo structure...'); +tic; + +% A +logl_A_flags=zeros(size(EEG.data(1,:))); +logl_A_flag_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indices','invert','on'); + +% Check to see if the overwrite will be successful. If the overwirte is a different size then some of the old data will be retained. +if length(EEG.amica(2).models.Lt) ~= length(logl_A_flag_inds) + EEG.amica(2).models.Lt = EEG.amica(2).models.Lt(1:length(logl_A_flag_inds)); + warning('YOU FORGOT TO DELETE YOUR OLD AMICA FILES AND THE NEW CHANNEL SIZES DO NOT MATCH UP'); +end + +logl_A_flags(logl_A_flag_inds)=(EEG.amica(2).models.Lt-min(EEG.amica(2).models.Lt))/(max(EEG.amica(2).models.Lt)-min(EEG.amica(2).models.Lt)); +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'logl_A',[1,0.45,0],logl_A_flags}); +clear logl_A_flags; +% B +logl_B_flags=zeros(size(EEG.data(1,:))); +logl_B_flag_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indices','invert','on'); +logl_B_flags(logl_B_flag_inds)=(EEG.amica(3).models.Lt-min(EEG.amica(3).models.Lt))/(max(EEG.amica(3).models.Lt)-min(EEG.amica(3).models.Lt)); +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'logl_B',[1,0.45,0],logl_B_flags}); +clear logl_B_flags; +% C +logl_C_flags=zeros(size(EEG.data(1,:))); +logl_C_flag_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indices','invert','on'); +logl_C_flags(logl_C_flag_inds)=(EEG.amica(4).models.Lt-min(EEG.amica(4).models.Lt))/(max(EEG.amica(4).models.Lt)-min(EEG.amica(4).models.Lt)); +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'logl_C',[1,0.45,0],logl_C_flags}); +clear logl_C_flags; + +logging_log('INFO', 'TIME TO: UPDATE REJECTION STRUCTURE...'); +toc + +%% IDENTIFY RELIABLE COMPONENTS USING ISCTEST +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Gives the A variable of each of the amicas to the function icatest. Returned are the indecies of the +% Components that were not replicated across all 3 of the models. + +tic; +% Clear a in case of local models are saved already +a = []; +for i=2:length(EEG.amica); + a(:,:,i-1)=EEG.amica(i).models(1).A; +end + +% Calculating the replicated component flag +% Replicate criteria changed from 0.05,0.05 to 0.1,0.1 to be less sensitive +ca=isctest(a,0.05,0.05,'mixing'); +if isempty(ca) + mess = ['ISCTest failed to return reasonable values. ISCTest is ' ... + 'meant to find duplicate channels in amica_{A,B,C} if this ' ... + 'failed consider investigating the datafile']; + logging_log('ERROR', mess); + error(mess); +end +icr_inds=setdiff(1:min(size(EEG.amica(2).models(1).A)),ca(:,1)); + +% Creating the replicated component flag +logging_log('INFO', 'Updating chflaginfo structure...'); +icr_comp_flags=zeros(min(size(EEG.amica(2).models(1).A)),1); +icr_comp_flags(icr_inds)=1; +EEG.marks=marks_add_label(EEG.marks,'comp_info', ... + {'ic_rt',[0,0,1],[0,0,1],-1,icr_comp_flags}); +logging_log('INFO', 'TIME TO: EDIT FIND REPLICATED COMPONENTS AND UPDATE COMPINFO STRUCT...'); +toc + +% Window the continuous data +logging_log('INFO', 'Windowing the continous data...'); +tic; +EEG=marks_continuous2epochs(EEG,'recurrence',1,'limits',[0 1]); +logging_log('INFO', 'TIME TO: WINDOW THE CONTINUOUS DATA...'); +toc + +%% CALCULATE ICAACT SD +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Similarly to the IC_SD flag created in compart. Marks sections of time where the components +% have an abnormally high standard deviation. + +% Calculating the replicated component flag +logging_log('INFO', 'Calculating the IC sd array for window criteria...'); +tic; +epoch_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indexes','invert','on'); +[EEG,icaact_sd2_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'varmeasure','absmean', ... + 'detrend','off', ... + 'plot_figs','off'); + +% Creating the ic_sd component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_sd_t_inds]=marks_array2flags(icaact_sd2_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); + +icsd_epoch_flags=zeros(size(EEG.data(1,:,:))); +icsd_epoch_flags(1,:,epoch_inds(flag_sd_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_sd2',[.4,.6,.6],icsd_epoch_flags}); +logging_log('INFO', 'TIME TO: EDIT FIND IC_SD AND UPDATE TIMEINFO REJECTION STRUCT...'); +toc +clear icsd_epoch_flags; + +%% CALCULATE DELTA/THETA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high Theta/Alpha Power + +% Calculating the Delta/Theta component flag +logging_log('INFO', 'Calculating the delta/theta IC power array for window criteria...'); +tic; +[EEG,icaact_dt_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[0 7], ... + 'plot_figs','off'); + +% Creating the Delta/Theta component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_dt_t_inds]=marks_array2flags(icaact_dt_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +icdt_epoch_flags=zeros(size(EEG.data(1,:,:))); +icdt_epoch_flags(1,:,epoch_inds(flag_dt_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_dt',[1,1,0],icdt_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE DELTA/THETA ICAACT POWER AND UPDATE MARKS...'); +toc +clear icdt_epoch_flags; + +%% CALCULATE ALPHA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high Alpha Power + +% Calculating the Alpha component flag +logging_log('INFO', 'Calculating the alpha IC power array for window criteria...'); +tic; +[EEG,icaact_a_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[8 13], ... + 'plot_figs','off'); + +% Creating the Alpha component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_a_t_inds]=marks_array2flags(icaact_a_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +ica_epoch_flags=zeros(size(EEG.data(1,:,:))); +ica_epoch_flags(1,:,epoch_inds(flag_a_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_a',[1,1,0],ica_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE ALPHA ICAACT POWER AND UPDATE MARKS...'); +toc +clear ica_epoch_flags; + +%% CALCULATE BETA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high Beta Power. +% Note that at the moment this mark is used for removals. + +% Calculating the Beta component flag +logging_log('INFO', 'Calculating the beta IC power array for window criteria...'); +tic; +[EEG,icaact_b_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[14 30], ... + 'plot_figs','off'); +logging_log('INFO', 'TIME TO: CALCULATE BETA ICAACT POWER...'); + +% Creating the Beta component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_b_t_inds]=marks_array2flags(icaact_b_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +logging_log('INFO', 'TIME TO: CREATE WINDOW CRITERIA VECTOR...'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +icb_epoch_flags=zeros(size(EEG.data(1,:,:))); +icb_epoch_flags(1,:,epoch_inds(flag_b_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_b',[0,1,1],icb_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE BETA ICAACT POWER AND UPDATE MARKS...'); +toc +clear icb_epoch_flags; + +%% CALCULATE LOW GAMMA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high Low-Gamma Power. +% Note that at the moment this mark is used for removals. + +% Calculating the Beta component flag +logging_log('INFO', 'Calculating the low-gamma IC power array for window criteria...'); +tic; +[EEG,icaact_lg_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[31 60], ... + 'plot_figs','off'); +logging_log('INFO', 'TIME TO: CALCULATE LOW-GAMMA ICAACT POWER...'); + +% Creating the LOW-GAMMA component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_lg_t_inds]=marks_array2flags(icaact_lg_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +logging_log('INFO', 'TIME TO: CREATE WINDOW CRITERIA VECTOR...'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +iclg_epoch_flags=zeros(size(EEG.data(1,:,:))); +iclg_epoch_flags(1,:,epoch_inds(flag_lg_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_lg',[0,1,1],iclg_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE LOW-GAMMA ICAACT POWER AND UPDATE MARKS...'); +toc +clear iclg_epoch_flags; + + +%% CALCULATE HIGH GAMMA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high High-Gamma Power. +% Note that at the moment this mark is used for removals. + +% Calculating the Beta component flag +logging_log('INFO', 'Calculating the high-gamma IC power array for window criteria...'); +tic; +[EEG,icaact_hg_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[61 100], ... + 'plot_figs','off'); +logging_log('INFO', 'TIME TO: CALCULATE HIGH-GAMMA ICAACT POWER...'); + +% Creating the HIGH-GAMMA component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_hg_t_inds]=marks_array2flags(icaact_hg_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +logging_log('INFO', 'TIME TO: CREATE WINDOW CRITERIA VECTOR...'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +ichg_epoch_flags=zeros(size(EEG.data(1,:,:))); +ichg_epoch_flags(1,:,epoch_inds(flag_hg_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_hg',[0,1,1],ichg_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE HIGH-GAMMA ICAACT POWER AND UPDATE MARKS...'); +toc +clear ichg_epoch_flags; + +%% MARKS UPDATE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Merges the epochs back to continuous data. Flags marks gaps and combines time +% the new time marks into the manual marks. + +% Concat epochs back into continuous data +logging_log('INFO', 'Concatenating windowed data...'); +tic; +EEG=marks_epochs2continuous(EEG); +EEG=eeg_checkset(EEG,'eventconsistency'); +logging_log('INFO', 'TIME TO: CONCATENATE THE WINDOWED DATA INTO CONTINUOUS DATA...'); +toc + +% Mark flag gaps between time marks +tic; +EEG=pop_marks_flag_gap(EEG,{'manual','mark_gap','ic_sd2'}, ... + 2000,'mark_gap',[.8,.8,.8],'offsets',[0 0],'ref_point','both'); +logging_log('INFO', 'TIME TO: MARK FLAG GAPS THAT ARE < 2 SECONDS...'); +toc + +% Combine the removal marks into manual +tic; +EEG=pop_marks_merge_labels(EEG,'time_info',{'manual','ic_sd2','mark_gap'},'target_label','manual'); +logging_log('INFO', 'TIME TO: COMBINE MARKS STRUCTURE INTO MANUAL FLAGS...'); +toc + +% begin prep for dipole fit +logging_log('INFO','Checking for JVM...'); +javachk('jvm'); + +% TODO(mae) this isn't added for some reason +addpath([pwd '/derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/Fieldtrip-lite141209/src']) + +%% DIPFIT +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Does the dipole fitting procedures. This takes a substantial amount of +% time. +% TODO: Make sure this is all needed, seems to be repeating things +logging_log('INFO','FITTING DIPOLES...'); +tic; +EEG = pop_dipfit_settings( EEG, 'hdmfile','derivatives/BIDS-Lossless-EEG/code/misc/standard_vol.mat', ... + 'coordformat','MNI', ... + 'mrifile','derivatives/BIDS-Lossless-EEG/code/misc/standard_mri.mat', ... + 'chanfile','derivatives/BIDS-Lossless-EEG/code/misc/standard_1020.elc', ... + 'coord_transform',[0 0 0 0 0 0 1 1 1], ... + 'chansel',[1:EEG.nbchan] ); + +% OLD SINGLE THREADED SETUP +% EEG = pop_multifit(EEG, [1:EEG.nbchan] ,'threshold',100,'plotopt',{'normlen' 'on'}); + +% NEW MULTI-THREADED SETUP +EEG = pop_multifit_bucanl_parallel(EEG, [1:EEG.nbchan], 8,'threshold',100,'plotopt',{'normlen' 'on'}); + +EEG=eeg_checkset(EEG); +logging_log('INFO','TIME TO: FIT DIPOLES...'); +toc + +%% MARK ICs FOR REJECTION BASED ON RV +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Creates marks based on dipfit results. Dipfit marks are added +% for rejection while ICMARC are just for reference. +% IC RESIDUAL VARIANCE FLAG +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Residuals greater than 15% are flagged - can be done here as it only references +% dipoles and their values. +%logging_log('INFO','UPDATE MARKS STRUCTURE WITH IC_RV...'); +%tic; +% Dipole criteria changed from 0.15 to 0.2 to be less sensitive +%EEG.reject.gcompreject(find([EEG.dipfit.model.rv]>.15))=1; +%EEG.reject.gcompreject(find(isnan([EEG.dipfit.model.rv])))=1; +%EEG.marks=marks_add_label(EEG.marks,'comp_info', ... +% {'ic_rv',[1,0,0],[1,0,0],-1,EEG.reject.gcompreject'}); +%logging_log('INFO','TIME TO: UPDATE MARKS STRUCTURE...'); +%toc + +% COMBINE MARKS STRUCTURE INTO MANUAL FLAGS... +%tic; +%EEG=pop_marks_merge_labels(EEG,'comp_info',{'manual','ic_rv'},'target_label','manual'); +%logging_log('INFO','TIME TO: COMBINE MARKS STRUCTURE INTO MANUAL FLAGS...'); +%toc + +% Adjust montage +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% This will pretty much always be dataset specific - so be careful +EEG = warp_locs( EEG, 'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020.elc', ... + 'transform',[0,0,0,0,0,1.57,1,1,1], ... + 'manual','off'); +EEG.chaninfo.nosedir='+X'; +EEG=eeg_checkset(EEG); + +%% SAVE LOSSLESS DERIVATIVE *_eeg_ll.set FILE (same name as the input file from the +% BIDS root directory [but located in the derivatives/lossless directory]). +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +logging_log('INFO','Saving set file: derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_eeg_ll.set...'); +tic; +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_eeg_ll.set'); +logging_log('INFO','TIME TO: SAVE DIP DATA SET...'); +toc + +% Save diagnostic arrays +try OCTAVE_VERSION; + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_icaact_sd2_t.mat','icaact_sd2_t'); +catch + save('derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_icaact_sd2_t.mat','icaact_sd2_t'); +end + +logging_log('INFO', 'Scheduler: sbatch'); +print_chan_sample(EEG); + + +if ~find(strcmpi({EEG.marks.chan_info.label},'ch_s_sd')); + EEG.marks=marks_add_label(EEG.marks,'chan_info',{'ch_s_sd',[1,0,0],[]}); +end; + +EEG = eeg_checkset(EEG); + +% CHECK FOR IC CLASSIFICATION MARKS +%% Use the ICLabel plugin +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% ICLabel is a database style plugin that classifies ICA components into the +% following seven categories + +% (Brain)(Muscle)(Eye)(Heart)(Line Noise)(Channel Noise)(Other) +% ( 1 )( 2 )( 3 )( 4 )( 5 )( 6 )( 7 ) + +% Colour structure and groupings are as follows: +% ---------- +% Brain -> [0, 0.4, 0] +% ---------- +% Muscle -> [0.4, 0, 0] +% Eye -> [0.8, 0, 0] +% Heart -> [0.8, 0.4, 0] +% ---------- +% Line Noise -> [0.2, 0.4, 0.8] +% Channel noise -> [0.4, 0, 0.8] +% ---------- +% Other -> [0.8, 0.8, 0] +% ---------- + +% Create lookup table of the above information. +pairs = {'brain',[0, 0.4, 0]; +'muscle',[0.4,0,0]; +'eye',[0.8,0,0]; +'heart',[0.8,0.4,0]; +'line_noise',[0.2,0.4,0.8]; +'chan_noise',[0.4,0,0.8]; +'other',[.7 .4 0]}; +lookupTable = cell2struct(pairs, {'name', 'colour'}, 2); + +EEG = pop_marks_select_data(EEG,'time marks',[],'labels',{'manual'},'remove','on'); + +% Run ICLabel +%EEG = iclabel(EEG); + +% extract features +disp 'ICLabel: extracting features...' +features = ICL_feature_extractor(EEG, 40); + +% run ICL +disp 'ICLabel: calculating labels...' +labels = run_ICL('default',features{:}); + +% save into EEG +disp 'ICLabel: saving results...' +EEG.etc.ic_classification.ICLabel.classes = ... + {'Brain', 'Muscle', 'Eye', 'Heart', 'Line', 'Channel', 'Other'}; +EEG.etc.ic_classification.ICLabel.classifications = labels; + +tmp=EEG.etc.ic_classification; + +% Reload dataset: +EEG = pop_loadset('filepath','derivatives/BIDS-Lossless-EEG','filename','./sub-006/eeg/sub-006_task-faceFO_eeg_ll.set'); +EEG = eeg_checkset(EEG); + +EEG.etc.ic_classification=tmp; + +% Extract labels from the structure. +rawLabels = zeros([1,length(EEG.etc.ic_classification.ICLabel.classifications)]); +for index = 1:length(EEG.etc.ic_classification.ICLabel.classifications) + [p,i] = max(EEG.etc.ic_classification.ICLabel.classifications(index,1:end-1)); + if p<.3; + i=7; + end + rawLabels(index) = i; +end + +%ICLabel Exporting to marks +%%%%%%%%%%%%%%%%%%%%%%%%%%%% +logging_log('INFO','UPDATE MARKS STRUCTURE WITH ICLABEL BRAIN COMPS...'); +for compStage = 1:7 + ICLabel_pass= zeros([1,length(rawLabels)]); + ICLabel_pass(find(rawLabels==compStage)) = 1; + ICLabel_pass = ICLabel_pass'; + EEG.marks=marks_add_label(EEG.marks,'comp_info', ... + {lookupTable(compStage).name,lookupTable(compStage).colour, ... + lookupTable(compStage).colour,1,ICLabel_pass}); +end +% make any brain components always on top: +EEG.marks.comp_info(1,find(strcmp({EEG.marks.comp_info.label},'brain'))).order=2; + + +%RESET/INITIATE CHAN MARKS... +manual_ind=find(strcmp('manual',{EEG.marks.chan_info.label})); + + +%RESET/INITIATE IC MARKS... +EEG.marks=marks_add_label(EEG.marks,'comp_info',{'ambig',[.3,.3,1],[.3,.3,1],3,zeros(min(size(EEG.icaweights)),1)}); +manual_ind=find(strcmp('manual',{EEG.marks.comp_info.label})); +ch_s_sd_ind=find(strcmp('ch_s_sd',{EEG.marks.chan_info.label})); +ch_sd_ind=find(strcmp('ch_sd',{EEG.marks.chan_info.label})); +low_r_ind=find(strcmp('low_r',{EEG.marks.chan_info.label})); +bridge_ind=find(strcmp('bridge',{EEG.marks.chan_info.label})); +rank_ind=find(strcmp('rank',{EEG.marks.chan_info.label})); +icrt_ind=find(strcmp('ic_rt',{EEG.marks.comp_info.label})); +brain_ind=find(strcmp('brain',{EEG.marks.comp_info.label})); +muscle_ind=find(strcmp('muscle',{EEG.marks.comp_info.label})); +eye_ind=find(strcmp('eye',{EEG.marks.comp_info.label})); +heart_ind=find(strcmp('heart',{EEG.marks.comp_info.label})); +line_noise_ind=find(strcmp('line_noise',{EEG.marks.comp_info.label})); +chan_noise_ind=find(strcmp('chan_noise',{EEG.marks.comp_info.label})); +other_ind=find(strcmp('other',{EEG.marks.comp_info.label})); +ambig_ind=find(strcmp('ambig',{EEG.marks.comp_info.label})); + +EEG.marks.comp_info(manual_ind).order=1; +if ~isempty(ch_s_sd_ind); % skip study-wide criteria if not used + EEG.marks.comp_info(ch_s_sd_ind).order=2; +end; +EEG.marks.comp_info(ch_sd_ind).order=2; +EEG.marks.comp_info(low_r_ind).order=2; +EEG.marks.comp_info(bridge_ind).order=2; +EEG.marks.comp_info(rank_ind).order=2; +EEG.marks.comp_info(icrt_ind).order=1; + +EEG.marks.comp_info(brain_ind).order=2; +EEG.marks.comp_info(muscle_ind).order=2; +EEG.marks.comp_info(eye_ind).order=2; +EEG.marks.comp_info(heart_ind).order=2; +EEG.marks.comp_info(line_noise_ind).order=2; +EEG.marks.comp_info(chan_noise_ind).order=2; +EEG.marks.comp_info(other_ind).order=2; +EEG.marks.comp_info(ambig_ind).order=3; + +bioa_inds = marks_label2index(EEG.marks.comp_info,{'muscle','eye','heart','chan_noise'},'indexes'); +EEG.marks.comp_info(manual_ind).flags=zeros(min(size(EEG.icaweights)),1); +EEG.marks.comp_info(manual_ind).flags(bioa_inds)=1; + +% BIDS Export calls +% Dot for path is on purpose. Don't worry - it's safe. +fName = './derivatives/BIDS-Lossless-EEG/./sub-006/eeg/sub-006_task-faceFO_eeg.edf'; +pop_writeeeg(EEG, fName, 'TYPE','EDF'); +dlmwrite(strrep(fName,'_eeg.edf','_icaweights.tsv'),EEG.icaweights,'\t'); +dlmwrite(strrep(fName,'_eeg.edf','_icasphere.tsv'),EEG.icasphere,'\t'); +s = {}; +s.algorithm = 'amica15'; +s.icachansind = EEG.icachansind; +s.intendedFor = fName; +savejson('',s,strrep(fName,'_eeg.edf','_icaweights.json')); +export_anno(EEG,fName,''); + +% ICLabel quick export +iclFname = strrep(fName,'_eeg.edf','_iclabel.mat'); +save(iclFname, 'tmp'); % see above ICLabel section for tmp definition diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/s05_concat_data_sub_007_task_faceFO_eeg.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/s05_concat_data_sub_007_task_faceFO_eeg.m new file mode 100644 index 000000000..37217e1b8 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/s05_concat_data_sub_007_task_faceFO_eeg.m @@ -0,0 +1,678 @@ +warning off; + +pkg load signal; +pkg load parallel; +pkg load io; + +file_path = fileparts(mfilename('fullpath')); + +addpath([file_path '/../../code/dependencies/eeglab_lossless'], ... + [file_path '/../../code/dependencies/matlog'], ... + [file_path '/../../code/tools']); +cd '/scratch/tk11br/Face13/'; + +% eeglab auto loads the path correctly instead of shotgun blasting with +% addpath(genpath(<>)) as we were doing prior +if ~exist('EEG', 'var') + eeglab; +end +% Use matlog disp function override +logging_override_disp(true); +% Use matlog INFO level debugging +logging_set_log_level('INFO') + +% Don't run with traditional but enable some of the options +save_default_options ('-mat-binary'); +confirm_recursive_rmdir(false); + +% This makes | and & || and && in earlier version of Octave where +% | and & dont short curcuit by default +if exist('do_braindead_shortcircuit_evaluation', 'builtin') + do_braindead_shortcircuit_evaluation(true); +end + +% Options +pop_editoptions('option_savetwofiles', 1,'option_single',0); + +%% SCRIPT DESCRIPTION +% This script combineds the results of the amicas A,B and C. It takes this information and using ICAtest it determines which components were +% and were not replicated across and flags them, along with time sections with high standard deviation. Beta,theta and Alpha power is calculated +% arcoss time and is marked but not primed for removal. +% +%% From Config key_strswap Description +%----------------------------------------------------------------------------------------------------------------------------- +% in_path = derivatives/BIDS-Lossless-EEG Relative path to input data files assuming cd = work_path +% recur_sec = 1 Recurrence (sec) for the current artifact detection epoching (e.g. 1) +% limit_sec = [0 1] Limits (sec) for the current artifact detection epoching (e.g. [0 1]) +% sd_t_meth = q Method used for flagging epochs (e.g. 'q' (quantiles), or 'na' (default)) +% sd_t_vals = [.3 .7] Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7]) +% sd_t_o = 6 z threshold for flagging epochs during standard deviation criteria (e.g. 6) +% sd_t_f_meth = fixed Fixed method used for flagging epochs (should be 'fixed') +% sd_t_f_vals = Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% sd_t_f_o = .2 z threshold for flagging epochs during fixed standard deviation criteria (e.g. .2) +% min_gap_ms = 2000 Minimum time (ms) to allow between periods marked for rejection +% icr_crit = 0.05 False discovery rate of component replicatablilty, measure of sensitivity % +% dip_vol = derivatives/BIDS-Lossless-EEG/code/misc/standard_vol.mat file path/name of the standard volume file used in dipole fitting (e.g. derivatives/lossless/code/misc/standard_vol.mat). +% dip_mri = derivatives/BIDS-Lossless-EEG/code/misc/standard_mri.mat file path/name of the standard mri file used in dipole fitting (e.g. derivatives/lossless/code/misc/standard_mri.mat). +% dip_elc = derivatives/BIDS-Lossless-EEG/code/misc/standard_1020.elc file path/name of the standard electrode file used in dipole fitting (derivatives/lossless/code/misc/standard_1020.elc). +% dip_transmat = [0 0 0 0 0 0 1 1 1] Transformation matrix to apply during dipole fit (e.g. [0 0 0 0 0 0 1 1 1]) +% cor_transmat = [0,0,0,0,0,1.57,1,1,1] The transformation matrix to apply between dipole fitting to the standard head and performing ICLabel to the EEGLAB default orientation (e.g.[0,0,0,0,0,1.57,1,1,1]) +% cor_nosedir = +X Nose direction of montage to apply between dipole fitting to the standard head and performing ICLabel to the EEGLAB default orientation (e.g. +Y) +% out_path = derivatives/BIDS-Lossless-EEG Relative path to output data files assuming cd = work_path + +%% LOAD COMPART DATASET +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Loads the data file and the first amica model. + +logging_log('INFO', 'Loading set file: ./sub-007/eeg/sub-007_task-faceFO_compart_data.set...'); +tic; +EEG = pop_loadset('filepath','derivatives/BIDS-Lossless-EEG','filename','./sub-007/eeg/sub-007_task-faceFO_compart_data.set'); +EEG = eeg_checkset( EEG ); +logging_log('INFO', 'TIME TO: LOAD DATASET...'); +toc + +% Set up the ICA channels +EEG.icachansind = marks_label2index(EEG.marks.chan_info,{'manual','rank'},'indices','invert','on'); + +% Load Amica A +logging_log('INFO', 'Loading Amica ABC...'); +tic; +EEG.amica(2).models=loadmodout15('derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_amicaout_A'); +EEG.icaweights=EEG.amica(2).models.W; +EEG.icasphere=EEG.amica(2).models.S(1:EEG.amica(2).models.num_pcs,:); + +%% PASS EEG DATA THROUGH THE AMICA MATRIX +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Loads the B and C amica models. Amica A is used to fill in the ICA information. + +% Instead of EEG=eeg_checkset(EEG); +tmpdata = EEG.data(EEG.icachansind,:); +tmpindices = find(~sum(isnan(tmpdata))); +EEG.icaact = zeros(size(EEG.icaweights,1), size(tmpdata,2)); EEG.icaact(:) = NaN; +EEG.icaact(:,tmpindices) = (EEG.icaweights*EEG.icasphere)*tmpdata(:,tmpindices); +clear tmpdata; + +% Create winv for topo plots manually +EEG.icawinv = []; +EEG.icawinv = pinv(EEG.icaweights * EEG.icasphere); + +% Load Amica B +EEG.amica(3).models=loadmodout15('derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_amicaout_B'); +% Load Amica C +EEG.amica(4).models=loadmodout15('derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_amicaout_C'); +logging_log('INFO', 'TIME TO: LOAD ICA ABC INFO...'); +toc + +%% UPDATE REJECTION STRUCTURE... +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Creating flag marks to based on the log liklyhodd models found for each of the ICA models + +% Flags for amicas A B and C +logging_log('INFO', 'Updating latflaginfo structure...'); +tic; + +% A +logl_A_flags=zeros(size(EEG.data(1,:))); +logl_A_flag_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indices','invert','on'); + +% Check to see if the overwrite will be successful. If the overwirte is a different size then some of the old data will be retained. +if length(EEG.amica(2).models.Lt) ~= length(logl_A_flag_inds) + EEG.amica(2).models.Lt = EEG.amica(2).models.Lt(1:length(logl_A_flag_inds)); + warning('YOU FORGOT TO DELETE YOUR OLD AMICA FILES AND THE NEW CHANNEL SIZES DO NOT MATCH UP'); +end + +logl_A_flags(logl_A_flag_inds)=(EEG.amica(2).models.Lt-min(EEG.amica(2).models.Lt))/(max(EEG.amica(2).models.Lt)-min(EEG.amica(2).models.Lt)); +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'logl_A',[1,0.45,0],logl_A_flags}); +clear logl_A_flags; +% B +logl_B_flags=zeros(size(EEG.data(1,:))); +logl_B_flag_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indices','invert','on'); +logl_B_flags(logl_B_flag_inds)=(EEG.amica(3).models.Lt-min(EEG.amica(3).models.Lt))/(max(EEG.amica(3).models.Lt)-min(EEG.amica(3).models.Lt)); +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'logl_B',[1,0.45,0],logl_B_flags}); +clear logl_B_flags; +% C +logl_C_flags=zeros(size(EEG.data(1,:))); +logl_C_flag_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indices','invert','on'); +logl_C_flags(logl_C_flag_inds)=(EEG.amica(4).models.Lt-min(EEG.amica(4).models.Lt))/(max(EEG.amica(4).models.Lt)-min(EEG.amica(4).models.Lt)); +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'logl_C',[1,0.45,0],logl_C_flags}); +clear logl_C_flags; + +logging_log('INFO', 'TIME TO: UPDATE REJECTION STRUCTURE...'); +toc + +%% IDENTIFY RELIABLE COMPONENTS USING ISCTEST +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Gives the A variable of each of the amicas to the function icatest. Returned are the indecies of the +% Components that were not replicated across all 3 of the models. + +tic; +% Clear a in case of local models are saved already +a = []; +for i=2:length(EEG.amica); + a(:,:,i-1)=EEG.amica(i).models(1).A; +end + +% Calculating the replicated component flag +% Replicate criteria changed from 0.05,0.05 to 0.1,0.1 to be less sensitive +ca=isctest(a,0.05,0.05,'mixing'); +if isempty(ca) + mess = ['ISCTest failed to return reasonable values. ISCTest is ' ... + 'meant to find duplicate channels in amica_{A,B,C} if this ' ... + 'failed consider investigating the datafile']; + logging_log('ERROR', mess); + error(mess); +end +icr_inds=setdiff(1:min(size(EEG.amica(2).models(1).A)),ca(:,1)); + +% Creating the replicated component flag +logging_log('INFO', 'Updating chflaginfo structure...'); +icr_comp_flags=zeros(min(size(EEG.amica(2).models(1).A)),1); +icr_comp_flags(icr_inds)=1; +EEG.marks=marks_add_label(EEG.marks,'comp_info', ... + {'ic_rt',[0,0,1],[0,0,1],-1,icr_comp_flags}); +logging_log('INFO', 'TIME TO: EDIT FIND REPLICATED COMPONENTS AND UPDATE COMPINFO STRUCT...'); +toc + +% Window the continuous data +logging_log('INFO', 'Windowing the continous data...'); +tic; +EEG=marks_continuous2epochs(EEG,'recurrence',1,'limits',[0 1]); +logging_log('INFO', 'TIME TO: WINDOW THE CONTINUOUS DATA...'); +toc + +%% CALCULATE ICAACT SD +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Similarly to the IC_SD flag created in compart. Marks sections of time where the components +% have an abnormally high standard deviation. + +% Calculating the replicated component flag +logging_log('INFO', 'Calculating the IC sd array for window criteria...'); +tic; +epoch_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indexes','invert','on'); +[EEG,icaact_sd2_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'varmeasure','absmean', ... + 'detrend','off', ... + 'plot_figs','off'); + +% Creating the ic_sd component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_sd_t_inds]=marks_array2flags(icaact_sd2_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); + +icsd_epoch_flags=zeros(size(EEG.data(1,:,:))); +icsd_epoch_flags(1,:,epoch_inds(flag_sd_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_sd2',[.4,.6,.6],icsd_epoch_flags}); +logging_log('INFO', 'TIME TO: EDIT FIND IC_SD AND UPDATE TIMEINFO REJECTION STRUCT...'); +toc +clear icsd_epoch_flags; + +%% CALCULATE DELTA/THETA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high Theta/Alpha Power + +% Calculating the Delta/Theta component flag +logging_log('INFO', 'Calculating the delta/theta IC power array for window criteria...'); +tic; +[EEG,icaact_dt_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[0 7], ... + 'plot_figs','off'); + +% Creating the Delta/Theta component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_dt_t_inds]=marks_array2flags(icaact_dt_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +icdt_epoch_flags=zeros(size(EEG.data(1,:,:))); +icdt_epoch_flags(1,:,epoch_inds(flag_dt_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_dt',[1,1,0],icdt_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE DELTA/THETA ICAACT POWER AND UPDATE MARKS...'); +toc +clear icdt_epoch_flags; + +%% CALCULATE ALPHA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high Alpha Power + +% Calculating the Alpha component flag +logging_log('INFO', 'Calculating the alpha IC power array for window criteria...'); +tic; +[EEG,icaact_a_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[8 13], ... + 'plot_figs','off'); + +% Creating the Alpha component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_a_t_inds]=marks_array2flags(icaact_a_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +ica_epoch_flags=zeros(size(EEG.data(1,:,:))); +ica_epoch_flags(1,:,epoch_inds(flag_a_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_a',[1,1,0],ica_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE ALPHA ICAACT POWER AND UPDATE MARKS...'); +toc +clear ica_epoch_flags; + +%% CALCULATE BETA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high Beta Power. +% Note that at the moment this mark is used for removals. + +% Calculating the Beta component flag +logging_log('INFO', 'Calculating the beta IC power array for window criteria...'); +tic; +[EEG,icaact_b_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[14 30], ... + 'plot_figs','off'); +logging_log('INFO', 'TIME TO: CALCULATE BETA ICAACT POWER...'); + +% Creating the Beta component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_b_t_inds]=marks_array2flags(icaact_b_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +logging_log('INFO', 'TIME TO: CREATE WINDOW CRITERIA VECTOR...'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +icb_epoch_flags=zeros(size(EEG.data(1,:,:))); +icb_epoch_flags(1,:,epoch_inds(flag_b_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_b',[0,1,1],icb_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE BETA ICAACT POWER AND UPDATE MARKS...'); +toc +clear icb_epoch_flags; + +%% CALCULATE LOW GAMMA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high Low-Gamma Power. +% Note that at the moment this mark is used for removals. + +% Calculating the Beta component flag +logging_log('INFO', 'Calculating the low-gamma IC power array for window criteria...'); +tic; +[EEG,icaact_lg_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[31 60], ... + 'plot_figs','off'); +logging_log('INFO', 'TIME TO: CALCULATE LOW-GAMMA ICAACT POWER...'); + +% Creating the LOW-GAMMA component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_lg_t_inds]=marks_array2flags(icaact_lg_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +logging_log('INFO', 'TIME TO: CREATE WINDOW CRITERIA VECTOR...'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +iclg_epoch_flags=zeros(size(EEG.data(1,:,:))); +iclg_epoch_flags(1,:,epoch_inds(flag_lg_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_lg',[0,1,1],iclg_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE LOW-GAMMA ICAACT POWER AND UPDATE MARKS...'); +toc +clear iclg_epoch_flags; + + +%% CALCULATE HIGH GAMMA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high High-Gamma Power. +% Note that at the moment this mark is used for removals. + +% Calculating the Beta component flag +logging_log('INFO', 'Calculating the high-gamma IC power array for window criteria...'); +tic; +[EEG,icaact_hg_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[61 100], ... + 'plot_figs','off'); +logging_log('INFO', 'TIME TO: CALCULATE HIGH-GAMMA ICAACT POWER...'); + +% Creating the HIGH-GAMMA component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_hg_t_inds]=marks_array2flags(icaact_hg_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +logging_log('INFO', 'TIME TO: CREATE WINDOW CRITERIA VECTOR...'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +ichg_epoch_flags=zeros(size(EEG.data(1,:,:))); +ichg_epoch_flags(1,:,epoch_inds(flag_hg_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_hg',[0,1,1],ichg_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE HIGH-GAMMA ICAACT POWER AND UPDATE MARKS...'); +toc +clear ichg_epoch_flags; + +%% MARKS UPDATE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Merges the epochs back to continuous data. Flags marks gaps and combines time +% the new time marks into the manual marks. + +% Concat epochs back into continuous data +logging_log('INFO', 'Concatenating windowed data...'); +tic; +EEG=marks_epochs2continuous(EEG); +EEG=eeg_checkset(EEG,'eventconsistency'); +logging_log('INFO', 'TIME TO: CONCATENATE THE WINDOWED DATA INTO CONTINUOUS DATA...'); +toc + +% Mark flag gaps between time marks +tic; +EEG=pop_marks_flag_gap(EEG,{'manual','mark_gap','ic_sd2'}, ... + 2000,'mark_gap',[.8,.8,.8],'offsets',[0 0],'ref_point','both'); +logging_log('INFO', 'TIME TO: MARK FLAG GAPS THAT ARE < 2 SECONDS...'); +toc + +% Combine the removal marks into manual +tic; +EEG=pop_marks_merge_labels(EEG,'time_info',{'manual','ic_sd2','mark_gap'},'target_label','manual'); +logging_log('INFO', 'TIME TO: COMBINE MARKS STRUCTURE INTO MANUAL FLAGS...'); +toc + +% begin prep for dipole fit +logging_log('INFO','Checking for JVM...'); +javachk('jvm'); + +% TODO(mae) this isn't added for some reason +addpath([pwd '/derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/Fieldtrip-lite141209/src']) + +%% DIPFIT +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Does the dipole fitting procedures. This takes a substantial amount of +% time. +% TODO: Make sure this is all needed, seems to be repeating things +logging_log('INFO','FITTING DIPOLES...'); +tic; +EEG = pop_dipfit_settings( EEG, 'hdmfile','derivatives/BIDS-Lossless-EEG/code/misc/standard_vol.mat', ... + 'coordformat','MNI', ... + 'mrifile','derivatives/BIDS-Lossless-EEG/code/misc/standard_mri.mat', ... + 'chanfile','derivatives/BIDS-Lossless-EEG/code/misc/standard_1020.elc', ... + 'coord_transform',[0 0 0 0 0 0 1 1 1], ... + 'chansel',[1:EEG.nbchan] ); + +% OLD SINGLE THREADED SETUP +% EEG = pop_multifit(EEG, [1:EEG.nbchan] ,'threshold',100,'plotopt',{'normlen' 'on'}); + +% NEW MULTI-THREADED SETUP +EEG = pop_multifit_bucanl_parallel(EEG, [1:EEG.nbchan], 8,'threshold',100,'plotopt',{'normlen' 'on'}); + +EEG=eeg_checkset(EEG); +logging_log('INFO','TIME TO: FIT DIPOLES...'); +toc + +%% MARK ICs FOR REJECTION BASED ON RV +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Creates marks based on dipfit results. Dipfit marks are added +% for rejection while ICMARC are just for reference. +% IC RESIDUAL VARIANCE FLAG +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Residuals greater than 15% are flagged - can be done here as it only references +% dipoles and their values. +%logging_log('INFO','UPDATE MARKS STRUCTURE WITH IC_RV...'); +%tic; +% Dipole criteria changed from 0.15 to 0.2 to be less sensitive +%EEG.reject.gcompreject(find([EEG.dipfit.model.rv]>.15))=1; +%EEG.reject.gcompreject(find(isnan([EEG.dipfit.model.rv])))=1; +%EEG.marks=marks_add_label(EEG.marks,'comp_info', ... +% {'ic_rv',[1,0,0],[1,0,0],-1,EEG.reject.gcompreject'}); +%logging_log('INFO','TIME TO: UPDATE MARKS STRUCTURE...'); +%toc + +% COMBINE MARKS STRUCTURE INTO MANUAL FLAGS... +%tic; +%EEG=pop_marks_merge_labels(EEG,'comp_info',{'manual','ic_rv'},'target_label','manual'); +%logging_log('INFO','TIME TO: COMBINE MARKS STRUCTURE INTO MANUAL FLAGS...'); +%toc + +% Adjust montage +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% This will pretty much always be dataset specific - so be careful +EEG = warp_locs( EEG, 'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020.elc', ... + 'transform',[0,0,0,0,0,1.57,1,1,1], ... + 'manual','off'); +EEG.chaninfo.nosedir='+X'; +EEG=eeg_checkset(EEG); + +%% SAVE LOSSLESS DERIVATIVE *_eeg_ll.set FILE (same name as the input file from the +% BIDS root directory [but located in the derivatives/lossless directory]). +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +logging_log('INFO','Saving set file: derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_eeg_ll.set...'); +tic; +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_eeg_ll.set'); +logging_log('INFO','TIME TO: SAVE DIP DATA SET...'); +toc + +% Save diagnostic arrays +try OCTAVE_VERSION; + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_icaact_sd2_t.mat','icaact_sd2_t'); +catch + save('derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_icaact_sd2_t.mat','icaact_sd2_t'); +end + +logging_log('INFO', 'Scheduler: sbatch'); +print_chan_sample(EEG); + + +if ~find(strcmpi({EEG.marks.chan_info.label},'ch_s_sd')); + EEG.marks=marks_add_label(EEG.marks,'chan_info',{'ch_s_sd',[1,0,0],[]}); +end; + +EEG = eeg_checkset(EEG); + +% CHECK FOR IC CLASSIFICATION MARKS +%% Use the ICLabel plugin +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% ICLabel is a database style plugin that classifies ICA components into the +% following seven categories + +% (Brain)(Muscle)(Eye)(Heart)(Line Noise)(Channel Noise)(Other) +% ( 1 )( 2 )( 3 )( 4 )( 5 )( 6 )( 7 ) + +% Colour structure and groupings are as follows: +% ---------- +% Brain -> [0, 0.4, 0] +% ---------- +% Muscle -> [0.4, 0, 0] +% Eye -> [0.8, 0, 0] +% Heart -> [0.8, 0.4, 0] +% ---------- +% Line Noise -> [0.2, 0.4, 0.8] +% Channel noise -> [0.4, 0, 0.8] +% ---------- +% Other -> [0.8, 0.8, 0] +% ---------- + +% Create lookup table of the above information. +pairs = {'brain',[0, 0.4, 0]; +'muscle',[0.4,0,0]; +'eye',[0.8,0,0]; +'heart',[0.8,0.4,0]; +'line_noise',[0.2,0.4,0.8]; +'chan_noise',[0.4,0,0.8]; +'other',[.7 .4 0]}; +lookupTable = cell2struct(pairs, {'name', 'colour'}, 2); + +EEG = pop_marks_select_data(EEG,'time marks',[],'labels',{'manual'},'remove','on'); + +% Run ICLabel +%EEG = iclabel(EEG); + +% extract features +disp 'ICLabel: extracting features...' +features = ICL_feature_extractor(EEG, 40); + +% run ICL +disp 'ICLabel: calculating labels...' +labels = run_ICL('default',features{:}); + +% save into EEG +disp 'ICLabel: saving results...' +EEG.etc.ic_classification.ICLabel.classes = ... + {'Brain', 'Muscle', 'Eye', 'Heart', 'Line', 'Channel', 'Other'}; +EEG.etc.ic_classification.ICLabel.classifications = labels; + +tmp=EEG.etc.ic_classification; + +% Reload dataset: +EEG = pop_loadset('filepath','derivatives/BIDS-Lossless-EEG','filename','./sub-007/eeg/sub-007_task-faceFO_eeg_ll.set'); +EEG = eeg_checkset(EEG); + +EEG.etc.ic_classification=tmp; + +% Extract labels from the structure. +rawLabels = zeros([1,length(EEG.etc.ic_classification.ICLabel.classifications)]); +for index = 1:length(EEG.etc.ic_classification.ICLabel.classifications) + [p,i] = max(EEG.etc.ic_classification.ICLabel.classifications(index,1:end-1)); + if p<.3; + i=7; + end + rawLabels(index) = i; +end + +%ICLabel Exporting to marks +%%%%%%%%%%%%%%%%%%%%%%%%%%%% +logging_log('INFO','UPDATE MARKS STRUCTURE WITH ICLABEL BRAIN COMPS...'); +for compStage = 1:7 + ICLabel_pass= zeros([1,length(rawLabels)]); + ICLabel_pass(find(rawLabels==compStage)) = 1; + ICLabel_pass = ICLabel_pass'; + EEG.marks=marks_add_label(EEG.marks,'comp_info', ... + {lookupTable(compStage).name,lookupTable(compStage).colour, ... + lookupTable(compStage).colour,1,ICLabel_pass}); +end +% make any brain components always on top: +EEG.marks.comp_info(1,find(strcmp({EEG.marks.comp_info.label},'brain'))).order=2; + + +%RESET/INITIATE CHAN MARKS... +manual_ind=find(strcmp('manual',{EEG.marks.chan_info.label})); + + +%RESET/INITIATE IC MARKS... +EEG.marks=marks_add_label(EEG.marks,'comp_info',{'ambig',[.3,.3,1],[.3,.3,1],3,zeros(min(size(EEG.icaweights)),1)}); +manual_ind=find(strcmp('manual',{EEG.marks.comp_info.label})); +ch_s_sd_ind=find(strcmp('ch_s_sd',{EEG.marks.chan_info.label})); +ch_sd_ind=find(strcmp('ch_sd',{EEG.marks.chan_info.label})); +low_r_ind=find(strcmp('low_r',{EEG.marks.chan_info.label})); +bridge_ind=find(strcmp('bridge',{EEG.marks.chan_info.label})); +rank_ind=find(strcmp('rank',{EEG.marks.chan_info.label})); +icrt_ind=find(strcmp('ic_rt',{EEG.marks.comp_info.label})); +brain_ind=find(strcmp('brain',{EEG.marks.comp_info.label})); +muscle_ind=find(strcmp('muscle',{EEG.marks.comp_info.label})); +eye_ind=find(strcmp('eye',{EEG.marks.comp_info.label})); +heart_ind=find(strcmp('heart',{EEG.marks.comp_info.label})); +line_noise_ind=find(strcmp('line_noise',{EEG.marks.comp_info.label})); +chan_noise_ind=find(strcmp('chan_noise',{EEG.marks.comp_info.label})); +other_ind=find(strcmp('other',{EEG.marks.comp_info.label})); +ambig_ind=find(strcmp('ambig',{EEG.marks.comp_info.label})); + +EEG.marks.comp_info(manual_ind).order=1; +if ~isempty(ch_s_sd_ind); % skip study-wide criteria if not used + EEG.marks.comp_info(ch_s_sd_ind).order=2; +end; +EEG.marks.comp_info(ch_sd_ind).order=2; +EEG.marks.comp_info(low_r_ind).order=2; +EEG.marks.comp_info(bridge_ind).order=2; +EEG.marks.comp_info(rank_ind).order=2; +EEG.marks.comp_info(icrt_ind).order=1; + +EEG.marks.comp_info(brain_ind).order=2; +EEG.marks.comp_info(muscle_ind).order=2; +EEG.marks.comp_info(eye_ind).order=2; +EEG.marks.comp_info(heart_ind).order=2; +EEG.marks.comp_info(line_noise_ind).order=2; +EEG.marks.comp_info(chan_noise_ind).order=2; +EEG.marks.comp_info(other_ind).order=2; +EEG.marks.comp_info(ambig_ind).order=3; + +bioa_inds = marks_label2index(EEG.marks.comp_info,{'muscle','eye','heart','chan_noise'},'indexes'); +EEG.marks.comp_info(manual_ind).flags=zeros(min(size(EEG.icaweights)),1); +EEG.marks.comp_info(manual_ind).flags(bioa_inds)=1; + +% BIDS Export calls +% Dot for path is on purpose. Don't worry - it's safe. +fName = './derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_eeg.edf'; +pop_writeeeg(EEG, fName, 'TYPE','EDF'); +dlmwrite(strrep(fName,'_eeg.edf','_icaweights.tsv'),EEG.icaweights,'\t'); +dlmwrite(strrep(fName,'_eeg.edf','_icasphere.tsv'),EEG.icasphere,'\t'); +s = {}; +s.algorithm = 'amica15'; +s.icachansind = EEG.icachansind; +s.intendedFor = fName; +savejson('',s,strrep(fName,'_eeg.edf','_icaweights.json')); +export_anno(EEG,fName,''); + +% ICLabel quick export +iclFname = strrep(fName,'_eeg.edf','_iclabel.mat'); +save(iclFname, 'tmp'); % see above ICLabel section for tmp definition diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/s05_concat_data_sub_008_task_faceFO_eeg.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/s05_concat_data_sub_008_task_faceFO_eeg.m new file mode 100644 index 000000000..088df5213 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/s05_concat_data_sub_008_task_faceFO_eeg.m @@ -0,0 +1,678 @@ +warning off; + +pkg load signal; +pkg load parallel; +pkg load io; + +file_path = fileparts(mfilename('fullpath')); + +addpath([file_path '/../../code/dependencies/eeglab_lossless'], ... + [file_path '/../../code/dependencies/matlog'], ... + [file_path '/../../code/tools']); +cd '/scratch/tk11br/Face13/'; + +% eeglab auto loads the path correctly instead of shotgun blasting with +% addpath(genpath(<>)) as we were doing prior +if ~exist('EEG', 'var') + eeglab; +end +% Use matlog disp function override +logging_override_disp(true); +% Use matlog INFO level debugging +logging_set_log_level('INFO') + +% Don't run with traditional but enable some of the options +save_default_options ('-mat-binary'); +confirm_recursive_rmdir(false); + +% This makes | and & || and && in earlier version of Octave where +% | and & dont short curcuit by default +if exist('do_braindead_shortcircuit_evaluation', 'builtin') + do_braindead_shortcircuit_evaluation(true); +end + +% Options +pop_editoptions('option_savetwofiles', 1,'option_single',0); + +%% SCRIPT DESCRIPTION +% This script combineds the results of the amicas A,B and C. It takes this information and using ICAtest it determines which components were +% and were not replicated across and flags them, along with time sections with high standard deviation. Beta,theta and Alpha power is calculated +% arcoss time and is marked but not primed for removal. +% +%% From Config key_strswap Description +%----------------------------------------------------------------------------------------------------------------------------- +% in_path = derivatives/BIDS-Lossless-EEG Relative path to input data files assuming cd = work_path +% recur_sec = 1 Recurrence (sec) for the current artifact detection epoching (e.g. 1) +% limit_sec = [0 1] Limits (sec) for the current artifact detection epoching (e.g. [0 1]) +% sd_t_meth = q Method used for flagging epochs (e.g. 'q' (quantiles), or 'na' (default)) +% sd_t_vals = [.3 .7] Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7]) +% sd_t_o = 6 z threshold for flagging epochs during standard deviation criteria (e.g. 6) +% sd_t_f_meth = fixed Fixed method used for flagging epochs (should be 'fixed') +% sd_t_f_vals = Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% sd_t_f_o = .2 z threshold for flagging epochs during fixed standard deviation criteria (e.g. .2) +% min_gap_ms = 2000 Minimum time (ms) to allow between periods marked for rejection +% icr_crit = 0.05 False discovery rate of component replicatablilty, measure of sensitivity % +% dip_vol = derivatives/BIDS-Lossless-EEG/code/misc/standard_vol.mat file path/name of the standard volume file used in dipole fitting (e.g. derivatives/lossless/code/misc/standard_vol.mat). +% dip_mri = derivatives/BIDS-Lossless-EEG/code/misc/standard_mri.mat file path/name of the standard mri file used in dipole fitting (e.g. derivatives/lossless/code/misc/standard_mri.mat). +% dip_elc = derivatives/BIDS-Lossless-EEG/code/misc/standard_1020.elc file path/name of the standard electrode file used in dipole fitting (derivatives/lossless/code/misc/standard_1020.elc). +% dip_transmat = [0 0 0 0 0 0 1 1 1] Transformation matrix to apply during dipole fit (e.g. [0 0 0 0 0 0 1 1 1]) +% cor_transmat = [0,0,0,0,0,1.57,1,1,1] The transformation matrix to apply between dipole fitting to the standard head and performing ICLabel to the EEGLAB default orientation (e.g.[0,0,0,0,0,1.57,1,1,1]) +% cor_nosedir = +X Nose direction of montage to apply between dipole fitting to the standard head and performing ICLabel to the EEGLAB default orientation (e.g. +Y) +% out_path = derivatives/BIDS-Lossless-EEG Relative path to output data files assuming cd = work_path + +%% LOAD COMPART DATASET +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Loads the data file and the first amica model. + +logging_log('INFO', 'Loading set file: ./sub-008/eeg/sub-008_task-faceFO_compart_data.set...'); +tic; +EEG = pop_loadset('filepath','derivatives/BIDS-Lossless-EEG','filename','./sub-008/eeg/sub-008_task-faceFO_compart_data.set'); +EEG = eeg_checkset( EEG ); +logging_log('INFO', 'TIME TO: LOAD DATASET...'); +toc + +% Set up the ICA channels +EEG.icachansind = marks_label2index(EEG.marks.chan_info,{'manual','rank'},'indices','invert','on'); + +% Load Amica A +logging_log('INFO', 'Loading Amica ABC...'); +tic; +EEG.amica(2).models=loadmodout15('derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_amicaout_A'); +EEG.icaweights=EEG.amica(2).models.W; +EEG.icasphere=EEG.amica(2).models.S(1:EEG.amica(2).models.num_pcs,:); + +%% PASS EEG DATA THROUGH THE AMICA MATRIX +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Loads the B and C amica models. Amica A is used to fill in the ICA information. + +% Instead of EEG=eeg_checkset(EEG); +tmpdata = EEG.data(EEG.icachansind,:); +tmpindices = find(~sum(isnan(tmpdata))); +EEG.icaact = zeros(size(EEG.icaweights,1), size(tmpdata,2)); EEG.icaact(:) = NaN; +EEG.icaact(:,tmpindices) = (EEG.icaweights*EEG.icasphere)*tmpdata(:,tmpindices); +clear tmpdata; + +% Create winv for topo plots manually +EEG.icawinv = []; +EEG.icawinv = pinv(EEG.icaweights * EEG.icasphere); + +% Load Amica B +EEG.amica(3).models=loadmodout15('derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_amicaout_B'); +% Load Amica C +EEG.amica(4).models=loadmodout15('derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_amicaout_C'); +logging_log('INFO', 'TIME TO: LOAD ICA ABC INFO...'); +toc + +%% UPDATE REJECTION STRUCTURE... +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Creating flag marks to based on the log liklyhodd models found for each of the ICA models + +% Flags for amicas A B and C +logging_log('INFO', 'Updating latflaginfo structure...'); +tic; + +% A +logl_A_flags=zeros(size(EEG.data(1,:))); +logl_A_flag_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indices','invert','on'); + +% Check to see if the overwrite will be successful. If the overwirte is a different size then some of the old data will be retained. +if length(EEG.amica(2).models.Lt) ~= length(logl_A_flag_inds) + EEG.amica(2).models.Lt = EEG.amica(2).models.Lt(1:length(logl_A_flag_inds)); + warning('YOU FORGOT TO DELETE YOUR OLD AMICA FILES AND THE NEW CHANNEL SIZES DO NOT MATCH UP'); +end + +logl_A_flags(logl_A_flag_inds)=(EEG.amica(2).models.Lt-min(EEG.amica(2).models.Lt))/(max(EEG.amica(2).models.Lt)-min(EEG.amica(2).models.Lt)); +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'logl_A',[1,0.45,0],logl_A_flags}); +clear logl_A_flags; +% B +logl_B_flags=zeros(size(EEG.data(1,:))); +logl_B_flag_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indices','invert','on'); +logl_B_flags(logl_B_flag_inds)=(EEG.amica(3).models.Lt-min(EEG.amica(3).models.Lt))/(max(EEG.amica(3).models.Lt)-min(EEG.amica(3).models.Lt)); +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'logl_B',[1,0.45,0],logl_B_flags}); +clear logl_B_flags; +% C +logl_C_flags=zeros(size(EEG.data(1,:))); +logl_C_flag_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indices','invert','on'); +logl_C_flags(logl_C_flag_inds)=(EEG.amica(4).models.Lt-min(EEG.amica(4).models.Lt))/(max(EEG.amica(4).models.Lt)-min(EEG.amica(4).models.Lt)); +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'logl_C',[1,0.45,0],logl_C_flags}); +clear logl_C_flags; + +logging_log('INFO', 'TIME TO: UPDATE REJECTION STRUCTURE...'); +toc + +%% IDENTIFY RELIABLE COMPONENTS USING ISCTEST +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Gives the A variable of each of the amicas to the function icatest. Returned are the indecies of the +% Components that were not replicated across all 3 of the models. + +tic; +% Clear a in case of local models are saved already +a = []; +for i=2:length(EEG.amica); + a(:,:,i-1)=EEG.amica(i).models(1).A; +end + +% Calculating the replicated component flag +% Replicate criteria changed from 0.05,0.05 to 0.1,0.1 to be less sensitive +ca=isctest(a,0.05,0.05,'mixing'); +if isempty(ca) + mess = ['ISCTest failed to return reasonable values. ISCTest is ' ... + 'meant to find duplicate channels in amica_{A,B,C} if this ' ... + 'failed consider investigating the datafile']; + logging_log('ERROR', mess); + error(mess); +end +icr_inds=setdiff(1:min(size(EEG.amica(2).models(1).A)),ca(:,1)); + +% Creating the replicated component flag +logging_log('INFO', 'Updating chflaginfo structure...'); +icr_comp_flags=zeros(min(size(EEG.amica(2).models(1).A)),1); +icr_comp_flags(icr_inds)=1; +EEG.marks=marks_add_label(EEG.marks,'comp_info', ... + {'ic_rt',[0,0,1],[0,0,1],-1,icr_comp_flags}); +logging_log('INFO', 'TIME TO: EDIT FIND REPLICATED COMPONENTS AND UPDATE COMPINFO STRUCT...'); +toc + +% Window the continuous data +logging_log('INFO', 'Windowing the continous data...'); +tic; +EEG=marks_continuous2epochs(EEG,'recurrence',1,'limits',[0 1]); +logging_log('INFO', 'TIME TO: WINDOW THE CONTINUOUS DATA...'); +toc + +%% CALCULATE ICAACT SD +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Similarly to the IC_SD flag created in compart. Marks sections of time where the components +% have an abnormally high standard deviation. + +% Calculating the replicated component flag +logging_log('INFO', 'Calculating the IC sd array for window criteria...'); +tic; +epoch_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indexes','invert','on'); +[EEG,icaact_sd2_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'varmeasure','absmean', ... + 'detrend','off', ... + 'plot_figs','off'); + +% Creating the ic_sd component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_sd_t_inds]=marks_array2flags(icaact_sd2_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); + +icsd_epoch_flags=zeros(size(EEG.data(1,:,:))); +icsd_epoch_flags(1,:,epoch_inds(flag_sd_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_sd2',[.4,.6,.6],icsd_epoch_flags}); +logging_log('INFO', 'TIME TO: EDIT FIND IC_SD AND UPDATE TIMEINFO REJECTION STRUCT...'); +toc +clear icsd_epoch_flags; + +%% CALCULATE DELTA/THETA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high Theta/Alpha Power + +% Calculating the Delta/Theta component flag +logging_log('INFO', 'Calculating the delta/theta IC power array for window criteria...'); +tic; +[EEG,icaact_dt_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[0 7], ... + 'plot_figs','off'); + +% Creating the Delta/Theta component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_dt_t_inds]=marks_array2flags(icaact_dt_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +icdt_epoch_flags=zeros(size(EEG.data(1,:,:))); +icdt_epoch_flags(1,:,epoch_inds(flag_dt_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_dt',[1,1,0],icdt_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE DELTA/THETA ICAACT POWER AND UPDATE MARKS...'); +toc +clear icdt_epoch_flags; + +%% CALCULATE ALPHA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high Alpha Power + +% Calculating the Alpha component flag +logging_log('INFO', 'Calculating the alpha IC power array for window criteria...'); +tic; +[EEG,icaact_a_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[8 13], ... + 'plot_figs','off'); + +% Creating the Alpha component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_a_t_inds]=marks_array2flags(icaact_a_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +ica_epoch_flags=zeros(size(EEG.data(1,:,:))); +ica_epoch_flags(1,:,epoch_inds(flag_a_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_a',[1,1,0],ica_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE ALPHA ICAACT POWER AND UPDATE MARKS...'); +toc +clear ica_epoch_flags; + +%% CALCULATE BETA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high Beta Power. +% Note that at the moment this mark is used for removals. + +% Calculating the Beta component flag +logging_log('INFO', 'Calculating the beta IC power array for window criteria...'); +tic; +[EEG,icaact_b_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[14 30], ... + 'plot_figs','off'); +logging_log('INFO', 'TIME TO: CALCULATE BETA ICAACT POWER...'); + +% Creating the Beta component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_b_t_inds]=marks_array2flags(icaact_b_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +logging_log('INFO', 'TIME TO: CREATE WINDOW CRITERIA VECTOR...'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +icb_epoch_flags=zeros(size(EEG.data(1,:,:))); +icb_epoch_flags(1,:,epoch_inds(flag_b_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_b',[0,1,1],icb_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE BETA ICAACT POWER AND UPDATE MARKS...'); +toc +clear icb_epoch_flags; + +%% CALCULATE LOW GAMMA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high Low-Gamma Power. +% Note that at the moment this mark is used for removals. + +% Calculating the Beta component flag +logging_log('INFO', 'Calculating the low-gamma IC power array for window criteria...'); +tic; +[EEG,icaact_lg_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[31 60], ... + 'plot_figs','off'); +logging_log('INFO', 'TIME TO: CALCULATE LOW-GAMMA ICAACT POWER...'); + +% Creating the LOW-GAMMA component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_lg_t_inds]=marks_array2flags(icaact_lg_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +logging_log('INFO', 'TIME TO: CREATE WINDOW CRITERIA VECTOR...'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +iclg_epoch_flags=zeros(size(EEG.data(1,:,:))); +iclg_epoch_flags(1,:,epoch_inds(flag_lg_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_lg',[0,1,1],iclg_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE LOW-GAMMA ICAACT POWER AND UPDATE MARKS...'); +toc +clear iclg_epoch_flags; + + +%% CALCULATE HIGH GAMMA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high High-Gamma Power. +% Note that at the moment this mark is used for removals. + +% Calculating the Beta component flag +logging_log('INFO', 'Calculating the high-gamma IC power array for window criteria...'); +tic; +[EEG,icaact_hg_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[61 100], ... + 'plot_figs','off'); +logging_log('INFO', 'TIME TO: CALCULATE HIGH-GAMMA ICAACT POWER...'); + +% Creating the HIGH-GAMMA component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_hg_t_inds]=marks_array2flags(icaact_hg_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +logging_log('INFO', 'TIME TO: CREATE WINDOW CRITERIA VECTOR...'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +ichg_epoch_flags=zeros(size(EEG.data(1,:,:))); +ichg_epoch_flags(1,:,epoch_inds(flag_hg_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_hg',[0,1,1],ichg_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE HIGH-GAMMA ICAACT POWER AND UPDATE MARKS...'); +toc +clear ichg_epoch_flags; + +%% MARKS UPDATE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Merges the epochs back to continuous data. Flags marks gaps and combines time +% the new time marks into the manual marks. + +% Concat epochs back into continuous data +logging_log('INFO', 'Concatenating windowed data...'); +tic; +EEG=marks_epochs2continuous(EEG); +EEG=eeg_checkset(EEG,'eventconsistency'); +logging_log('INFO', 'TIME TO: CONCATENATE THE WINDOWED DATA INTO CONTINUOUS DATA...'); +toc + +% Mark flag gaps between time marks +tic; +EEG=pop_marks_flag_gap(EEG,{'manual','mark_gap','ic_sd2'}, ... + 2000,'mark_gap',[.8,.8,.8],'offsets',[0 0],'ref_point','both'); +logging_log('INFO', 'TIME TO: MARK FLAG GAPS THAT ARE < 2 SECONDS...'); +toc + +% Combine the removal marks into manual +tic; +EEG=pop_marks_merge_labels(EEG,'time_info',{'manual','ic_sd2','mark_gap'},'target_label','manual'); +logging_log('INFO', 'TIME TO: COMBINE MARKS STRUCTURE INTO MANUAL FLAGS...'); +toc + +% begin prep for dipole fit +logging_log('INFO','Checking for JVM...'); +javachk('jvm'); + +% TODO(mae) this isn't added for some reason +addpath([pwd '/derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/Fieldtrip-lite141209/src']) + +%% DIPFIT +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Does the dipole fitting procedures. This takes a substantial amount of +% time. +% TODO: Make sure this is all needed, seems to be repeating things +logging_log('INFO','FITTING DIPOLES...'); +tic; +EEG = pop_dipfit_settings( EEG, 'hdmfile','derivatives/BIDS-Lossless-EEG/code/misc/standard_vol.mat', ... + 'coordformat','MNI', ... + 'mrifile','derivatives/BIDS-Lossless-EEG/code/misc/standard_mri.mat', ... + 'chanfile','derivatives/BIDS-Lossless-EEG/code/misc/standard_1020.elc', ... + 'coord_transform',[0 0 0 0 0 0 1 1 1], ... + 'chansel',[1:EEG.nbchan] ); + +% OLD SINGLE THREADED SETUP +% EEG = pop_multifit(EEG, [1:EEG.nbchan] ,'threshold',100,'plotopt',{'normlen' 'on'}); + +% NEW MULTI-THREADED SETUP +EEG = pop_multifit_bucanl_parallel(EEG, [1:EEG.nbchan], 8,'threshold',100,'plotopt',{'normlen' 'on'}); + +EEG=eeg_checkset(EEG); +logging_log('INFO','TIME TO: FIT DIPOLES...'); +toc + +%% MARK ICs FOR REJECTION BASED ON RV +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Creates marks based on dipfit results. Dipfit marks are added +% for rejection while ICMARC are just for reference. +% IC RESIDUAL VARIANCE FLAG +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Residuals greater than 15% are flagged - can be done here as it only references +% dipoles and their values. +%logging_log('INFO','UPDATE MARKS STRUCTURE WITH IC_RV...'); +%tic; +% Dipole criteria changed from 0.15 to 0.2 to be less sensitive +%EEG.reject.gcompreject(find([EEG.dipfit.model.rv]>.15))=1; +%EEG.reject.gcompreject(find(isnan([EEG.dipfit.model.rv])))=1; +%EEG.marks=marks_add_label(EEG.marks,'comp_info', ... +% {'ic_rv',[1,0,0],[1,0,0],-1,EEG.reject.gcompreject'}); +%logging_log('INFO','TIME TO: UPDATE MARKS STRUCTURE...'); +%toc + +% COMBINE MARKS STRUCTURE INTO MANUAL FLAGS... +%tic; +%EEG=pop_marks_merge_labels(EEG,'comp_info',{'manual','ic_rv'},'target_label','manual'); +%logging_log('INFO','TIME TO: COMBINE MARKS STRUCTURE INTO MANUAL FLAGS...'); +%toc + +% Adjust montage +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% This will pretty much always be dataset specific - so be careful +EEG = warp_locs( EEG, 'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020.elc', ... + 'transform',[0,0,0,0,0,1.57,1,1,1], ... + 'manual','off'); +EEG.chaninfo.nosedir='+X'; +EEG=eeg_checkset(EEG); + +%% SAVE LOSSLESS DERIVATIVE *_eeg_ll.set FILE (same name as the input file from the +% BIDS root directory [but located in the derivatives/lossless directory]). +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +logging_log('INFO','Saving set file: derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_eeg_ll.set...'); +tic; +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_eeg_ll.set'); +logging_log('INFO','TIME TO: SAVE DIP DATA SET...'); +toc + +% Save diagnostic arrays +try OCTAVE_VERSION; + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_icaact_sd2_t.mat','icaact_sd2_t'); +catch + save('derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_icaact_sd2_t.mat','icaact_sd2_t'); +end + +logging_log('INFO', 'Scheduler: sbatch'); +print_chan_sample(EEG); + + +if ~find(strcmpi({EEG.marks.chan_info.label},'ch_s_sd')); + EEG.marks=marks_add_label(EEG.marks,'chan_info',{'ch_s_sd',[1,0,0],[]}); +end; + +EEG = eeg_checkset(EEG); + +% CHECK FOR IC CLASSIFICATION MARKS +%% Use the ICLabel plugin +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% ICLabel is a database style plugin that classifies ICA components into the +% following seven categories + +% (Brain)(Muscle)(Eye)(Heart)(Line Noise)(Channel Noise)(Other) +% ( 1 )( 2 )( 3 )( 4 )( 5 )( 6 )( 7 ) + +% Colour structure and groupings are as follows: +% ---------- +% Brain -> [0, 0.4, 0] +% ---------- +% Muscle -> [0.4, 0, 0] +% Eye -> [0.8, 0, 0] +% Heart -> [0.8, 0.4, 0] +% ---------- +% Line Noise -> [0.2, 0.4, 0.8] +% Channel noise -> [0.4, 0, 0.8] +% ---------- +% Other -> [0.8, 0.8, 0] +% ---------- + +% Create lookup table of the above information. +pairs = {'brain',[0, 0.4, 0]; +'muscle',[0.4,0,0]; +'eye',[0.8,0,0]; +'heart',[0.8,0.4,0]; +'line_noise',[0.2,0.4,0.8]; +'chan_noise',[0.4,0,0.8]; +'other',[.7 .4 0]}; +lookupTable = cell2struct(pairs, {'name', 'colour'}, 2); + +EEG = pop_marks_select_data(EEG,'time marks',[],'labels',{'manual'},'remove','on'); + +% Run ICLabel +%EEG = iclabel(EEG); + +% extract features +disp 'ICLabel: extracting features...' +features = ICL_feature_extractor(EEG, 40); + +% run ICL +disp 'ICLabel: calculating labels...' +labels = run_ICL('default',features{:}); + +% save into EEG +disp 'ICLabel: saving results...' +EEG.etc.ic_classification.ICLabel.classes = ... + {'Brain', 'Muscle', 'Eye', 'Heart', 'Line', 'Channel', 'Other'}; +EEG.etc.ic_classification.ICLabel.classifications = labels; + +tmp=EEG.etc.ic_classification; + +% Reload dataset: +EEG = pop_loadset('filepath','derivatives/BIDS-Lossless-EEG','filename','./sub-008/eeg/sub-008_task-faceFO_eeg_ll.set'); +EEG = eeg_checkset(EEG); + +EEG.etc.ic_classification=tmp; + +% Extract labels from the structure. +rawLabels = zeros([1,length(EEG.etc.ic_classification.ICLabel.classifications)]); +for index = 1:length(EEG.etc.ic_classification.ICLabel.classifications) + [p,i] = max(EEG.etc.ic_classification.ICLabel.classifications(index,1:end-1)); + if p<.3; + i=7; + end + rawLabels(index) = i; +end + +%ICLabel Exporting to marks +%%%%%%%%%%%%%%%%%%%%%%%%%%%% +logging_log('INFO','UPDATE MARKS STRUCTURE WITH ICLABEL BRAIN COMPS...'); +for compStage = 1:7 + ICLabel_pass= zeros([1,length(rawLabels)]); + ICLabel_pass(find(rawLabels==compStage)) = 1; + ICLabel_pass = ICLabel_pass'; + EEG.marks=marks_add_label(EEG.marks,'comp_info', ... + {lookupTable(compStage).name,lookupTable(compStage).colour, ... + lookupTable(compStage).colour,1,ICLabel_pass}); +end +% make any brain components always on top: +EEG.marks.comp_info(1,find(strcmp({EEG.marks.comp_info.label},'brain'))).order=2; + + +%RESET/INITIATE CHAN MARKS... +manual_ind=find(strcmp('manual',{EEG.marks.chan_info.label})); + + +%RESET/INITIATE IC MARKS... +EEG.marks=marks_add_label(EEG.marks,'comp_info',{'ambig',[.3,.3,1],[.3,.3,1],3,zeros(min(size(EEG.icaweights)),1)}); +manual_ind=find(strcmp('manual',{EEG.marks.comp_info.label})); +ch_s_sd_ind=find(strcmp('ch_s_sd',{EEG.marks.chan_info.label})); +ch_sd_ind=find(strcmp('ch_sd',{EEG.marks.chan_info.label})); +low_r_ind=find(strcmp('low_r',{EEG.marks.chan_info.label})); +bridge_ind=find(strcmp('bridge',{EEG.marks.chan_info.label})); +rank_ind=find(strcmp('rank',{EEG.marks.chan_info.label})); +icrt_ind=find(strcmp('ic_rt',{EEG.marks.comp_info.label})); +brain_ind=find(strcmp('brain',{EEG.marks.comp_info.label})); +muscle_ind=find(strcmp('muscle',{EEG.marks.comp_info.label})); +eye_ind=find(strcmp('eye',{EEG.marks.comp_info.label})); +heart_ind=find(strcmp('heart',{EEG.marks.comp_info.label})); +line_noise_ind=find(strcmp('line_noise',{EEG.marks.comp_info.label})); +chan_noise_ind=find(strcmp('chan_noise',{EEG.marks.comp_info.label})); +other_ind=find(strcmp('other',{EEG.marks.comp_info.label})); +ambig_ind=find(strcmp('ambig',{EEG.marks.comp_info.label})); + +EEG.marks.comp_info(manual_ind).order=1; +if ~isempty(ch_s_sd_ind); % skip study-wide criteria if not used + EEG.marks.comp_info(ch_s_sd_ind).order=2; +end; +EEG.marks.comp_info(ch_sd_ind).order=2; +EEG.marks.comp_info(low_r_ind).order=2; +EEG.marks.comp_info(bridge_ind).order=2; +EEG.marks.comp_info(rank_ind).order=2; +EEG.marks.comp_info(icrt_ind).order=1; + +EEG.marks.comp_info(brain_ind).order=2; +EEG.marks.comp_info(muscle_ind).order=2; +EEG.marks.comp_info(eye_ind).order=2; +EEG.marks.comp_info(heart_ind).order=2; +EEG.marks.comp_info(line_noise_ind).order=2; +EEG.marks.comp_info(chan_noise_ind).order=2; +EEG.marks.comp_info(other_ind).order=2; +EEG.marks.comp_info(ambig_ind).order=3; + +bioa_inds = marks_label2index(EEG.marks.comp_info,{'muscle','eye','heart','chan_noise'},'indexes'); +EEG.marks.comp_info(manual_ind).flags=zeros(min(size(EEG.icaweights)),1); +EEG.marks.comp_info(manual_ind).flags(bioa_inds)=1; + +% BIDS Export calls +% Dot for path is on purpose. Don't worry - it's safe. +fName = './derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_eeg.edf'; +pop_writeeeg(EEG, fName, 'TYPE','EDF'); +dlmwrite(strrep(fName,'_eeg.edf','_icaweights.tsv'),EEG.icaweights,'\t'); +dlmwrite(strrep(fName,'_eeg.edf','_icasphere.tsv'),EEG.icasphere,'\t'); +s = {}; +s.algorithm = 'amica15'; +s.icachansind = EEG.icachansind; +s.intendedFor = fName; +savejson('',s,strrep(fName,'_eeg.edf','_icaweights.json')); +export_anno(EEG,fName,''); + +% ICLabel quick export +iclFname = strrep(fName,'_eeg.edf','_iclabel.mat'); +save(iclFname, 'tmp'); % see above ICLabel section for tmp definition diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/s05_concat_data_sub_009_task_faceFO_eeg.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/s05_concat_data_sub_009_task_faceFO_eeg.m new file mode 100644 index 000000000..d93d95e4d --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/s05_concat_data_sub_009_task_faceFO_eeg.m @@ -0,0 +1,678 @@ +warning off; + +pkg load signal; +pkg load parallel; +pkg load io; + +file_path = fileparts(mfilename('fullpath')); + +addpath([file_path '/../../code/dependencies/eeglab_lossless'], ... + [file_path '/../../code/dependencies/matlog'], ... + [file_path '/../../code/tools']); +cd '/scratch/tk11br/Face13/'; + +% eeglab auto loads the path correctly instead of shotgun blasting with +% addpath(genpath(<>)) as we were doing prior +if ~exist('EEG', 'var') + eeglab; +end +% Use matlog disp function override +logging_override_disp(true); +% Use matlog INFO level debugging +logging_set_log_level('INFO') + +% Don't run with traditional but enable some of the options +save_default_options ('-mat-binary'); +confirm_recursive_rmdir(false); + +% This makes | and & || and && in earlier version of Octave where +% | and & dont short curcuit by default +if exist('do_braindead_shortcircuit_evaluation', 'builtin') + do_braindead_shortcircuit_evaluation(true); +end + +% Options +pop_editoptions('option_savetwofiles', 1,'option_single',0); + +%% SCRIPT DESCRIPTION +% This script combineds the results of the amicas A,B and C. It takes this information and using ICAtest it determines which components were +% and were not replicated across and flags them, along with time sections with high standard deviation. Beta,theta and Alpha power is calculated +% arcoss time and is marked but not primed for removal. +% +%% From Config key_strswap Description +%----------------------------------------------------------------------------------------------------------------------------- +% in_path = derivatives/BIDS-Lossless-EEG Relative path to input data files assuming cd = work_path +% recur_sec = 1 Recurrence (sec) for the current artifact detection epoching (e.g. 1) +% limit_sec = [0 1] Limits (sec) for the current artifact detection epoching (e.g. [0 1]) +% sd_t_meth = q Method used for flagging epochs (e.g. 'q' (quantiles), or 'na' (default)) +% sd_t_vals = [.3 .7] Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7]) +% sd_t_o = 6 z threshold for flagging epochs during standard deviation criteria (e.g. 6) +% sd_t_f_meth = fixed Fixed method used for flagging epochs (should be 'fixed') +% sd_t_f_vals = Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% sd_t_f_o = .2 z threshold for flagging epochs during fixed standard deviation criteria (e.g. .2) +% min_gap_ms = 2000 Minimum time (ms) to allow between periods marked for rejection +% icr_crit = 0.05 False discovery rate of component replicatablilty, measure of sensitivity % +% dip_vol = derivatives/BIDS-Lossless-EEG/code/misc/standard_vol.mat file path/name of the standard volume file used in dipole fitting (e.g. derivatives/lossless/code/misc/standard_vol.mat). +% dip_mri = derivatives/BIDS-Lossless-EEG/code/misc/standard_mri.mat file path/name of the standard mri file used in dipole fitting (e.g. derivatives/lossless/code/misc/standard_mri.mat). +% dip_elc = derivatives/BIDS-Lossless-EEG/code/misc/standard_1020.elc file path/name of the standard electrode file used in dipole fitting (derivatives/lossless/code/misc/standard_1020.elc). +% dip_transmat = [0 0 0 0 0 0 1 1 1] Transformation matrix to apply during dipole fit (e.g. [0 0 0 0 0 0 1 1 1]) +% cor_transmat = [0,0,0,0,0,1.57,1,1,1] The transformation matrix to apply between dipole fitting to the standard head and performing ICLabel to the EEGLAB default orientation (e.g.[0,0,0,0,0,1.57,1,1,1]) +% cor_nosedir = +X Nose direction of montage to apply between dipole fitting to the standard head and performing ICLabel to the EEGLAB default orientation (e.g. +Y) +% out_path = derivatives/BIDS-Lossless-EEG Relative path to output data files assuming cd = work_path + +%% LOAD COMPART DATASET +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Loads the data file and the first amica model. + +logging_log('INFO', 'Loading set file: ./sub-009/eeg/sub-009_task-faceFO_compart_data.set...'); +tic; +EEG = pop_loadset('filepath','derivatives/BIDS-Lossless-EEG','filename','./sub-009/eeg/sub-009_task-faceFO_compart_data.set'); +EEG = eeg_checkset( EEG ); +logging_log('INFO', 'TIME TO: LOAD DATASET...'); +toc + +% Set up the ICA channels +EEG.icachansind = marks_label2index(EEG.marks.chan_info,{'manual','rank'},'indices','invert','on'); + +% Load Amica A +logging_log('INFO', 'Loading Amica ABC...'); +tic; +EEG.amica(2).models=loadmodout15('derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_amicaout_A'); +EEG.icaweights=EEG.amica(2).models.W; +EEG.icasphere=EEG.amica(2).models.S(1:EEG.amica(2).models.num_pcs,:); + +%% PASS EEG DATA THROUGH THE AMICA MATRIX +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Loads the B and C amica models. Amica A is used to fill in the ICA information. + +% Instead of EEG=eeg_checkset(EEG); +tmpdata = EEG.data(EEG.icachansind,:); +tmpindices = find(~sum(isnan(tmpdata))); +EEG.icaact = zeros(size(EEG.icaweights,1), size(tmpdata,2)); EEG.icaact(:) = NaN; +EEG.icaact(:,tmpindices) = (EEG.icaweights*EEG.icasphere)*tmpdata(:,tmpindices); +clear tmpdata; + +% Create winv for topo plots manually +EEG.icawinv = []; +EEG.icawinv = pinv(EEG.icaweights * EEG.icasphere); + +% Load Amica B +EEG.amica(3).models=loadmodout15('derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_amicaout_B'); +% Load Amica C +EEG.amica(4).models=loadmodout15('derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_amicaout_C'); +logging_log('INFO', 'TIME TO: LOAD ICA ABC INFO...'); +toc + +%% UPDATE REJECTION STRUCTURE... +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Creating flag marks to based on the log liklyhodd models found for each of the ICA models + +% Flags for amicas A B and C +logging_log('INFO', 'Updating latflaginfo structure...'); +tic; + +% A +logl_A_flags=zeros(size(EEG.data(1,:))); +logl_A_flag_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indices','invert','on'); + +% Check to see if the overwrite will be successful. If the overwirte is a different size then some of the old data will be retained. +if length(EEG.amica(2).models.Lt) ~= length(logl_A_flag_inds) + EEG.amica(2).models.Lt = EEG.amica(2).models.Lt(1:length(logl_A_flag_inds)); + warning('YOU FORGOT TO DELETE YOUR OLD AMICA FILES AND THE NEW CHANNEL SIZES DO NOT MATCH UP'); +end + +logl_A_flags(logl_A_flag_inds)=(EEG.amica(2).models.Lt-min(EEG.amica(2).models.Lt))/(max(EEG.amica(2).models.Lt)-min(EEG.amica(2).models.Lt)); +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'logl_A',[1,0.45,0],logl_A_flags}); +clear logl_A_flags; +% B +logl_B_flags=zeros(size(EEG.data(1,:))); +logl_B_flag_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indices','invert','on'); +logl_B_flags(logl_B_flag_inds)=(EEG.amica(3).models.Lt-min(EEG.amica(3).models.Lt))/(max(EEG.amica(3).models.Lt)-min(EEG.amica(3).models.Lt)); +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'logl_B',[1,0.45,0],logl_B_flags}); +clear logl_B_flags; +% C +logl_C_flags=zeros(size(EEG.data(1,:))); +logl_C_flag_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indices','invert','on'); +logl_C_flags(logl_C_flag_inds)=(EEG.amica(4).models.Lt-min(EEG.amica(4).models.Lt))/(max(EEG.amica(4).models.Lt)-min(EEG.amica(4).models.Lt)); +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'logl_C',[1,0.45,0],logl_C_flags}); +clear logl_C_flags; + +logging_log('INFO', 'TIME TO: UPDATE REJECTION STRUCTURE...'); +toc + +%% IDENTIFY RELIABLE COMPONENTS USING ISCTEST +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Gives the A variable of each of the amicas to the function icatest. Returned are the indecies of the +% Components that were not replicated across all 3 of the models. + +tic; +% Clear a in case of local models are saved already +a = []; +for i=2:length(EEG.amica); + a(:,:,i-1)=EEG.amica(i).models(1).A; +end + +% Calculating the replicated component flag +% Replicate criteria changed from 0.05,0.05 to 0.1,0.1 to be less sensitive +ca=isctest(a,0.05,0.05,'mixing'); +if isempty(ca) + mess = ['ISCTest failed to return reasonable values. ISCTest is ' ... + 'meant to find duplicate channels in amica_{A,B,C} if this ' ... + 'failed consider investigating the datafile']; + logging_log('ERROR', mess); + error(mess); +end +icr_inds=setdiff(1:min(size(EEG.amica(2).models(1).A)),ca(:,1)); + +% Creating the replicated component flag +logging_log('INFO', 'Updating chflaginfo structure...'); +icr_comp_flags=zeros(min(size(EEG.amica(2).models(1).A)),1); +icr_comp_flags(icr_inds)=1; +EEG.marks=marks_add_label(EEG.marks,'comp_info', ... + {'ic_rt',[0,0,1],[0,0,1],-1,icr_comp_flags}); +logging_log('INFO', 'TIME TO: EDIT FIND REPLICATED COMPONENTS AND UPDATE COMPINFO STRUCT...'); +toc + +% Window the continuous data +logging_log('INFO', 'Windowing the continous data...'); +tic; +EEG=marks_continuous2epochs(EEG,'recurrence',1,'limits',[0 1]); +logging_log('INFO', 'TIME TO: WINDOW THE CONTINUOUS DATA...'); +toc + +%% CALCULATE ICAACT SD +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Similarly to the IC_SD flag created in compart. Marks sections of time where the components +% have an abnormally high standard deviation. + +% Calculating the replicated component flag +logging_log('INFO', 'Calculating the IC sd array for window criteria...'); +tic; +epoch_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indexes','invert','on'); +[EEG,icaact_sd2_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'varmeasure','absmean', ... + 'detrend','off', ... + 'plot_figs','off'); + +% Creating the ic_sd component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_sd_t_inds]=marks_array2flags(icaact_sd2_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); + +icsd_epoch_flags=zeros(size(EEG.data(1,:,:))); +icsd_epoch_flags(1,:,epoch_inds(flag_sd_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_sd2',[.4,.6,.6],icsd_epoch_flags}); +logging_log('INFO', 'TIME TO: EDIT FIND IC_SD AND UPDATE TIMEINFO REJECTION STRUCT...'); +toc +clear icsd_epoch_flags; + +%% CALCULATE DELTA/THETA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high Theta/Alpha Power + +% Calculating the Delta/Theta component flag +logging_log('INFO', 'Calculating the delta/theta IC power array for window criteria...'); +tic; +[EEG,icaact_dt_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[0 7], ... + 'plot_figs','off'); + +% Creating the Delta/Theta component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_dt_t_inds]=marks_array2flags(icaact_dt_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +icdt_epoch_flags=zeros(size(EEG.data(1,:,:))); +icdt_epoch_flags(1,:,epoch_inds(flag_dt_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_dt',[1,1,0],icdt_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE DELTA/THETA ICAACT POWER AND UPDATE MARKS...'); +toc +clear icdt_epoch_flags; + +%% CALCULATE ALPHA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high Alpha Power + +% Calculating the Alpha component flag +logging_log('INFO', 'Calculating the alpha IC power array for window criteria...'); +tic; +[EEG,icaact_a_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[8 13], ... + 'plot_figs','off'); + +% Creating the Alpha component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_a_t_inds]=marks_array2flags(icaact_a_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +ica_epoch_flags=zeros(size(EEG.data(1,:,:))); +ica_epoch_flags(1,:,epoch_inds(flag_a_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_a',[1,1,0],ica_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE ALPHA ICAACT POWER AND UPDATE MARKS...'); +toc +clear ica_epoch_flags; + +%% CALCULATE BETA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high Beta Power. +% Note that at the moment this mark is used for removals. + +% Calculating the Beta component flag +logging_log('INFO', 'Calculating the beta IC power array for window criteria...'); +tic; +[EEG,icaact_b_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[14 30], ... + 'plot_figs','off'); +logging_log('INFO', 'TIME TO: CALCULATE BETA ICAACT POWER...'); + +% Creating the Beta component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_b_t_inds]=marks_array2flags(icaact_b_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +logging_log('INFO', 'TIME TO: CREATE WINDOW CRITERIA VECTOR...'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +icb_epoch_flags=zeros(size(EEG.data(1,:,:))); +icb_epoch_flags(1,:,epoch_inds(flag_b_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_b',[0,1,1],icb_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE BETA ICAACT POWER AND UPDATE MARKS...'); +toc +clear icb_epoch_flags; + +%% CALCULATE LOW GAMMA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high Low-Gamma Power. +% Note that at the moment this mark is used for removals. + +% Calculating the Beta component flag +logging_log('INFO', 'Calculating the low-gamma IC power array for window criteria...'); +tic; +[EEG,icaact_lg_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[31 60], ... + 'plot_figs','off'); +logging_log('INFO', 'TIME TO: CALCULATE LOW-GAMMA ICAACT POWER...'); + +% Creating the LOW-GAMMA component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_lg_t_inds]=marks_array2flags(icaact_lg_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +logging_log('INFO', 'TIME TO: CREATE WINDOW CRITERIA VECTOR...'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +iclg_epoch_flags=zeros(size(EEG.data(1,:,:))); +iclg_epoch_flags(1,:,epoch_inds(flag_lg_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_lg',[0,1,1],iclg_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE LOW-GAMMA ICAACT POWER AND UPDATE MARKS...'); +toc +clear iclg_epoch_flags; + + +%% CALCULATE HIGH GAMMA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high High-Gamma Power. +% Note that at the moment this mark is used for removals. + +% Calculating the Beta component flag +logging_log('INFO', 'Calculating the high-gamma IC power array for window criteria...'); +tic; +[EEG,icaact_hg_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[61 100], ... + 'plot_figs','off'); +logging_log('INFO', 'TIME TO: CALCULATE HIGH-GAMMA ICAACT POWER...'); + +% Creating the HIGH-GAMMA component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_hg_t_inds]=marks_array2flags(icaact_hg_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +logging_log('INFO', 'TIME TO: CREATE WINDOW CRITERIA VECTOR...'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +ichg_epoch_flags=zeros(size(EEG.data(1,:,:))); +ichg_epoch_flags(1,:,epoch_inds(flag_hg_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_hg',[0,1,1],ichg_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE HIGH-GAMMA ICAACT POWER AND UPDATE MARKS...'); +toc +clear ichg_epoch_flags; + +%% MARKS UPDATE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Merges the epochs back to continuous data. Flags marks gaps and combines time +% the new time marks into the manual marks. + +% Concat epochs back into continuous data +logging_log('INFO', 'Concatenating windowed data...'); +tic; +EEG=marks_epochs2continuous(EEG); +EEG=eeg_checkset(EEG,'eventconsistency'); +logging_log('INFO', 'TIME TO: CONCATENATE THE WINDOWED DATA INTO CONTINUOUS DATA...'); +toc + +% Mark flag gaps between time marks +tic; +EEG=pop_marks_flag_gap(EEG,{'manual','mark_gap','ic_sd2'}, ... + 2000,'mark_gap',[.8,.8,.8],'offsets',[0 0],'ref_point','both'); +logging_log('INFO', 'TIME TO: MARK FLAG GAPS THAT ARE < 2 SECONDS...'); +toc + +% Combine the removal marks into manual +tic; +EEG=pop_marks_merge_labels(EEG,'time_info',{'manual','ic_sd2','mark_gap'},'target_label','manual'); +logging_log('INFO', 'TIME TO: COMBINE MARKS STRUCTURE INTO MANUAL FLAGS...'); +toc + +% begin prep for dipole fit +logging_log('INFO','Checking for JVM...'); +javachk('jvm'); + +% TODO(mae) this isn't added for some reason +addpath([pwd '/derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/Fieldtrip-lite141209/src']) + +%% DIPFIT +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Does the dipole fitting procedures. This takes a substantial amount of +% time. +% TODO: Make sure this is all needed, seems to be repeating things +logging_log('INFO','FITTING DIPOLES...'); +tic; +EEG = pop_dipfit_settings( EEG, 'hdmfile','derivatives/BIDS-Lossless-EEG/code/misc/standard_vol.mat', ... + 'coordformat','MNI', ... + 'mrifile','derivatives/BIDS-Lossless-EEG/code/misc/standard_mri.mat', ... + 'chanfile','derivatives/BIDS-Lossless-EEG/code/misc/standard_1020.elc', ... + 'coord_transform',[0 0 0 0 0 0 1 1 1], ... + 'chansel',[1:EEG.nbchan] ); + +% OLD SINGLE THREADED SETUP +% EEG = pop_multifit(EEG, [1:EEG.nbchan] ,'threshold',100,'plotopt',{'normlen' 'on'}); + +% NEW MULTI-THREADED SETUP +EEG = pop_multifit_bucanl_parallel(EEG, [1:EEG.nbchan], 8,'threshold',100,'plotopt',{'normlen' 'on'}); + +EEG=eeg_checkset(EEG); +logging_log('INFO','TIME TO: FIT DIPOLES...'); +toc + +%% MARK ICs FOR REJECTION BASED ON RV +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Creates marks based on dipfit results. Dipfit marks are added +% for rejection while ICMARC are just for reference. +% IC RESIDUAL VARIANCE FLAG +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Residuals greater than 15% are flagged - can be done here as it only references +% dipoles and their values. +%logging_log('INFO','UPDATE MARKS STRUCTURE WITH IC_RV...'); +%tic; +% Dipole criteria changed from 0.15 to 0.2 to be less sensitive +%EEG.reject.gcompreject(find([EEG.dipfit.model.rv]>.15))=1; +%EEG.reject.gcompreject(find(isnan([EEG.dipfit.model.rv])))=1; +%EEG.marks=marks_add_label(EEG.marks,'comp_info', ... +% {'ic_rv',[1,0,0],[1,0,0],-1,EEG.reject.gcompreject'}); +%logging_log('INFO','TIME TO: UPDATE MARKS STRUCTURE...'); +%toc + +% COMBINE MARKS STRUCTURE INTO MANUAL FLAGS... +%tic; +%EEG=pop_marks_merge_labels(EEG,'comp_info',{'manual','ic_rv'},'target_label','manual'); +%logging_log('INFO','TIME TO: COMBINE MARKS STRUCTURE INTO MANUAL FLAGS...'); +%toc + +% Adjust montage +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% This will pretty much always be dataset specific - so be careful +EEG = warp_locs( EEG, 'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020.elc', ... + 'transform',[0,0,0,0,0,1.57,1,1,1], ... + 'manual','off'); +EEG.chaninfo.nosedir='+X'; +EEG=eeg_checkset(EEG); + +%% SAVE LOSSLESS DERIVATIVE *_eeg_ll.set FILE (same name as the input file from the +% BIDS root directory [but located in the derivatives/lossless directory]). +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +logging_log('INFO','Saving set file: derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_eeg_ll.set...'); +tic; +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_eeg_ll.set'); +logging_log('INFO','TIME TO: SAVE DIP DATA SET...'); +toc + +% Save diagnostic arrays +try OCTAVE_VERSION; + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_icaact_sd2_t.mat','icaact_sd2_t'); +catch + save('derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_icaact_sd2_t.mat','icaact_sd2_t'); +end + +logging_log('INFO', 'Scheduler: sbatch'); +print_chan_sample(EEG); + + +if ~find(strcmpi({EEG.marks.chan_info.label},'ch_s_sd')); + EEG.marks=marks_add_label(EEG.marks,'chan_info',{'ch_s_sd',[1,0,0],[]}); +end; + +EEG = eeg_checkset(EEG); + +% CHECK FOR IC CLASSIFICATION MARKS +%% Use the ICLabel plugin +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% ICLabel is a database style plugin that classifies ICA components into the +% following seven categories + +% (Brain)(Muscle)(Eye)(Heart)(Line Noise)(Channel Noise)(Other) +% ( 1 )( 2 )( 3 )( 4 )( 5 )( 6 )( 7 ) + +% Colour structure and groupings are as follows: +% ---------- +% Brain -> [0, 0.4, 0] +% ---------- +% Muscle -> [0.4, 0, 0] +% Eye -> [0.8, 0, 0] +% Heart -> [0.8, 0.4, 0] +% ---------- +% Line Noise -> [0.2, 0.4, 0.8] +% Channel noise -> [0.4, 0, 0.8] +% ---------- +% Other -> [0.8, 0.8, 0] +% ---------- + +% Create lookup table of the above information. +pairs = {'brain',[0, 0.4, 0]; +'muscle',[0.4,0,0]; +'eye',[0.8,0,0]; +'heart',[0.8,0.4,0]; +'line_noise',[0.2,0.4,0.8]; +'chan_noise',[0.4,0,0.8]; +'other',[.7 .4 0]}; +lookupTable = cell2struct(pairs, {'name', 'colour'}, 2); + +EEG = pop_marks_select_data(EEG,'time marks',[],'labels',{'manual'},'remove','on'); + +% Run ICLabel +%EEG = iclabel(EEG); + +% extract features +disp 'ICLabel: extracting features...' +features = ICL_feature_extractor(EEG, 40); + +% run ICL +disp 'ICLabel: calculating labels...' +labels = run_ICL('default',features{:}); + +% save into EEG +disp 'ICLabel: saving results...' +EEG.etc.ic_classification.ICLabel.classes = ... + {'Brain', 'Muscle', 'Eye', 'Heart', 'Line', 'Channel', 'Other'}; +EEG.etc.ic_classification.ICLabel.classifications = labels; + +tmp=EEG.etc.ic_classification; + +% Reload dataset: +EEG = pop_loadset('filepath','derivatives/BIDS-Lossless-EEG','filename','./sub-009/eeg/sub-009_task-faceFO_eeg_ll.set'); +EEG = eeg_checkset(EEG); + +EEG.etc.ic_classification=tmp; + +% Extract labels from the structure. +rawLabels = zeros([1,length(EEG.etc.ic_classification.ICLabel.classifications)]); +for index = 1:length(EEG.etc.ic_classification.ICLabel.classifications) + [p,i] = max(EEG.etc.ic_classification.ICLabel.classifications(index,1:end-1)); + if p<.3; + i=7; + end + rawLabels(index) = i; +end + +%ICLabel Exporting to marks +%%%%%%%%%%%%%%%%%%%%%%%%%%%% +logging_log('INFO','UPDATE MARKS STRUCTURE WITH ICLABEL BRAIN COMPS...'); +for compStage = 1:7 + ICLabel_pass= zeros([1,length(rawLabels)]); + ICLabel_pass(find(rawLabels==compStage)) = 1; + ICLabel_pass = ICLabel_pass'; + EEG.marks=marks_add_label(EEG.marks,'comp_info', ... + {lookupTable(compStage).name,lookupTable(compStage).colour, ... + lookupTable(compStage).colour,1,ICLabel_pass}); +end +% make any brain components always on top: +EEG.marks.comp_info(1,find(strcmp({EEG.marks.comp_info.label},'brain'))).order=2; + + +%RESET/INITIATE CHAN MARKS... +manual_ind=find(strcmp('manual',{EEG.marks.chan_info.label})); + + +%RESET/INITIATE IC MARKS... +EEG.marks=marks_add_label(EEG.marks,'comp_info',{'ambig',[.3,.3,1],[.3,.3,1],3,zeros(min(size(EEG.icaweights)),1)}); +manual_ind=find(strcmp('manual',{EEG.marks.comp_info.label})); +ch_s_sd_ind=find(strcmp('ch_s_sd',{EEG.marks.chan_info.label})); +ch_sd_ind=find(strcmp('ch_sd',{EEG.marks.chan_info.label})); +low_r_ind=find(strcmp('low_r',{EEG.marks.chan_info.label})); +bridge_ind=find(strcmp('bridge',{EEG.marks.chan_info.label})); +rank_ind=find(strcmp('rank',{EEG.marks.chan_info.label})); +icrt_ind=find(strcmp('ic_rt',{EEG.marks.comp_info.label})); +brain_ind=find(strcmp('brain',{EEG.marks.comp_info.label})); +muscle_ind=find(strcmp('muscle',{EEG.marks.comp_info.label})); +eye_ind=find(strcmp('eye',{EEG.marks.comp_info.label})); +heart_ind=find(strcmp('heart',{EEG.marks.comp_info.label})); +line_noise_ind=find(strcmp('line_noise',{EEG.marks.comp_info.label})); +chan_noise_ind=find(strcmp('chan_noise',{EEG.marks.comp_info.label})); +other_ind=find(strcmp('other',{EEG.marks.comp_info.label})); +ambig_ind=find(strcmp('ambig',{EEG.marks.comp_info.label})); + +EEG.marks.comp_info(manual_ind).order=1; +if ~isempty(ch_s_sd_ind); % skip study-wide criteria if not used + EEG.marks.comp_info(ch_s_sd_ind).order=2; +end; +EEG.marks.comp_info(ch_sd_ind).order=2; +EEG.marks.comp_info(low_r_ind).order=2; +EEG.marks.comp_info(bridge_ind).order=2; +EEG.marks.comp_info(rank_ind).order=2; +EEG.marks.comp_info(icrt_ind).order=1; + +EEG.marks.comp_info(brain_ind).order=2; +EEG.marks.comp_info(muscle_ind).order=2; +EEG.marks.comp_info(eye_ind).order=2; +EEG.marks.comp_info(heart_ind).order=2; +EEG.marks.comp_info(line_noise_ind).order=2; +EEG.marks.comp_info(chan_noise_ind).order=2; +EEG.marks.comp_info(other_ind).order=2; +EEG.marks.comp_info(ambig_ind).order=3; + +bioa_inds = marks_label2index(EEG.marks.comp_info,{'muscle','eye','heart','chan_noise'},'indexes'); +EEG.marks.comp_info(manual_ind).flags=zeros(min(size(EEG.icaweights)),1); +EEG.marks.comp_info(manual_ind).flags(bioa_inds)=1; + +% BIDS Export calls +% Dot for path is on purpose. Don't worry - it's safe. +fName = './derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_eeg.edf'; +pop_writeeeg(EEG, fName, 'TYPE','EDF'); +dlmwrite(strrep(fName,'_eeg.edf','_icaweights.tsv'),EEG.icaweights,'\t'); +dlmwrite(strrep(fName,'_eeg.edf','_icasphere.tsv'),EEG.icasphere,'\t'); +s = {}; +s.algorithm = 'amica15'; +s.icachansind = EEG.icachansind; +s.intendedFor = fName; +savejson('',s,strrep(fName,'_eeg.edf','_icaweights.json')); +export_anno(EEG,fName,''); + +% ICLabel quick export +iclFname = strrep(fName,'_eeg.edf','_iclabel.mat'); +save(iclFname, 'tmp'); % see above ICLabel section for tmp definition diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/s05_concat_data_sub_010_task_faceFO_eeg.m b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/s05_concat_data_sub_010_task_faceFO_eeg.m new file mode 100644 index 000000000..b91ce0807 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/s05_concat_data_sub_010_task_faceFO_eeg.m @@ -0,0 +1,678 @@ +warning off; + +pkg load signal; +pkg load parallel; +pkg load io; + +file_path = fileparts(mfilename('fullpath')); + +addpath([file_path '/../../code/dependencies/eeglab_lossless'], ... + [file_path '/../../code/dependencies/matlog'], ... + [file_path '/../../code/tools']); +cd '/scratch/tk11br/Face13/'; + +% eeglab auto loads the path correctly instead of shotgun blasting with +% addpath(genpath(<>)) as we were doing prior +if ~exist('EEG', 'var') + eeglab; +end +% Use matlog disp function override +logging_override_disp(true); +% Use matlog INFO level debugging +logging_set_log_level('INFO') + +% Don't run with traditional but enable some of the options +save_default_options ('-mat-binary'); +confirm_recursive_rmdir(false); + +% This makes | and & || and && in earlier version of Octave where +% | and & dont short curcuit by default +if exist('do_braindead_shortcircuit_evaluation', 'builtin') + do_braindead_shortcircuit_evaluation(true); +end + +% Options +pop_editoptions('option_savetwofiles', 1,'option_single',0); + +%% SCRIPT DESCRIPTION +% This script combineds the results of the amicas A,B and C. It takes this information and using ICAtest it determines which components were +% and were not replicated across and flags them, along with time sections with high standard deviation. Beta,theta and Alpha power is calculated +% arcoss time and is marked but not primed for removal. +% +%% From Config key_strswap Description +%----------------------------------------------------------------------------------------------------------------------------- +% in_path = derivatives/BIDS-Lossless-EEG Relative path to input data files assuming cd = work_path +% recur_sec = 1 Recurrence (sec) for the current artifact detection epoching (e.g. 1) +% limit_sec = [0 1] Limits (sec) for the current artifact detection epoching (e.g. [0 1]) +% sd_t_meth = q Method used for flagging epochs (e.g. 'q' (quantiles), or 'na' (default)) +% sd_t_vals = [.3 .7] Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7]) +% sd_t_o = 6 z threshold for flagging epochs during standard deviation criteria (e.g. 6) +% sd_t_f_meth = fixed Fixed method used for flagging epochs (should be 'fixed') +% sd_t_f_vals = Percentage trim for confidence intervals during epoch standard deviation criteria (e.g. [.3 .7], leave empty for 'fixed') +% sd_t_f_o = .2 z threshold for flagging epochs during fixed standard deviation criteria (e.g. .2) +% min_gap_ms = 2000 Minimum time (ms) to allow between periods marked for rejection +% icr_crit = 0.05 False discovery rate of component replicatablilty, measure of sensitivity % +% dip_vol = derivatives/BIDS-Lossless-EEG/code/misc/standard_vol.mat file path/name of the standard volume file used in dipole fitting (e.g. derivatives/lossless/code/misc/standard_vol.mat). +% dip_mri = derivatives/BIDS-Lossless-EEG/code/misc/standard_mri.mat file path/name of the standard mri file used in dipole fitting (e.g. derivatives/lossless/code/misc/standard_mri.mat). +% dip_elc = derivatives/BIDS-Lossless-EEG/code/misc/standard_1020.elc file path/name of the standard electrode file used in dipole fitting (derivatives/lossless/code/misc/standard_1020.elc). +% dip_transmat = [0 0 0 0 0 0 1 1 1] Transformation matrix to apply during dipole fit (e.g. [0 0 0 0 0 0 1 1 1]) +% cor_transmat = [0,0,0,0,0,1.57,1,1,1] The transformation matrix to apply between dipole fitting to the standard head and performing ICLabel to the EEGLAB default orientation (e.g.[0,0,0,0,0,1.57,1,1,1]) +% cor_nosedir = +X Nose direction of montage to apply between dipole fitting to the standard head and performing ICLabel to the EEGLAB default orientation (e.g. +Y) +% out_path = derivatives/BIDS-Lossless-EEG Relative path to output data files assuming cd = work_path + +%% LOAD COMPART DATASET +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Loads the data file and the first amica model. + +logging_log('INFO', 'Loading set file: ./sub-010/eeg/sub-010_task-faceFO_compart_data.set...'); +tic; +EEG = pop_loadset('filepath','derivatives/BIDS-Lossless-EEG','filename','./sub-010/eeg/sub-010_task-faceFO_compart_data.set'); +EEG = eeg_checkset( EEG ); +logging_log('INFO', 'TIME TO: LOAD DATASET...'); +toc + +% Set up the ICA channels +EEG.icachansind = marks_label2index(EEG.marks.chan_info,{'manual','rank'},'indices','invert','on'); + +% Load Amica A +logging_log('INFO', 'Loading Amica ABC...'); +tic; +EEG.amica(2).models=loadmodout15('derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_amicaout_A'); +EEG.icaweights=EEG.amica(2).models.W; +EEG.icasphere=EEG.amica(2).models.S(1:EEG.amica(2).models.num_pcs,:); + +%% PASS EEG DATA THROUGH THE AMICA MATRIX +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Loads the B and C amica models. Amica A is used to fill in the ICA information. + +% Instead of EEG=eeg_checkset(EEG); +tmpdata = EEG.data(EEG.icachansind,:); +tmpindices = find(~sum(isnan(tmpdata))); +EEG.icaact = zeros(size(EEG.icaweights,1), size(tmpdata,2)); EEG.icaact(:) = NaN; +EEG.icaact(:,tmpindices) = (EEG.icaweights*EEG.icasphere)*tmpdata(:,tmpindices); +clear tmpdata; + +% Create winv for topo plots manually +EEG.icawinv = []; +EEG.icawinv = pinv(EEG.icaweights * EEG.icasphere); + +% Load Amica B +EEG.amica(3).models=loadmodout15('derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_amicaout_B'); +% Load Amica C +EEG.amica(4).models=loadmodout15('derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_amicaout_C'); +logging_log('INFO', 'TIME TO: LOAD ICA ABC INFO...'); +toc + +%% UPDATE REJECTION STRUCTURE... +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Creating flag marks to based on the log liklyhodd models found for each of the ICA models + +% Flags for amicas A B and C +logging_log('INFO', 'Updating latflaginfo structure...'); +tic; + +% A +logl_A_flags=zeros(size(EEG.data(1,:))); +logl_A_flag_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indices','invert','on'); + +% Check to see if the overwrite will be successful. If the overwirte is a different size then some of the old data will be retained. +if length(EEG.amica(2).models.Lt) ~= length(logl_A_flag_inds) + EEG.amica(2).models.Lt = EEG.amica(2).models.Lt(1:length(logl_A_flag_inds)); + warning('YOU FORGOT TO DELETE YOUR OLD AMICA FILES AND THE NEW CHANNEL SIZES DO NOT MATCH UP'); +end + +logl_A_flags(logl_A_flag_inds)=(EEG.amica(2).models.Lt-min(EEG.amica(2).models.Lt))/(max(EEG.amica(2).models.Lt)-min(EEG.amica(2).models.Lt)); +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'logl_A',[1,0.45,0],logl_A_flags}); +clear logl_A_flags; +% B +logl_B_flags=zeros(size(EEG.data(1,:))); +logl_B_flag_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indices','invert','on'); +logl_B_flags(logl_B_flag_inds)=(EEG.amica(3).models.Lt-min(EEG.amica(3).models.Lt))/(max(EEG.amica(3).models.Lt)-min(EEG.amica(3).models.Lt)); +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'logl_B',[1,0.45,0],logl_B_flags}); +clear logl_B_flags; +% C +logl_C_flags=zeros(size(EEG.data(1,:))); +logl_C_flag_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indices','invert','on'); +logl_C_flags(logl_C_flag_inds)=(EEG.amica(4).models.Lt-min(EEG.amica(4).models.Lt))/(max(EEG.amica(4).models.Lt)-min(EEG.amica(4).models.Lt)); +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'logl_C',[1,0.45,0],logl_C_flags}); +clear logl_C_flags; + +logging_log('INFO', 'TIME TO: UPDATE REJECTION STRUCTURE...'); +toc + +%% IDENTIFY RELIABLE COMPONENTS USING ISCTEST +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Gives the A variable of each of the amicas to the function icatest. Returned are the indecies of the +% Components that were not replicated across all 3 of the models. + +tic; +% Clear a in case of local models are saved already +a = []; +for i=2:length(EEG.amica); + a(:,:,i-1)=EEG.amica(i).models(1).A; +end + +% Calculating the replicated component flag +% Replicate criteria changed from 0.05,0.05 to 0.1,0.1 to be less sensitive +ca=isctest(a,0.05,0.05,'mixing'); +if isempty(ca) + mess = ['ISCTest failed to return reasonable values. ISCTest is ' ... + 'meant to find duplicate channels in amica_{A,B,C} if this ' ... + 'failed consider investigating the datafile']; + logging_log('ERROR', mess); + error(mess); +end +icr_inds=setdiff(1:min(size(EEG.amica(2).models(1).A)),ca(:,1)); + +% Creating the replicated component flag +logging_log('INFO', 'Updating chflaginfo structure...'); +icr_comp_flags=zeros(min(size(EEG.amica(2).models(1).A)),1); +icr_comp_flags(icr_inds)=1; +EEG.marks=marks_add_label(EEG.marks,'comp_info', ... + {'ic_rt',[0,0,1],[0,0,1],-1,icr_comp_flags}); +logging_log('INFO', 'TIME TO: EDIT FIND REPLICATED COMPONENTS AND UPDATE COMPINFO STRUCT...'); +toc + +% Window the continuous data +logging_log('INFO', 'Windowing the continous data...'); +tic; +EEG=marks_continuous2epochs(EEG,'recurrence',1,'limits',[0 1]); +logging_log('INFO', 'TIME TO: WINDOW THE CONTINUOUS DATA...'); +toc + +%% CALCULATE ICAACT SD +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Similarly to the IC_SD flag created in compart. Marks sections of time where the components +% have an abnormally high standard deviation. + +% Calculating the replicated component flag +logging_log('INFO', 'Calculating the IC sd array for window criteria...'); +tic; +epoch_inds=marks_label2index(EEG.marks.time_info,{'manual'},'indexes','invert','on'); +[EEG,icaact_sd2_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'varmeasure','absmean', ... + 'detrend','off', ... + 'plot_figs','off'); + +% Creating the ic_sd component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_sd_t_inds]=marks_array2flags(icaact_sd2_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); + +icsd_epoch_flags=zeros(size(EEG.data(1,:,:))); +icsd_epoch_flags(1,:,epoch_inds(flag_sd_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_sd2',[.4,.6,.6],icsd_epoch_flags}); +logging_log('INFO', 'TIME TO: EDIT FIND IC_SD AND UPDATE TIMEINFO REJECTION STRUCT...'); +toc +clear icsd_epoch_flags; + +%% CALCULATE DELTA/THETA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high Theta/Alpha Power + +% Calculating the Delta/Theta component flag +logging_log('INFO', 'Calculating the delta/theta IC power array for window criteria...'); +tic; +[EEG,icaact_dt_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[0 7], ... + 'plot_figs','off'); + +% Creating the Delta/Theta component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_dt_t_inds]=marks_array2flags(icaact_dt_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +icdt_epoch_flags=zeros(size(EEG.data(1,:,:))); +icdt_epoch_flags(1,:,epoch_inds(flag_dt_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_dt',[1,1,0],icdt_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE DELTA/THETA ICAACT POWER AND UPDATE MARKS...'); +toc +clear icdt_epoch_flags; + +%% CALCULATE ALPHA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high Alpha Power + +% Calculating the Alpha component flag +logging_log('INFO', 'Calculating the alpha IC power array for window criteria...'); +tic; +[EEG,icaact_a_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[8 13], ... + 'plot_figs','off'); + +% Creating the Alpha component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_a_t_inds]=marks_array2flags(icaact_a_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +ica_epoch_flags=zeros(size(EEG.data(1,:,:))); +ica_epoch_flags(1,:,epoch_inds(flag_a_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_a',[1,1,0],ica_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE ALPHA ICAACT POWER AND UPDATE MARKS...'); +toc +clear ica_epoch_flags; + +%% CALCULATE BETA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high Beta Power. +% Note that at the moment this mark is used for removals. + +% Calculating the Beta component flag +logging_log('INFO', 'Calculating the beta IC power array for window criteria...'); +tic; +[EEG,icaact_b_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[14 30], ... + 'plot_figs','off'); +logging_log('INFO', 'TIME TO: CALCULATE BETA ICAACT POWER...'); + +% Creating the Beta component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_b_t_inds]=marks_array2flags(icaact_b_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +logging_log('INFO', 'TIME TO: CREATE WINDOW CRITERIA VECTOR...'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +icb_epoch_flags=zeros(size(EEG.data(1,:,:))); +icb_epoch_flags(1,:,epoch_inds(flag_b_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_b',[0,1,1],icb_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE BETA ICAACT POWER AND UPDATE MARKS...'); +toc +clear icb_epoch_flags; + +%% CALCULATE LOW GAMMA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high Low-Gamma Power. +% Note that at the moment this mark is used for removals. + +% Calculating the Beta component flag +logging_log('INFO', 'Calculating the low-gamma IC power array for window criteria...'); +tic; +[EEG,icaact_lg_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[31 60], ... + 'plot_figs','off'); +logging_log('INFO', 'TIME TO: CALCULATE LOW-GAMMA ICAACT POWER...'); + +% Creating the LOW-GAMMA component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_lg_t_inds]=marks_array2flags(icaact_lg_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +logging_log('INFO', 'TIME TO: CREATE WINDOW CRITERIA VECTOR...'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +iclg_epoch_flags=zeros(size(EEG.data(1,:,:))); +iclg_epoch_flags(1,:,epoch_inds(flag_lg_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_lg',[0,1,1],iclg_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE LOW-GAMMA ICAACT POWER AND UPDATE MARKS...'); +toc +clear iclg_epoch_flags; + + +%% CALCULATE HIGH GAMMA ICAACT POWER +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculates and flags times of high High-Gamma Power. +% Note that at the moment this mark is used for removals. + +% Calculating the Beta component flag +logging_log('INFO', 'Calculating the high-gamma IC power array for window criteria...'); +tic; +[EEG,icaact_hg_t]=chan_variance(EEG,'data_field','icaact', ... + 'epoch_inds',epoch_inds, ... + 'detrend','on', ... + 'varmeasure','spect', ... + 'spectrange',[61 100], ... + 'plot_figs','off'); +logging_log('INFO', 'TIME TO: CALCULATE HIGH-GAMMA ICAACT POWER...'); + +% Creating the HIGH-GAMMA component flag +logging_log('INFO', 'Assessing window icaact sd distributions...') +[~,flag_hg_t_inds]=marks_array2flags(icaact_hg_t, ... + 'flag_dim','col', ... + 'init_method','q', ... + 'init_vals',[.3 .7], ... + 'init_crit',6, ... + 'flag_method','fixed', ... + 'flag_vals',[ ], ... + 'flag_crit',.2, ... + 'plot_figs','off'); + +logging_log('INFO', 'TIME TO: CREATE WINDOW CRITERIA VECTOR...'); + +% Updating rejection structure +logging_log('INFO', 'Updating latflaginfo structure...'); +ichg_epoch_flags=zeros(size(EEG.data(1,:,:))); +ichg_epoch_flags(1,:,epoch_inds(flag_hg_t_inds))=1; +EEG.marks=marks_add_label(EEG.marks,'time_info', ... + {'ic_hg',[0,1,1],ichg_epoch_flags}); +logging_log('INFO', 'TIME TO: CALCULATE HIGH-GAMMA ICAACT POWER AND UPDATE MARKS...'); +toc +clear ichg_epoch_flags; + +%% MARKS UPDATE +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Merges the epochs back to continuous data. Flags marks gaps and combines time +% the new time marks into the manual marks. + +% Concat epochs back into continuous data +logging_log('INFO', 'Concatenating windowed data...'); +tic; +EEG=marks_epochs2continuous(EEG); +EEG=eeg_checkset(EEG,'eventconsistency'); +logging_log('INFO', 'TIME TO: CONCATENATE THE WINDOWED DATA INTO CONTINUOUS DATA...'); +toc + +% Mark flag gaps between time marks +tic; +EEG=pop_marks_flag_gap(EEG,{'manual','mark_gap','ic_sd2'}, ... + 2000,'mark_gap',[.8,.8,.8],'offsets',[0 0],'ref_point','both'); +logging_log('INFO', 'TIME TO: MARK FLAG GAPS THAT ARE < 2 SECONDS...'); +toc + +% Combine the removal marks into manual +tic; +EEG=pop_marks_merge_labels(EEG,'time_info',{'manual','ic_sd2','mark_gap'},'target_label','manual'); +logging_log('INFO', 'TIME TO: COMBINE MARKS STRUCTURE INTO MANUAL FLAGS...'); +toc + +% begin prep for dipole fit +logging_log('INFO','Checking for JVM...'); +javachk('jvm'); + +% TODO(mae) this isn't added for some reason +addpath([pwd '/derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/Fieldtrip-lite141209/src']) + +%% DIPFIT +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Does the dipole fitting procedures. This takes a substantial amount of +% time. +% TODO: Make sure this is all needed, seems to be repeating things +logging_log('INFO','FITTING DIPOLES...'); +tic; +EEG = pop_dipfit_settings( EEG, 'hdmfile','derivatives/BIDS-Lossless-EEG/code/misc/standard_vol.mat', ... + 'coordformat','MNI', ... + 'mrifile','derivatives/BIDS-Lossless-EEG/code/misc/standard_mri.mat', ... + 'chanfile','derivatives/BIDS-Lossless-EEG/code/misc/standard_1020.elc', ... + 'coord_transform',[0 0 0 0 0 0 1 1 1], ... + 'chansel',[1:EEG.nbchan] ); + +% OLD SINGLE THREADED SETUP +% EEG = pop_multifit(EEG, [1:EEG.nbchan] ,'threshold',100,'plotopt',{'normlen' 'on'}); + +% NEW MULTI-THREADED SETUP +EEG = pop_multifit_bucanl_parallel(EEG, [1:EEG.nbchan], 8,'threshold',100,'plotopt',{'normlen' 'on'}); + +EEG=eeg_checkset(EEG); +logging_log('INFO','TIME TO: FIT DIPOLES...'); +toc + +%% MARK ICs FOR REJECTION BASED ON RV +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Creates marks based on dipfit results. Dipfit marks are added +% for rejection while ICMARC are just for reference. +% IC RESIDUAL VARIANCE FLAG +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Residuals greater than 15% are flagged - can be done here as it only references +% dipoles and their values. +%logging_log('INFO','UPDATE MARKS STRUCTURE WITH IC_RV...'); +%tic; +% Dipole criteria changed from 0.15 to 0.2 to be less sensitive +%EEG.reject.gcompreject(find([EEG.dipfit.model.rv]>.15))=1; +%EEG.reject.gcompreject(find(isnan([EEG.dipfit.model.rv])))=1; +%EEG.marks=marks_add_label(EEG.marks,'comp_info', ... +% {'ic_rv',[1,0,0],[1,0,0],-1,EEG.reject.gcompreject'}); +%logging_log('INFO','TIME TO: UPDATE MARKS STRUCTURE...'); +%toc + +% COMBINE MARKS STRUCTURE INTO MANUAL FLAGS... +%tic; +%EEG=pop_marks_merge_labels(EEG,'comp_info',{'manual','ic_rv'},'target_label','manual'); +%logging_log('INFO','TIME TO: COMBINE MARKS STRUCTURE INTO MANUAL FLAGS...'); +%toc + +% Adjust montage +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% This will pretty much always be dataset specific - so be careful +EEG = warp_locs( EEG, 'derivatives/BIDS-Lossless-EEG/code/misc/standard_1020.elc', ... + 'transform',[0,0,0,0,0,1.57,1,1,1], ... + 'manual','off'); +EEG.chaninfo.nosedir='+X'; +EEG=eeg_checkset(EEG); + +%% SAVE LOSSLESS DERIVATIVE *_eeg_ll.set FILE (same name as the input file from the +% BIDS root directory [but located in the derivatives/lossless directory]). +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +logging_log('INFO','Saving set file: derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_eeg_ll.set...'); +tic; +EEG = pop_saveset( EEG, 'filename','derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_eeg_ll.set'); +logging_log('INFO','TIME TO: SAVE DIP DATA SET...'); +toc + +% Save diagnostic arrays +try OCTAVE_VERSION; + save('-mat7-binary','derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_icaact_sd2_t.mat','icaact_sd2_t'); +catch + save('derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_icaact_sd2_t.mat','icaact_sd2_t'); +end + +logging_log('INFO', 'Scheduler: sbatch'); +print_chan_sample(EEG); + + +if ~find(strcmpi({EEG.marks.chan_info.label},'ch_s_sd')); + EEG.marks=marks_add_label(EEG.marks,'chan_info',{'ch_s_sd',[1,0,0],[]}); +end; + +EEG = eeg_checkset(EEG); + +% CHECK FOR IC CLASSIFICATION MARKS +%% Use the ICLabel plugin +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% ICLabel is a database style plugin that classifies ICA components into the +% following seven categories + +% (Brain)(Muscle)(Eye)(Heart)(Line Noise)(Channel Noise)(Other) +% ( 1 )( 2 )( 3 )( 4 )( 5 )( 6 )( 7 ) + +% Colour structure and groupings are as follows: +% ---------- +% Brain -> [0, 0.4, 0] +% ---------- +% Muscle -> [0.4, 0, 0] +% Eye -> [0.8, 0, 0] +% Heart -> [0.8, 0.4, 0] +% ---------- +% Line Noise -> [0.2, 0.4, 0.8] +% Channel noise -> [0.4, 0, 0.8] +% ---------- +% Other -> [0.8, 0.8, 0] +% ---------- + +% Create lookup table of the above information. +pairs = {'brain',[0, 0.4, 0]; +'muscle',[0.4,0,0]; +'eye',[0.8,0,0]; +'heart',[0.8,0.4,0]; +'line_noise',[0.2,0.4,0.8]; +'chan_noise',[0.4,0,0.8]; +'other',[.7 .4 0]}; +lookupTable = cell2struct(pairs, {'name', 'colour'}, 2); + +EEG = pop_marks_select_data(EEG,'time marks',[],'labels',{'manual'},'remove','on'); + +% Run ICLabel +%EEG = iclabel(EEG); + +% extract features +disp 'ICLabel: extracting features...' +features = ICL_feature_extractor(EEG, 40); + +% run ICL +disp 'ICLabel: calculating labels...' +labels = run_ICL('default',features{:}); + +% save into EEG +disp 'ICLabel: saving results...' +EEG.etc.ic_classification.ICLabel.classes = ... + {'Brain', 'Muscle', 'Eye', 'Heart', 'Line', 'Channel', 'Other'}; +EEG.etc.ic_classification.ICLabel.classifications = labels; + +tmp=EEG.etc.ic_classification; + +% Reload dataset: +EEG = pop_loadset('filepath','derivatives/BIDS-Lossless-EEG','filename','./sub-010/eeg/sub-010_task-faceFO_eeg_ll.set'); +EEG = eeg_checkset(EEG); + +EEG.etc.ic_classification=tmp; + +% Extract labels from the structure. +rawLabels = zeros([1,length(EEG.etc.ic_classification.ICLabel.classifications)]); +for index = 1:length(EEG.etc.ic_classification.ICLabel.classifications) + [p,i] = max(EEG.etc.ic_classification.ICLabel.classifications(index,1:end-1)); + if p<.3; + i=7; + end + rawLabels(index) = i; +end + +%ICLabel Exporting to marks +%%%%%%%%%%%%%%%%%%%%%%%%%%%% +logging_log('INFO','UPDATE MARKS STRUCTURE WITH ICLABEL BRAIN COMPS...'); +for compStage = 1:7 + ICLabel_pass= zeros([1,length(rawLabels)]); + ICLabel_pass(find(rawLabels==compStage)) = 1; + ICLabel_pass = ICLabel_pass'; + EEG.marks=marks_add_label(EEG.marks,'comp_info', ... + {lookupTable(compStage).name,lookupTable(compStage).colour, ... + lookupTable(compStage).colour,1,ICLabel_pass}); +end +% make any brain components always on top: +EEG.marks.comp_info(1,find(strcmp({EEG.marks.comp_info.label},'brain'))).order=2; + + +%RESET/INITIATE CHAN MARKS... +manual_ind=find(strcmp('manual',{EEG.marks.chan_info.label})); + + +%RESET/INITIATE IC MARKS... +EEG.marks=marks_add_label(EEG.marks,'comp_info',{'ambig',[.3,.3,1],[.3,.3,1],3,zeros(min(size(EEG.icaweights)),1)}); +manual_ind=find(strcmp('manual',{EEG.marks.comp_info.label})); +ch_s_sd_ind=find(strcmp('ch_s_sd',{EEG.marks.chan_info.label})); +ch_sd_ind=find(strcmp('ch_sd',{EEG.marks.chan_info.label})); +low_r_ind=find(strcmp('low_r',{EEG.marks.chan_info.label})); +bridge_ind=find(strcmp('bridge',{EEG.marks.chan_info.label})); +rank_ind=find(strcmp('rank',{EEG.marks.chan_info.label})); +icrt_ind=find(strcmp('ic_rt',{EEG.marks.comp_info.label})); +brain_ind=find(strcmp('brain',{EEG.marks.comp_info.label})); +muscle_ind=find(strcmp('muscle',{EEG.marks.comp_info.label})); +eye_ind=find(strcmp('eye',{EEG.marks.comp_info.label})); +heart_ind=find(strcmp('heart',{EEG.marks.comp_info.label})); +line_noise_ind=find(strcmp('line_noise',{EEG.marks.comp_info.label})); +chan_noise_ind=find(strcmp('chan_noise',{EEG.marks.comp_info.label})); +other_ind=find(strcmp('other',{EEG.marks.comp_info.label})); +ambig_ind=find(strcmp('ambig',{EEG.marks.comp_info.label})); + +EEG.marks.comp_info(manual_ind).order=1; +if ~isempty(ch_s_sd_ind); % skip study-wide criteria if not used + EEG.marks.comp_info(ch_s_sd_ind).order=2; +end; +EEG.marks.comp_info(ch_sd_ind).order=2; +EEG.marks.comp_info(low_r_ind).order=2; +EEG.marks.comp_info(bridge_ind).order=2; +EEG.marks.comp_info(rank_ind).order=2; +EEG.marks.comp_info(icrt_ind).order=1; + +EEG.marks.comp_info(brain_ind).order=2; +EEG.marks.comp_info(muscle_ind).order=2; +EEG.marks.comp_info(eye_ind).order=2; +EEG.marks.comp_info(heart_ind).order=2; +EEG.marks.comp_info(line_noise_ind).order=2; +EEG.marks.comp_info(chan_noise_ind).order=2; +EEG.marks.comp_info(other_ind).order=2; +EEG.marks.comp_info(ambig_ind).order=3; + +bioa_inds = marks_label2index(EEG.marks.comp_info,{'muscle','eye','heart','chan_noise'},'indexes'); +EEG.marks.comp_info(manual_ind).flags=zeros(min(size(EEG.icaweights)),1); +EEG.marks.comp_info(manual_ind).flags(bioa_inds)=1; + +% BIDS Export calls +% Dot for path is on purpose. Don't worry - it's safe. +fName = './derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_eeg.edf'; +pop_writeeeg(EEG, fName, 'TYPE','EDF'); +dlmwrite(strrep(fName,'_eeg.edf','_icaweights.tsv'),EEG.icaweights,'\t'); +dlmwrite(strrep(fName,'_eeg.edf','_icasphere.tsv'),EEG.icasphere,'\t'); +s = {}; +s.algorithm = 'amica15'; +s.icachansind = EEG.icachansind; +s.intendedFor = fName; +savejson('',s,strrep(fName,'_eeg.edf','_icaweights.json')); +export_anno(EEG,fName,''); + +% ICLabel quick export +iclFname = strrep(fName,'_eeg.edf','_iclabel.mat'); +save(iclFname, 'tmp'); % see above ICLabel section for tmp definition diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/submit.sh b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/submit.sh new file mode 100644 index 000000000..089076a29 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/submit.sh @@ -0,0 +1,24 @@ +#!/bin/bash +set -e +module load gcc/7.3.0; +module load octave/4.4.1; + +sbatch --job-name=s05_concat_data_sub-001_task-faceFO_eeg --output=derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/s05_concat_data_sub-001_task-faceFO_eeg.log --dependency=afterok:19977207,19977220,19977234 --mem-per-cpu=8000m --time=01:00:00 --cpus-per-task=8 --account=def-bucanl <(echo -e '#!/bin/bash\noctave derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/batch_context/batch/exec_func/octave_exit_wrapper.m derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/s05_concat_data_sub_001_task_faceFO_eeg.m') + +sbatch --job-name=s05_concat_data_sub-002_task-faceFO_eeg --output=derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/s05_concat_data_sub-002_task-faceFO_eeg.log --dependency=afterok:19977208,19977221,19977235 --mem-per-cpu=8000m --time=01:00:00 --cpus-per-task=8 --account=def-bucanl <(echo -e '#!/bin/bash\noctave derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/batch_context/batch/exec_func/octave_exit_wrapper.m derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/s05_concat_data_sub_002_task_faceFO_eeg.m') + +sbatch --job-name=s05_concat_data_sub-003_task-faceFO_eeg --output=derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/s05_concat_data_sub-003_task-faceFO_eeg.log --dependency=afterok:19977209,19977222,19977236 --mem-per-cpu=8000m --time=01:00:00 --cpus-per-task=8 --account=def-bucanl <(echo -e '#!/bin/bash\noctave derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/batch_context/batch/exec_func/octave_exit_wrapper.m derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/s05_concat_data_sub_003_task_faceFO_eeg.m') + +sbatch --job-name=s05_concat_data_sub-004_task-faceFO_eeg --output=derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/s05_concat_data_sub-004_task-faceFO_eeg.log --dependency=afterok:19977210,19977223,19977237 --mem-per-cpu=8000m --time=01:00:00 --cpus-per-task=8 --account=def-bucanl <(echo -e '#!/bin/bash\noctave derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/batch_context/batch/exec_func/octave_exit_wrapper.m derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/s05_concat_data_sub_004_task_faceFO_eeg.m') + +sbatch --job-name=s05_concat_data_sub-005_task-faceFO_eeg --output=derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/s05_concat_data_sub-005_task-faceFO_eeg.log --dependency=afterok:19977211,19977224,19977238 --mem-per-cpu=8000m --time=01:00:00 --cpus-per-task=8 --account=def-bucanl <(echo -e '#!/bin/bash\noctave derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/batch_context/batch/exec_func/octave_exit_wrapper.m derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/s05_concat_data_sub_005_task_faceFO_eeg.m') + +sbatch --job-name=s05_concat_data_sub-006_task-faceFO_eeg --output=derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/s05_concat_data_sub-006_task-faceFO_eeg.log --dependency=afterok:19977212,19977225,19977239 --mem-per-cpu=8000m --time=01:00:00 --cpus-per-task=8 --account=def-bucanl <(echo -e '#!/bin/bash\noctave derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/batch_context/batch/exec_func/octave_exit_wrapper.m derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/s05_concat_data_sub_006_task_faceFO_eeg.m') + +sbatch --job-name=s05_concat_data_sub-007_task-faceFO_eeg --output=derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/s05_concat_data_sub-007_task-faceFO_eeg.log --dependency=afterok:19977213,19977226,19977240 --mem-per-cpu=8000m --time=01:00:00 --cpus-per-task=8 --account=def-bucanl <(echo -e '#!/bin/bash\noctave derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/batch_context/batch/exec_func/octave_exit_wrapper.m derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/s05_concat_data_sub_007_task_faceFO_eeg.m') + +sbatch --job-name=s05_concat_data_sub-008_task-faceFO_eeg --output=derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/s05_concat_data_sub-008_task-faceFO_eeg.log --dependency=afterok:19977214,19977227,19977241 --mem-per-cpu=8000m --time=01:00:00 --cpus-per-task=8 --account=def-bucanl <(echo -e '#!/bin/bash\noctave derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/batch_context/batch/exec_func/octave_exit_wrapper.m derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/s05_concat_data_sub_008_task_faceFO_eeg.m') + +sbatch --job-name=s05_concat_data_sub-009_task-faceFO_eeg --output=derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/s05_concat_data_sub-009_task-faceFO_eeg.log --dependency=afterok:19977215,19977228,19977242 --mem-per-cpu=8000m --time=01:00:00 --cpus-per-task=8 --account=def-bucanl <(echo -e '#!/bin/bash\noctave derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/batch_context/batch/exec_func/octave_exit_wrapper.m derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/s05_concat_data_sub_009_task_faceFO_eeg.m') + +sbatch --job-name=s05_concat_data_sub-010_task-faceFO_eeg --output=derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/s05_concat_data_sub-010_task-faceFO_eeg.log --dependency=afterok:19977216,19977229,19977243 --mem-per-cpu=8000m --time=01:00:00 --cpus-per-task=8 --account=def-bucanl <(echo -e '#!/bin/bash\noctave derivatives/BIDS-Lossless-EEG/code/dependencies/eeglab_lossless/plugins/batch_context/batch/exec_func/octave_exit_wrapper.m derivatives/BIDS-Lossless-EEG/log/s05_concat_data-2019-10-01T14-35-11/s05_concat_data_sub_010_task_faceFO_eeg.m') diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-001/eeg/sub-001_task-faceFO_annotations.json b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-001/eeg/sub-001_task-faceFO_annotations.json new file mode 100644 index 000000000..ab7b11134 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-001/eeg/sub-001_task-faceFO_annotations.json @@ -0,0 +1,63 @@ +{ + "Description": "Lossless state channel and component annotations.", + "IntendedFor": "./derivatives/BIDS-Lossless-EEG/./sub-001/eeg/sub-001_task-faceFO_eeg.edf", + "Sources": "See BUCANL GitHub", + "Author": "Lossless", + "LabelDescription": { + "chan_manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which channels are considered artifactual for any reason.", + "chan_ch_sd": "Pipeline decision flag indicating that channels were too often outliers compared to other channels for the measure of standard deviation of voltage within one second epochs.", + "chan_low_r": "Pipeline decision flag indicating that channels were too often outliers compared to other channels for the measure of correlation coefficient to spatially neighbouring channels within one second epochs.", + "chan_bridge": "Pipeline decision flag indicating that channels were outliers for in terms of having high and invariant correlation coefficients to spatially neighbouring channels.", + "chan_rank": "Pipeline decision identifying the channel that has the least amount of unique information (highest average correlation coefficient to spatially neighbouring channels) to be ignored by ICA in order to account for the rank deficiency of the average referenced data.", + "comp_manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which components are considered artifactual for any reason.", + "comp_ic_rt": "Pipeline decision flag indicating that components had a poor dipole fit (typically > %15 residual variance).", + "comp_brain": "ICLabel 0.3 Mark indicating that the compnents have cortical characteristics.", + "comp_muscle": "ICLabel 0.3 Mark indicating that the compnents have EMG characteristics.", + "comp_eye": "ICLabel 0.3 Mark indicating that the compnents have EOG characteristics.", + "comp_heart": "ICLabel 0.3 Mark indicating that the compnents have ECG characteristics.", + "comp_line_noise": "ICLabel 0.3 Mark indicating that the compnents have electrical mains noise characteristics.", + "comp_chan_noise": "ICLabel 0.3 Mark indicating that the compnents have channel independence characteristics.", + "comp_other": "ICLabel 0.3 Mark indicating that the compnents could not be confidently classified within any other ICLabel classification.", + "comp_ambig": "QC procedure rater markup indicating that the compnents are difficult to classify as either artifact or not.", + "in_task_fhbc_onset": "Based on experimental task events indicating time points within the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "in_leadup_fhbc_onset": "Based on experimental task events indicating time points leading into (a few second before and a few seconds after the first experimental event in a block of trials) the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "out_task": "Based on experimental task events indicating time points outside the duration of any experimental tasks (e.g. breaks, start up and end off times, etc).", + "in_task_fhbc_fade": "Based on experimental task events indicating time points within the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "in_leadup_fhbc_fade": "Based on experimental task events indicating time points leading into (a few second before and a few seconds after the first experimental event in a block of trials) the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "ic_hg": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral High Gama within one second epochs.", + "manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which time points are considered artifactual for any reason.", + "init_ind": "Continuous variable indicating the initial time point index within the session.", + "mark_gap": "Pipeline decision flag indicating that time points are within a short gap between other annotations.", + "ch_sd": "Pipeline decision flag indicating that time points were too often outliers across channels compared to other time points for the measure of standard deviation of voltage within one second epochs.", + "low_r": "Pipeline decision flag indicating that time points were too often outliers across channels compared to other time points for the measure of correlation coefficient to spatially neighbouring channels within one second epochs.", + "logl_init": "Log likelihood of initial AMICA decomposition.", + "ic_sd1": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component standard deviation of voltage within one second epochs.", + "logl_A": "Log likelihood of final AMICA decomposition (replication A).", + "logl_B": "Log likelihood of final AMICA decomposition (replication B).", + "logl_C": "Log likelihood of final AMICA decomposition (replication C).", + "ic_sd2": "Pipeline decision flag indicating that time points were too often outliers across final components compared to other time points for the measure of component standard deviation of voltage within one second epochs.", + "ic_dt": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral Theta within one second epochs.", + "ic_a": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral Alpha within one second epochs.", + "ic_b": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral Beta within one second epochs.", + "ic_lg": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral Low Gama within one second epochs." + }, + "SamplingFrequency": 512, + "StartTime": 0, + "Columns": [ + "init_ind", + "mark_gap", + "ch_sd", + "low_r", + "logl_init", + "ic_sd1", + "logl_A", + "logl_B", + "logl_C", + "ic_sd2", + "ic_dt", + "ic_a", + "ic_b", + "ic_lg", + "ic_hg" + ] +} diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-001/eeg/sub-001_task-faceFO_annotations.mat b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-001/eeg/sub-001_task-faceFO_annotations.mat new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-001/eeg/sub-001_task-faceFO_annotations.tsv b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-001/eeg/sub-001_task-faceFO_annotations.tsv new file mode 100644 index 000000000..776871232 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-001/eeg/sub-001_task-faceFO_annotations.tsv @@ -0,0 +1,45 @@ +onset duration label channels +n/a n/a chan_manual ["EEG008","EEG009","EEG010","EEG011","EEG012","EEG025","EEG026","EEG065","EEG074","EEG128"] +n/a n/a chan_ch_sd ["EEG065","EEG074"] +n/a n/a chan_bridge ["EEG008","EEG009","EEG010","EEG011","EEG012","EEG025","EEG026","EEG128"] +n/a n/a chan_rank ["EEG125"] +n/a n/a comp_manual ["ICA001","ICA009","ICA012","ICA013","ICA015","ICA016","ICA019","ICA020","ICA023","ICA025","ICA026","ICA027","ICA029","ICA030","ICA032","ICA033","ICA034","ICA036","ICA037","ICA038","ICA039","ICA040","ICA041","ICA042","ICA043","ICA044","ICA045","ICA046","ICA048","ICA049","ICA050","ICA051","ICA052","ICA053","ICA055","ICA056","ICA057","ICA058","ICA059","ICA060","ICA061","ICA062","ICA064","ICA065","ICA066","ICA067","ICA068","ICA069","ICA070","ICA071","ICA072","ICA073","ICA074","ICA075","ICA077","ICA079","ICA080","ICA081","ICA082","ICA083","ICA084","ICA085","ICA086","ICA087","ICA088","ICA089","ICA090","ICA091","ICA092","ICA094","ICA095","ICA096","ICA097","ICA098","ICA099","ICA100","ICA101","ICA102","ICA103","ICA104","ICA105","ICA106","ICA107","ICA108","ICA109","ICA110","ICA111","ICA112","ICA113","ICA114","ICA115","ICA116","ICA117"] +n/a n/a comp_ic_rt ["ICA003","ICA005","ICA006","ICA007","ICA008","ICA010","ICA011","ICA017","ICA018","ICA021","ICA022","ICA024","ICA026","ICA028","ICA029","ICA030","ICA031","ICA034","ICA035","ICA037","ICA038","ICA041","ICA042","ICA043","ICA045","ICA046","ICA047","ICA049","ICA052","ICA054","ICA056","ICA059","ICA061","ICA063","ICA064","ICA065","ICA066","ICA068","ICA069","ICA070","ICA071","ICA073","ICA074","ICA076","ICA077","ICA078","ICA079","ICA081","ICA082","ICA083","ICA084","ICA086","ICA088","ICA089","ICA090","ICA091","ICA092","ICA093","ICA094","ICA095","ICA096","ICA097","ICA098","ICA099","ICA100","ICA102","ICA103","ICA104","ICA105","ICA106","ICA107","ICA108","ICA109","ICA110","ICA111","ICA112","ICA113","ICA114","ICA115","ICA116","ICA117"] +n/a n/a comp_brain ["ICA002","ICA003","ICA004","ICA005","ICA006","ICA007","ICA008","ICA010","ICA011","ICA014","ICA017","ICA018","ICA021","ICA022","ICA024","ICA028","ICA031","ICA035","ICA047","ICA054","ICA063","ICA078","ICA093"] +n/a n/a comp_muscle ["ICA009","ICA012","ICA013","ICA015","ICA016","ICA019","ICA023","ICA025","ICA026","ICA029","ICA032","ICA033","ICA037","ICA038","ICA044","ICA046","ICA053","ICA067","ICA069","ICA070","ICA072"] +n/a n/a comp_eye ["ICA001","ICA052"] +n/a n/a comp_line_noise ["ICA076"] +n/a n/a comp_chan_noise ["ICA020","ICA027","ICA030","ICA034","ICA036","ICA039","ICA040","ICA041","ICA042","ICA043","ICA045","ICA048","ICA049","ICA050","ICA051","ICA055","ICA056","ICA057","ICA058","ICA059","ICA060","ICA061","ICA062","ICA064","ICA065","ICA066","ICA068","ICA071","ICA073","ICA074","ICA075","ICA077","ICA079","ICA080","ICA081","ICA082","ICA083","ICA084","ICA085","ICA086","ICA087","ICA088","ICA089","ICA090","ICA091","ICA092","ICA094","ICA095","ICA096","ICA097","ICA098","ICA099","ICA100","ICA101","ICA102","ICA103","ICA104","ICA105","ICA106","ICA107","ICA108","ICA109","ICA110","ICA111","ICA112","ICA113","ICA114","ICA115","ICA116","ICA117"] +0.0019531 10.0918 manual n/a +0.0019531 3.0918 out_task n/a +2.0918 8.002 in_leadup_fhbc_fade n/a +3.0938 225.0078 in_task_fhbc_fade n/a +228.1016 11.5234 manual n/a +228.1016 4.5234 out_task n/a +231.623 8.002 in_leadup_fhbc_fade n/a +232.625 218.2012 in_task_fhbc_fade n/a +450.8262 12.7617 manual n/a +450.8262 5.7617 out_task n/a +455.5859 8.002 in_leadup_fhbc_fade n/a +456.5879 222.1777 in_task_fhbc_fade n/a +678.7656 27.5703 manual n/a +678.7656 20.5703 out_task n/a +698.334 8.002 in_leadup_fhbc_fade n/a +699.3359 223.0605 in_task_fhbc_fade n/a +922.3965 13.8594 manual n/a +922.3965 6.8594 out_task n/a +928.2539 8.002 in_leadup_fhbc_onset n/a +929.2559 268.9102 in_task_fhbc_onset n/a +1198.166 12.7402 manual n/a +1198.166 5.7402 out_task n/a +1202.9043 8.002 in_leadup_fhbc_onset n/a +1203.9062 269.7109 in_task_fhbc_onset n/a +1473.6172 40.5859 manual n/a +1473.6172 33.5859 out_task n/a +1506.2012 8.002 in_leadup_fhbc_onset n/a +1507.2031 270.5586 in_task_fhbc_onset n/a +1777.7617 18.3828 manual n/a +1777.7617 11.3828 out_task n/a +1788.1426 8.002 in_leadup_fhbc_onset n/a +1789.1445 255.8574 in_task_fhbc_onset n/a +2038.002 3 manual n/a diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-001/eeg/sub-001_task-faceFO_desc-qc_annotations.json b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-001/eeg/sub-001_task-faceFO_desc-qc_annotations.json new file mode 100755 index 000000000..c5ddca5de --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-001/eeg/sub-001_task-faceFO_desc-qc_annotations.json @@ -0,0 +1,30 @@ +{ + "Description": "Lossless state channel and component annotations.", + "IntendedFor": "derivatives/BIDS-Lossless-EEG//./sub-001/eeg/sub-001_task-faceFO_eeg.edf", + "Sources": "See BUCANL GitHub", + "Author": "Lossless", + "LabelDescription": { + "chan_manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which channels are considered artifactual for any reason.", + "chan_ch_sd": "Pipeline decision flag indicating that channels were too often outliers compared to other channels for the measure of standard deviation of voltage within one second epochs.", + "chan_low_r": "Pipeline decision flag indicating that channels were too often outliers compared to other channels for the measure of correlation coefficient to spatially neighbouring channels within one second epochs.", + "chan_bridge": "Pipeline decision flag indicating that channels were outliers for in terms of having high and invariant correlation coefficients to spatially neighbouring channels.", + "chan_rank": "Pipeline decision identifying the channel that has the least amount of unique information (highest average correlation coefficient to spatially neighbouring channels) to be ignored by ICA in order to account for the rank deficiency of the average referenced data.", + "comp_manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which components are considered artifactual for any reason.", + "comp_ic_rt": "Pipeline decision flag indicating that components had a poor dipole fit (typically > %15 residual variance).", + "comp_brain": "ICLabel 0.3 Mark indicating that the compnents have cortical characteristics.", + "comp_muscle": "ICLabel 0.3 Mark indicating that the compnents have EMG characteristics.", + "comp_eye": "ICLabel 0.3 Mark indicating that the compnents have EOG characteristics.", + "comp_heart": "ICLabel 0.3 Mark indicating that the compnents have ECG characteristics.", + "comp_line_noise": "ICLabel 0.3 Mark indicating that the compnents have electrical mains noise characteristics.", + "comp_chan_noise": "ICLabel 0.3 Mark indicating that the compnents have channel independence characteristics.", + "comp_other": "ICLabel 0.3 Mark indicating that the compnents could not be confidently classified within any other ICLabel classification.", + "comp_ambig": "QC procedure rater markup indicating that the compnents are difficult to classify as either artifact or not.", + "in_task_fhbc_onset": "Based on experimental task events indicating time points within the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "in_leadup_fhbc_onset": "Based on experimental task events indicating time points leading into (a few second before and a few seconds after the first experimental event in a block of trials) the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "out_task": "Based on experimental task events indicating time points outside the duration of any experimental tasks (e.g. breaks, start up and end off times, etc).", + "in_task_fhbc_fade": "Based on experimental task events indicating time points within the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "in_leadup_fhbc_fade": "Based on experimental task events indicating time points leading into (a few second before and a few seconds after the first experimental event in a block of trials) the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "ic_hg": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral High Gama within one second epochs.", + "manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which time points are considered artifactual for any reason." + } +} diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-001/eeg/sub-001_task-faceFO_desc-qc_annotations.mat b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-001/eeg/sub-001_task-faceFO_desc-qc_annotations.mat new file mode 100755 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-001/eeg/sub-001_task-faceFO_desc-qc_annotations.tsv b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-001/eeg/sub-001_task-faceFO_desc-qc_annotations.tsv new file mode 100755 index 000000000..da7a87a70 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-001/eeg/sub-001_task-faceFO_desc-qc_annotations.tsv @@ -0,0 +1,46 @@ +onset duration label channels +n/a n/a chan_manual ["EEG008","EEG009","EEG010","EEG011","EEG012","EEG025","EEG026","EEG065","EEG074","EEG128"] +n/a n/a chan_ch_sd ["EEG065","EEG074"] +n/a n/a chan_bridge ["EEG008","EEG009","EEG010","EEG011","EEG012","EEG025","EEG026","EEG128"] +n/a n/a chan_rank ["EEG125"] +n/a n/a comp_manual ["ICA001","ICA002","ICA009","ICA012","ICA013","ICA014","ICA015","ICA016","ICA019","ICA020","ICA023","ICA025","ICA027","ICA028","ICA030","ICA031","ICA032","ICA033","ICA035","ICA036","ICA037","ICA038","ICA039","ICA040","ICA041","ICA042","ICA045","ICA046","ICA047","ICA048","ICA049","ICA050","ICA051","ICA052","ICA053","ICA054","ICA055","ICA057","ICA058","ICA059","ICA060","ICA061","ICA062","ICA065","ICA066","ICA067","ICA068","ICA069","ICA070","ICA071","ICA072","ICA073","ICA074","ICA075","ICA076","ICA077","ICA079","ICA080","ICA081","ICA082","ICA083","ICA084","ICA085","ICA086","ICA087","ICA088","ICA089","ICA090","ICA091","ICA092","ICA094","ICA095","ICA096","ICA097","ICA098","ICA099","ICA100","ICA101","ICA102","ICA103","ICA104","ICA105","ICA106","ICA107","ICA108","ICA109","ICA110","ICA111","ICA112","ICA113","ICA114","ICA115","ICA116","ICA117"] +n/a n/a comp_ic_rt 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["ICA020","ICA028","ICA031","ICA035","ICA036","ICA039","ICA040","ICA041","ICA042","ICA046","ICA048","ICA049","ICA050","ICA051","ICA054","ICA055","ICA057","ICA058","ICA059","ICA060","ICA061","ICA062","ICA065","ICA067","ICA068","ICA071","ICA073","ICA074","ICA075","ICA076","ICA079","ICA080","ICA081","ICA082","ICA083","ICA084","ICA085","ICA086","ICA087","ICA088","ICA089","ICA090","ICA091","ICA092","ICA094","ICA095","ICA096","ICA097","ICA098","ICA099","ICA100","ICA101","ICA102","ICA103","ICA104","ICA105","ICA106","ICA107","ICA108","ICA109","ICA110","ICA111","ICA112","ICA113","ICA114","ICA115","ICA116","ICA117"] +n/a n/a comp_other ["ICA011"] +0.0019531 10.0938 manual n/a +0.0019531 3.0938 out_task n/a +2.0918 8.0039 in_leadup_fhbc_fade n/a +3.0938 225.0098 in_task_fhbc_fade n/a +228.1016 11.5254 manual n/a +228.1016 4.5254 out_task n/a +231.623 8.0039 in_leadup_fhbc_fade n/a +232.625 218.2031 in_task_fhbc_fade n/a +450.8262 12.7637 manual n/a +450.8262 5.7637 out_task n/a +455.5859 8.0039 in_leadup_fhbc_fade n/a +456.5879 222.1797 in_task_fhbc_fade n/a +678.7656 27.5723 manual n/a +678.7656 20.5723 out_task n/a +698.334 8.0039 in_leadup_fhbc_fade n/a +699.3359 223.0625 in_task_fhbc_fade n/a +922.3965 13.8613 manual n/a +922.3965 6.8613 out_task n/a +928.2539 8.0039 in_leadup_fhbc_onset n/a +929.2559 268.9121 in_task_fhbc_onset n/a +1198.166 12.7422 manual n/a +1198.166 5.7422 out_task n/a +1202.9043 8.0039 in_leadup_fhbc_onset n/a +1203.9062 269.7129 in_task_fhbc_onset n/a +1473.6172 40.5879 manual n/a +1473.6172 33.5879 out_task n/a +1506.2012 8.0039 in_leadup_fhbc_onset n/a +1507.2031 270.5605 in_task_fhbc_onset n/a +1777.7617 18.3848 manual n/a +1777.7617 11.3848 out_task n/a +1788.1426 8.0039 in_leadup_fhbc_onset n/a +1789.1445 255.8594 in_task_fhbc_onset n/a +2038.002 3.002 manual n/a diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-001/eeg/sub-001_task-faceFO_dipole.mat b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-001/eeg/sub-001_task-faceFO_dipole.mat 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a/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-001/eeg/sub-001_task-faceFO_iclabel.mat b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-001/eeg/sub-001_task-faceFO_iclabel.mat new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-002/eeg/sub-002_task-faceFO_annotations.json b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-002/eeg/sub-002_task-faceFO_annotations.json new file mode 100644 index 000000000..5889e1fcd --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-002/eeg/sub-002_task-faceFO_annotations.json @@ -0,0 +1,61 @@ +{ + "Description": "Lossless state channel and component annotations.", + "IntendedFor": "./derivatives/BIDS-Lossless-EEG/./sub-002/eeg/sub-002_task-faceFO_eeg.edf", + "Sources": "See BUCANL GitHub", + "Author": "Lossless", + "LabelDescription": { + "chan_manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which channels are considered artifactual for any reason.", + "chan_ch_sd": "Pipeline decision flag indicating that channels were too often outliers compared to other channels for the measure of standard deviation of voltage within one second epochs.", + "chan_low_r": "Pipeline decision flag indicating that channels were too often outliers compared to other channels for the measure of correlation coefficient to spatially neighbouring channels within one second epochs.", + "chan_bridge": "Pipeline decision flag indicating that channels were outliers for in terms of having high and invariant correlation coefficients to spatially neighbouring channels.", + "chan_rank": "Pipeline decision identifying the channel that has the least amount of unique information (highest average correlation coefficient to spatially neighbouring channels) to be ignored by ICA in order to account for the rank deficiency of the average referenced data.", + "comp_manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which components are considered artifactual for any reason.", + "comp_ic_rt": "Pipeline decision flag indicating that components had a poor dipole fit (typically > %15 residual variance).", + "comp_brain": "ICLabel 0.3 Mark indicating that the compnents have cortical characteristics.", + "comp_muscle": "ICLabel 0.3 Mark indicating that the compnents have EMG characteristics.", + "comp_eye": "ICLabel 0.3 Mark indicating that the compnents have EOG characteristics.", + "comp_heart": "ICLabel 0.3 Mark indicating that the compnents have ECG characteristics.", + "comp_line_noise": "ICLabel 0.3 Mark indicating that the compnents have electrical mains noise characteristics.", + "comp_chan_noise": "ICLabel 0.3 Mark indicating that the compnents have channel independence characteristics.", + "comp_other": "ICLabel 0.3 Mark indicating that the compnents could not be confidently classified within any other ICLabel classification.", + "comp_ambig": "QC procedure rater markup indicating that the compnents are difficult to classify as either artifact or not.", + "in_task_fhbc_onset": "Based on experimental task events indicating time points within the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "in_leadup_fhbc_onset": "Based on experimental task events indicating time points leading into (a few second before and a few seconds after the first experimental event in a block of trials) the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "out_task": "Based on experimental task events indicating time points outside the duration of any experimental tasks (e.g. breaks, start up and end off times, etc).", + "in_task_fhbc_fade": "Based on experimental task events indicating time points within the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "in_leadup_fhbc_fade": "Based on experimental task events indicating time points leading into (a few second before and a few seconds after the first experimental event in a block of trials) the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "ic_hg": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral High Gama within one second epochs.", + "manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which time points are considered artifactual for any reason.", + "init_ind": "Continuous variable indicating the initial time point index within the session.", + "mark_gap": "Pipeline decision flag indicating that time points are within a short gap between other annotations.", + "ch_sd": "Pipeline decision flag indicating that time points were too often outliers across channels compared to other time points for the measure of standard deviation of voltage within one second epochs.", + "low_r": "Pipeline decision flag indicating that time points were too often outliers across channels compared to other time points for the measure of correlation coefficient to spatially neighbouring channels within one second epochs.", + "logl_init": "Log likelihood of initial AMICA decomposition.", + "ic_sd1": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component standard deviation of voltage within one second epochs.", + "logl_A": "Log likelihood of final AMICA decomposition (replication A).", + "logl_B": "Log likelihood of final AMICA decomposition (replication B).", + "logl_C": "Log likelihood of final AMICA decomposition (replication C).", + "ic_sd2": "Pipeline decision flag indicating that time points were too often outliers across final components compared to other time points for the measure of component standard deviation of voltage within one second epochs.", + "ic_dt": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral Theta within one second epochs.", + "ic_a": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral Alpha within one second epochs.", + "ic_b": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral Beta within one second epochs.", + "ic_lg": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral Low Gama within one second epochs." + }, + "SamplingFrequency": 512, + "StartTime": 0, + "Columns": [ + "init_ind", + "mark_gap", + "ch_sd", + "low_r", + "logl_init", + "ic_sd1", + "logl_A", + "logl_B", + "logl_C", + "ic_sd2", + "ic_b", + "ic_lg", + "ic_hg" + ] +} diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-002/eeg/sub-002_task-faceFO_annotations.mat b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-002/eeg/sub-002_task-faceFO_annotations.mat new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-002/eeg/sub-002_task-faceFO_annotations.tsv b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-002/eeg/sub-002_task-faceFO_annotations.tsv new file mode 100644 index 000000000..e46e5e54e --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-002/eeg/sub-002_task-faceFO_annotations.tsv @@ -0,0 +1,50 @@ +onset duration label channels +n/a n/a chan_manual ["EEG048","EEG049","EEG059","EEG060","EEG074","EEG082"] +n/a n/a chan_ch_sd ["EEG074","EEG082"] +n/a n/a chan_bridge ["EEG048","EEG049","EEG059","EEG060"] +n/a n/a chan_rank ["EEG022"] +n/a n/a comp_manual ["ICA001","ICA002","ICA004","ICA005","ICA006","ICA008","ICA009","ICA010","ICA011","ICA012","ICA013","ICA014","ICA015","ICA016","ICA017","ICA018","ICA019","ICA021","ICA022","ICA023","ICA024","ICA025","ICA026","ICA028","ICA029","ICA030","ICA031","ICA032","ICA034","ICA035","ICA036","ICA037","ICA038","ICA039","ICA040","ICA041","ICA043","ICA044","ICA045","ICA047","ICA048","ICA049","ICA050","ICA051","ICA052","ICA053","ICA055","ICA056","ICA058","ICA059","ICA060","ICA063","ICA064","ICA066","ICA069","ICA071","ICA074","ICA075","ICA077","ICA078","ICA079","ICA080","ICA081","ICA082","ICA083","ICA084","ICA085","ICA089","ICA090","ICA091","ICA093","ICA094","ICA095","ICA096","ICA098","ICA099","ICA100","ICA101","ICA102","ICA103","ICA105","ICA106","ICA107","ICA108","ICA109","ICA110","ICA112","ICA113","ICA115","ICA116","ICA117","ICA118","ICA119","ICA120","ICA121"] +n/a n/a comp_ic_rt 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out_task n/a +1610.7148 8.002 in_leadup_fhbc_onset n/a +1611.7168 271.3066 in_task_fhbc_onset n/a +1883.0234 33.0117 manual n/a +1883.0234 26.0117 out_task n/a +1908.0332 8.002 in_leadup_fhbc_onset n/a +1909.0352 270.0254 in_task_fhbc_onset n/a +2167.002 1 ic_a n/a +2179.0605 18.9414 manual n/a +2179.0605 18.9414 out_task n/a diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-002/eeg/sub-002_task-faceFO_desc-qc_annotations.json b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-002/eeg/sub-002_task-faceFO_desc-qc_annotations.json new file mode 100755 index 000000000..5c717b0eb --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-002/eeg/sub-002_task-faceFO_desc-qc_annotations.json @@ -0,0 +1,30 @@ +{ + "Description": "Lossless state channel and component annotations.", + "IntendedFor": "derivatives/BIDS-Lossless-EEG//./sub-002/eeg/sub-002_task-faceFO_eeg.edf", + "Sources": "See BUCANL GitHub", + "Author": "Lossless", + "LabelDescription": { + "chan_manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which channels are considered artifactual for any reason.", + "chan_ch_sd": "Pipeline decision flag indicating that channels were too often outliers compared to other channels for the measure of standard deviation of voltage within one second epochs.", + "chan_low_r": "Pipeline decision flag indicating that channels were too often outliers compared to other channels for the measure of correlation coefficient to spatially neighbouring channels within one second epochs.", + "chan_bridge": "Pipeline decision flag indicating that channels were outliers for in terms of having high and invariant correlation coefficients to spatially neighbouring channels.", + "chan_rank": "Pipeline decision identifying the channel that has the least amount of unique information (highest average correlation coefficient to spatially neighbouring channels) to be ignored by ICA in order to account for the rank deficiency of the average referenced data.", + "comp_manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which components are considered artifactual for any reason.", + "comp_ic_rt": "Pipeline decision flag indicating that components had a poor dipole fit (typically > %15 residual variance).", + "comp_brain": "ICLabel 0.3 Mark indicating that the compnents have cortical characteristics.", + "comp_muscle": "ICLabel 0.3 Mark indicating that the compnents have EMG characteristics.", + "comp_eye": "ICLabel 0.3 Mark indicating that the compnents have EOG characteristics.", + "comp_heart": "ICLabel 0.3 Mark indicating that the compnents have ECG characteristics.", + "comp_line_noise": "ICLabel 0.3 Mark indicating that the compnents have electrical mains noise characteristics.", + "comp_chan_noise": "ICLabel 0.3 Mark indicating that the compnents have channel 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experimental task events indicating time points within the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "in_leadup_fhbc_fade": "Based on experimental task events indicating time points leading into (a few second before and a few seconds after the first experimental event in a block of trials) the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "ic_hg": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral High Gama within one second epochs.", + "manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which time points are considered artifactual for any reason." + } +} diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-002/eeg/sub-002_task-faceFO_desc-qc_annotations.mat b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-002/eeg/sub-002_task-faceFO_desc-qc_annotations.mat new file mode 100755 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-002/eeg/sub-002_task-faceFO_desc-qc_annotations.tsv b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-002/eeg/sub-002_task-faceFO_desc-qc_annotations.tsv new file mode 100755 index 000000000..afe0e510a --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-002/eeg/sub-002_task-faceFO_desc-qc_annotations.tsv @@ -0,0 +1,50 @@ +onset duration label channels +n/a n/a chan_manual ["EEG048","EEG049","EEG059","EEG060","EEG074","EEG082"] +n/a n/a chan_ch_sd ["EEG074","EEG082"] +n/a n/a chan_bridge ["EEG048","EEG049","EEG059","EEG060"] +n/a n/a chan_rank ["EEG022"] +n/a n/a comp_manual 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mode 100644 index 000000000..4505caf4b --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-003/eeg/sub-003_task-faceFO_annotations.json @@ -0,0 +1,59 @@ +{ + "Description": "Lossless state channel and component annotations.", + "IntendedFor": "./derivatives/BIDS-Lossless-EEG/./sub-003/eeg/sub-003_task-faceFO_eeg.edf", + "Sources": "See BUCANL GitHub", + "Author": "Lossless", + "LabelDescription": { + "chan_manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which channels are considered artifactual for any reason.", + "chan_ch_sd": "Pipeline decision flag indicating that channels were too often outliers compared to other channels for the measure of standard deviation of voltage within one second epochs.", + "chan_low_r": "Pipeline decision flag indicating that channels were too often outliers compared to other channels for the measure of correlation coefficient to spatially neighbouring channels within one second epochs.", + "chan_bridge": "Pipeline decision flag indicating that channels were outliers for in terms of having high and invariant correlation coefficients to spatially neighbouring channels.", + "chan_rank": "Pipeline decision identifying the channel that has the least amount of unique information (highest average correlation coefficient to spatially neighbouring channels) to be ignored by ICA in order to account for the rank deficiency of the average referenced data.", + "comp_manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which components are considered artifactual for any reason.", + "comp_ic_rt": "Pipeline decision flag indicating that components had a poor dipole fit (typically > %15 residual variance).", + "comp_brain": "ICLabel 0.3 Mark indicating that the compnents have cortical characteristics.", + "comp_muscle": "ICLabel 0.3 Mark indicating that the compnents have EMG characteristics.", + "comp_eye": "ICLabel 0.3 Mark indicating that the compnents have EOG characteristics.", + "comp_heart": "ICLabel 0.3 Mark indicating that the compnents have ECG characteristics.", + "comp_line_noise": "ICLabel 0.3 Mark indicating that the compnents have electrical mains noise characteristics.", + "comp_chan_noise": "ICLabel 0.3 Mark indicating that the compnents have channel independence characteristics.", + "comp_other": "ICLabel 0.3 Mark indicating that the compnents could not be confidently classified within any other ICLabel classification.", + "comp_ambig": "QC procedure rater markup indicating that the compnents are difficult to classify as either artifact or not.", + "in_task_fhbc_onset": "Based on experimental task events indicating time points within the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "in_leadup_fhbc_onset": "Based on experimental task events indicating time points leading into (a few second before and a few seconds after the first experimental event in a block of trials) the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "out_task": "Based on experimental task events indicating time points outside the duration of any experimental tasks (e.g. breaks, start up and end off times, etc).", + "in_task_fhbc_fade": "Based on experimental task events indicating time points within the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "in_leadup_fhbc_fade": "Based on experimental task events indicating time points leading into (a few second before and a few seconds after the first experimental event in a block of trials) the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "ic_hg": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral High Gama within one second epochs.", + "manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which time points are considered artifactual for any reason.", + "init_ind": "Continuous variable indicating the initial time point index within the session.", + "mark_gap": "Pipeline decision flag indicating that time points are within a short gap between other annotations.", + "ch_sd": "Pipeline decision flag indicating that time points were too often outliers across channels compared to other time points for the measure of standard deviation of voltage within one second epochs.", + "low_r": "Pipeline decision flag indicating that time points were too often outliers across channels compared to other time points for the measure of correlation coefficient to spatially neighbouring channels within one second epochs.", + "logl_init": "Log likelihood of initial AMICA decomposition.", + "ic_sd1": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component standard deviation of voltage within one second epochs.", + "logl_A": "Log likelihood of final AMICA decomposition (replication A).", + "logl_B": "Log likelihood of final AMICA decomposition (replication B).", + "logl_C": "Log likelihood of final AMICA decomposition (replication C).", + "ic_sd2": "Pipeline decision flag indicating that time points were too often outliers across final components compared to other time points for the measure of component standard deviation of voltage within one second epochs.", + "ic_dt": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral Theta within one second epochs.", + "ic_a": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral Alpha within one second epochs.", + "ic_b": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral Beta within one second epochs.", + "ic_lg": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral Low Gama within one second epochs." + }, + "SamplingFrequency": 512, + "StartTime": 0, + "Columns": [ + "init_ind", + "ch_sd", + "low_r", + "logl_init", + "ic_sd1", + "logl_A", + "logl_B", + "logl_C", + "ic_sd2", + "ic_dt", + "ic_a" + ] +} diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-003/eeg/sub-003_task-faceFO_annotations.mat b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-003/eeg/sub-003_task-faceFO_annotations.mat new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-003/eeg/sub-003_task-faceFO_annotations.tsv b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-003/eeg/sub-003_task-faceFO_annotations.tsv new file mode 100644 index 000000000..7a2369e45 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-003/eeg/sub-003_task-faceFO_annotations.tsv @@ -0,0 +1,71 @@ +onset duration label channels +n/a n/a chan_manual ["EEG001","EEG007","EEG008","EEG009","EEG010","EEG011","EEG012","EEG014","EEG015","EEG025","EEG026","EEG030","EEG031","EEG033","EEG034","EEG036","EEG037","EEG074","EEG106","EEG107","EEG118","EEG119","EEG120","EEG121","EEG125","EEG126","EEG127","EEG128"] +n/a n/a chan_ch_sd ["EEG001","EEG074"] +n/a n/a chan_bridge ["EEG007","EEG008","EEG009","EEG010","EEG011","EEG012","EEG014","EEG015","EEG025","EEG026","EEG030","EEG031","EEG033","EEG034","EEG036","EEG037","EEG106","EEG107","EEG118","EEG119","EEG120","EEG121","EEG125","EEG126","EEG127","EEG128"] +n/a n/a chan_rank ["EEG116"] +n/a n/a comp_manual ["ICA001","ICA006","ICA008","ICA009","ICA012","ICA013","ICA015","ICA016","ICA019","ICA021","ICA022","ICA024","ICA026","ICA027","ICA029","ICA031","ICA033","ICA034","ICA035","ICA041","ICA042","ICA044","ICA045","ICA046","ICA047","ICA048","ICA049","ICA050","ICA052","ICA053","ICA054","ICA055","ICA056","ICA057","ICA058","ICA061","ICA062","ICA063","ICA065","ICA066","ICA067","ICA069","ICA070","ICA071","ICA072","ICA073","ICA074","ICA075","ICA076","ICA077","ICA078","ICA079","ICA080","ICA081","ICA082","ICA083","ICA084","ICA085","ICA087","ICA088","ICA090","ICA091","ICA093","ICA094","ICA095","ICA096","ICA097","ICA098","ICA099"] +n/a n/a comp_ic_rt ["ICA002","ICA005","ICA006","ICA007","ICA008","ICA009","ICA010","ICA011","ICA012","ICA013","ICA014","ICA016","ICA017","ICA018","ICA019","ICA020","ICA023","ICA024","ICA025","ICA026","ICA027","ICA028","ICA029","ICA030","ICA031","ICA032","ICA033","ICA034","ICA035","ICA036","ICA037","ICA038","ICA039","ICA040","ICA042","ICA043","ICA044","ICA045","ICA046","ICA047","ICA048","ICA049","ICA050","ICA051","ICA053","ICA054","ICA055","ICA056","ICA057","ICA058","ICA059","ICA060","ICA061","ICA062","ICA063","ICA064","ICA065","ICA066","ICA067","ICA068","ICA069","ICA070","ICA071","ICA073","ICA074","ICA075","ICA076","ICA077","ICA079","ICA080","ICA081","ICA082","ICA083","ICA084","ICA085","ICA086","ICA087","ICA089","ICA090","ICA091","ICA092","ICA093","ICA094","ICA095","ICA096","ICA097","ICA098","ICA099"] +n/a n/a comp_brain ["ICA002","ICA003","ICA004","ICA005","ICA007","ICA010","ICA011","ICA014","ICA017","ICA018","ICA020","ICA023","ICA025","ICA028","ICA030","ICA032","ICA036","ICA037","ICA038","ICA039","ICA040","ICA043","ICA051","ICA059","ICA060","ICA064","ICA068","ICA086","ICA092"] +n/a n/a comp_muscle ["ICA015","ICA021","ICA022","ICA026","ICA027","ICA033","ICA041","ICA045","ICA054","ICA057","ICA072","ICA075","ICA080"] +n/a n/a comp_eye ["ICA001","ICA008","ICA012","ICA019","ICA024","ICA031","ICA074"] +n/a n/a comp_heart ["ICA009"] +n/a n/a comp_chan_noise ["ICA006","ICA013","ICA016","ICA029","ICA034","ICA035","ICA042","ICA044","ICA046","ICA047","ICA048","ICA049","ICA050","ICA052","ICA053","ICA055","ICA056","ICA058","ICA061","ICA062","ICA063","ICA065","ICA066","ICA067","ICA069","ICA070","ICA071","ICA073","ICA076","ICA077","ICA078","ICA079","ICA081","ICA082","ICA083","ICA084","ICA085","ICA087","ICA088","ICA090","ICA091","ICA093","ICA094","ICA095","ICA096","ICA097","ICA098","ICA099"] +n/a n/a comp_other 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/dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-003/eeg/sub-003_task-faceFO_desc-qc_annotations.json @@ -0,0 +1,30 @@ +{ + "Description": "Lossless state channel and component annotations.", + "IntendedFor": "derivatives/BIDS-Lossless-EEG//./sub-003/eeg/sub-003_task-faceFO_eeg.edf", + "Sources": "See BUCANL GitHub", + "Author": "Lossless", + "LabelDescription": { + "chan_manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which channels are considered artifactual for any reason.", + "chan_ch_sd": "Pipeline decision flag indicating that channels were too often outliers compared to other channels for the measure of standard deviation of voltage within one second epochs.", + "chan_low_r": "Pipeline decision flag indicating that channels were too often outliers compared to other channels for the measure of correlation coefficient to spatially neighbouring channels within one second epochs.", + "chan_bridge": "Pipeline decision flag indicating that channels were outliers for in terms of having high and invariant correlation coefficients to spatially neighbouring channels.", + "chan_rank": "Pipeline decision identifying the channel that has the least amount of unique information (highest average correlation coefficient to spatially neighbouring channels) to be ignored by ICA in order to account for the rank deficiency of the average referenced data.", + "comp_manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which components are considered artifactual for any reason.", + "comp_ic_rt": "Pipeline decision flag indicating that components had a poor dipole fit (typically > %15 residual variance).", + "comp_brain": "ICLabel 0.3 Mark indicating that the compnents have cortical characteristics.", + "comp_muscle": "ICLabel 0.3 Mark indicating that the compnents have EMG characteristics.", + "comp_eye": "ICLabel 0.3 Mark indicating that the compnents have EOG characteristics.", + "comp_heart": "ICLabel 0.3 Mark indicating that the compnents have ECG characteristics.", + "comp_line_noise": "ICLabel 0.3 Mark indicating that the compnents have electrical mains noise characteristics.", + "comp_chan_noise": "ICLabel 0.3 Mark indicating that the compnents have channel independence characteristics.", + "comp_other": "ICLabel 0.3 Mark indicating that the compnents could not be confidently classified within any other ICLabel classification.", + "comp_ambig": "QC procedure rater markup indicating that the compnents are difficult to classify as either artifact or not.", + "in_task_fhbc_onset": "Based on experimental task events indicating time points within the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "in_leadup_fhbc_onset": "Based on experimental task events indicating time points leading into (a few second before and a few seconds after the first experimental event in a block of trials) the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "out_task": "Based on experimental task events indicating time points outside the duration of any experimental tasks (e.g. breaks, start up and end off times, etc).", + "in_task_fhbc_fade": "Based on experimental task events indicating time points within the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "in_leadup_fhbc_fade": "Based on experimental task events indicating time points leading into (a few second before and a few seconds after the first experimental event in a block of trials) the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "ic_hg": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral High Gama within one second epochs.", + "manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which time points are considered artifactual for any reason." + } +} diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-003/eeg/sub-003_task-faceFO_desc-qc_annotations.mat b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-003/eeg/sub-003_task-faceFO_desc-qc_annotations.mat new file mode 100755 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-003/eeg/sub-003_task-faceFO_desc-qc_annotations.tsv b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-003/eeg/sub-003_task-faceFO_desc-qc_annotations.tsv new file mode 100755 index 000000000..e5ad2d3c3 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-003/eeg/sub-003_task-faceFO_desc-qc_annotations.tsv @@ -0,0 +1,68 @@ +onset duration label channels +n/a n/a chan_manual ["EEG001","EEG007","EEG008","EEG009","EEG010","EEG011","EEG012","EEG014","EEG015","EEG025","EEG026","EEG030","EEG031","EEG033","EEG034","EEG036","EEG037","EEG074","EEG106","EEG107","EEG118","EEG119","EEG120","EEG121","EEG125","EEG126","EEG127","EEG128"] +n/a n/a chan_ch_sd ["EEG001","EEG074"] +n/a n/a chan_bridge ["EEG007","EEG008","EEG009","EEG010","EEG011","EEG012","EEG014","EEG015","EEG025","EEG026","EEG030","EEG031","EEG033","EEG034","EEG036","EEG037","EEG106","EEG107","EEG118","EEG119","EEG120","EEG121","EEG125","EEG126","EEG127","EEG128"] +n/a n/a chan_rank ["EEG116"] +n/a n/a comp_manual 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@@ -0,0 +1,60 @@ +{ + "Description": "Lossless state channel and component annotations.", + "IntendedFor": "./derivatives/BIDS-Lossless-EEG/./sub-004/eeg/sub-004_task-faceFO_eeg.edf", + "Sources": "See BUCANL GitHub", + "Author": "Lossless", + "LabelDescription": { + "chan_manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which channels are considered artifactual for any reason.", + "chan_ch_sd": "Pipeline decision flag indicating that channels were too often outliers compared to other channels for the measure of standard deviation of voltage within one second epochs.", + "chan_low_r": "Pipeline decision flag indicating that channels were too often outliers compared to other channels for the measure of correlation coefficient to spatially neighbouring channels within one second epochs.", + "chan_bridge": "Pipeline decision flag indicating that channels were outliers for in terms of having high and invariant correlation coefficients to spatially neighbouring channels.", + "chan_rank": "Pipeline decision identifying the channel that has the least amount of unique information (highest average correlation coefficient to spatially neighbouring channels) to be ignored by ICA in order to account for the rank deficiency of the average referenced data.", + "comp_manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which components are considered artifactual for any reason.", + "comp_ic_rt": "Pipeline decision flag indicating that components had a poor dipole fit (typically > %15 residual variance).", + "comp_brain": "ICLabel 0.3 Mark indicating that the compnents have cortical characteristics.", + "comp_muscle": "ICLabel 0.3 Mark indicating that the compnents have EMG characteristics.", + "comp_eye": "ICLabel 0.3 Mark indicating that the compnents have EOG characteristics.", + "comp_heart": "ICLabel 0.3 Mark indicating that the compnents have ECG characteristics.", + "comp_line_noise": "ICLabel 0.3 Mark indicating that the compnents have electrical mains noise characteristics.", + "comp_chan_noise": "ICLabel 0.3 Mark indicating that the compnents have channel independence characteristics.", + "comp_other": "ICLabel 0.3 Mark indicating that the compnents could not be confidently classified within any other ICLabel classification.", + "comp_ambig": "QC procedure rater markup indicating that the compnents are difficult to classify as either artifact or not.", + "in_task_fhbc_onset": "Based on experimental task events indicating time points within the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "in_leadup_fhbc_onset": "Based on experimental task events indicating time points leading into (a few second before and a few seconds after the first experimental event in a block of trials) the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "out_task": "Based on experimental task events indicating time points outside the duration of any experimental tasks (e.g. breaks, start up and end off times, etc).", + "in_task_fhbc_fade": "Based on experimental task events indicating time points within the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "in_leadup_fhbc_fade": "Based on experimental task events indicating time points leading into (a few second before and a few seconds after the first experimental event in a block of trials) the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "ic_hg": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral High Gama within one second epochs.", + "manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which time points are considered artifactual for any reason.", + "init_ind": "Continuous variable indicating the initial time point index within the session.", + "mark_gap": "Pipeline decision flag indicating that time points are within a short gap between other annotations.", + "ch_sd": "Pipeline decision flag indicating that time points were too often outliers across channels compared to other time points for the measure of standard deviation of voltage within one second epochs.", + "low_r": "Pipeline decision flag indicating that time points were too often outliers across channels compared to other time points for the measure of correlation coefficient to spatially neighbouring channels within one second epochs.", + "logl_init": "Log likelihood of initial AMICA decomposition.", + "ic_sd1": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component standard deviation of voltage within one second epochs.", + "logl_A": "Log likelihood of final AMICA decomposition (replication A).", + "logl_B": "Log likelihood of final AMICA decomposition (replication B).", + "logl_C": "Log likelihood of final AMICA decomposition (replication C).", + "ic_sd2": "Pipeline decision flag indicating that time points were too often outliers across final components compared to other time points for the measure of component standard deviation of voltage within one second epochs.", + "ic_dt": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral Theta within one second epochs.", + "ic_a": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral Alpha within one second epochs.", + "ic_b": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral Beta within one second epochs.", + "ic_lg": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral Low Gama within one second epochs." + }, + "SamplingFrequency": 512, + "StartTime": 0, + "Columns": [ + "init_ind", + "mark_gap", + "ch_sd", + "low_r", + "logl_init", + "ic_sd1", + "logl_A", + "logl_B", + "logl_C", + "ic_dt", + "ic_a", + "ic_b" + ] +} diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-004/eeg/sub-004_task-faceFO_annotations.mat b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-004/eeg/sub-004_task-faceFO_annotations.mat new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-004/eeg/sub-004_task-faceFO_annotations.tsv b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-004/eeg/sub-004_task-faceFO_annotations.tsv new file mode 100644 index 000000000..0d518bd20 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-004/eeg/sub-004_task-faceFO_annotations.tsv @@ -0,0 +1,50 @@ +onset duration label channels +n/a n/a chan_manual ["EEG009","EEG010","EEG011","EEG012","EEG048","EEG049","EEG050","EEG051","EEG065","EEG074","EEG097","EEG098","EEG127","EEG128"] +n/a n/a chan_ch_sd ["EEG065","EEG074"] +n/a n/a chan_bridge ["EEG009","EEG010","EEG011","EEG012","EEG048","EEG049","EEG050","EEG051","EEG097","EEG098","EEG127","EEG128"] +n/a n/a chan_rank ["EEG125"] +n/a n/a comp_manual 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a/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-004/eeg/sub-004_task-faceFO_desc-qc_annotations.json b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-004/eeg/sub-004_task-faceFO_desc-qc_annotations.json new file mode 100755 index 000000000..5b84a38dc --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-004/eeg/sub-004_task-faceFO_desc-qc_annotations.json @@ -0,0 +1,30 @@ +{ + "Description": "Lossless state channel and component annotations.", + "IntendedFor": "derivatives/BIDS-Lossless-EEG//./sub-004/eeg/sub-004_task-faceFO_eeg.edf", + "Sources": "See BUCANL GitHub", + "Author": "Lossless", + "LabelDescription": { + "chan_manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which channels are considered artifactual for any reason.", + "chan_ch_sd": "Pipeline decision flag indicating that channels were too often outliers compared to other channels for the measure of standard deviation of voltage within one second epochs.", + "chan_low_r": "Pipeline decision flag indicating that channels were too often outliers compared to other channels for the measure of correlation coefficient to spatially neighbouring channels within one second epochs.", + "chan_bridge": "Pipeline decision flag indicating that channels were outliers for in terms of having high and invariant correlation coefficients to spatially neighbouring channels.", + "chan_rank": "Pipeline decision identifying the channel that has the least amount of unique information (highest average correlation coefficient to spatially neighbouring channels) to be ignored by ICA in order to account for the rank deficiency of the average referenced data.", + "comp_manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which components are considered artifactual for any reason.", + "comp_ic_rt": "Pipeline decision flag indicating that components had a poor dipole fit (typically > %15 residual variance).", + "comp_brain": "ICLabel 0.3 Mark indicating that the compnents have cortical characteristics.", + "comp_muscle": "ICLabel 0.3 Mark indicating that the compnents have EMG characteristics.", + "comp_eye": "ICLabel 0.3 Mark indicating that the compnents have EOG characteristics.", + "comp_heart": "ICLabel 0.3 Mark indicating that the compnents have ECG characteristics.", + "comp_line_noise": "ICLabel 0.3 Mark indicating that the compnents have electrical mains noise characteristics.", + "comp_chan_noise": "ICLabel 0.3 Mark indicating that the compnents have channel independence characteristics.", + "comp_other": "ICLabel 0.3 Mark indicating that the compnents could not be confidently classified within any other ICLabel classification.", + "comp_ambig": "QC procedure rater markup indicating that the compnents are difficult to classify as either artifact or not.", + "in_task_fhbc_onset": "Based on experimental task events indicating time points within the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "in_leadup_fhbc_onset": "Based on experimental task events indicating time points leading into (a few second before and a few seconds after the first experimental event in a block of trials) the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "out_task": "Based on experimental task events indicating time points outside the duration of any experimental tasks (e.g. breaks, start up and end off times, etc).", + "in_task_fhbc_fade": "Based on experimental task events indicating time points within the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "in_leadup_fhbc_fade": "Based on experimental task events indicating time points leading into (a few second before and a few seconds after the first experimental event in a block of trials) the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "ic_hg": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral High Gama within one second epochs.", + "manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which time points are considered artifactual for any reason." + } +} diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-004/eeg/sub-004_task-faceFO_desc-qc_annotations.mat b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-004/eeg/sub-004_task-faceFO_desc-qc_annotations.mat new file mode 100755 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-004/eeg/sub-004_task-faceFO_desc-qc_annotations.tsv b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-004/eeg/sub-004_task-faceFO_desc-qc_annotations.tsv new file mode 100755 index 000000000..eb506e966 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-004/eeg/sub-004_task-faceFO_desc-qc_annotations.tsv @@ -0,0 +1,51 @@ +onset duration label channels +n/a n/a chan_manual ["EEG009","EEG010","EEG011","EEG012","EEG048","EEG049","EEG050","EEG051","EEG065","EEG074","EEG097","EEG098","EEG127","EEG128"] +n/a n/a chan_ch_sd ["EEG065","EEG074"] +n/a n/a chan_bridge ["EEG009","EEG010","EEG011","EEG012","EEG048","EEG049","EEG050","EEG051","EEG097","EEG098","EEG127","EEG128"] +n/a n/a chan_rank ["EEG125"] +n/a n/a comp_manual 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state channel and component annotations.", + "IntendedFor": "./derivatives/BIDS-Lossless-EEG/./sub-005/eeg/sub-005_task-faceFO_eeg.edf", + "Sources": "See BUCANL GitHub", + "Author": "Lossless", + "LabelDescription": { + "chan_manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which channels are considered artifactual for any reason.", + "chan_ch_sd": "Pipeline decision flag indicating that channels were too often outliers compared to other channels for the measure of standard deviation of voltage within one second epochs.", + "chan_low_r": "Pipeline decision flag indicating that channels were too often outliers compared to other channels for the measure of correlation coefficient to spatially neighbouring channels within one second epochs.", + "chan_bridge": "Pipeline decision flag indicating that channels were outliers for in terms of having high and invariant correlation coefficients to spatially neighbouring channels.", + "chan_rank": "Pipeline decision identifying the channel that has the least amount of unique information (highest average correlation coefficient to spatially neighbouring channels) to be ignored by ICA in order to account for the rank deficiency of the average referenced data.", + "comp_manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which components are considered artifactual for any reason.", + "comp_ic_rt": "Pipeline decision flag indicating that components had a poor dipole fit (typically > %15 residual variance).", + "comp_brain": "ICLabel 0.3 Mark indicating that the compnents have cortical characteristics.", + "comp_muscle": "ICLabel 0.3 Mark indicating that the compnents have EMG characteristics.", + "comp_eye": "ICLabel 0.3 Mark indicating that the compnents have EOG characteristics.", + "comp_heart": "ICLabel 0.3 Mark indicating that the compnents have ECG characteristics.", + "comp_line_noise": "ICLabel 0.3 Mark indicating that the compnents have electrical mains noise characteristics.", + "comp_chan_noise": "ICLabel 0.3 Mark indicating that the compnents have channel independence characteristics.", + "comp_other": "ICLabel 0.3 Mark indicating that the compnents could not be confidently classified within any other ICLabel classification.", + "comp_ambig": "QC procedure rater markup indicating that the compnents are difficult to classify as either artifact or not.", + "in_task_fhbc_onset": "Based on experimental task events indicating time points within the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "in_leadup_fhbc_onset": "Based on experimental task events indicating time points leading into (a few second before and a few seconds after the first experimental event in a block of trials) the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "out_task": "Based on experimental task events indicating time points outside the duration of any experimental tasks (e.g. breaks, start up and end off times, etc).", + "in_task_fhbc_fade": "Based on experimental task events indicating time points within the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "in_leadup_fhbc_fade": "Based on experimental task events indicating time points leading into (a few second before and a few seconds after the first experimental event in a block of trials) the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "ic_hg": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral High Gama within one second epochs.", + "manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which time points are considered artifactual for any reason.", + "init_ind": "Continuous variable indicating the initial time point index within the session.", + "mark_gap": "Pipeline decision flag indicating that time points are within a short gap between other annotations.", + "ch_sd": "Pipeline decision flag indicating that time points were too often outliers across channels compared to other time points for the measure of standard deviation of voltage within one second epochs.", + "low_r": "Pipeline decision flag indicating that time points were too often outliers across channels compared to other time points for the measure of correlation coefficient to spatially neighbouring channels within one second epochs.", + "logl_init": "Log likelihood of initial AMICA decomposition.", + "ic_sd1": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component standard deviation of voltage within one second epochs.", + "logl_A": "Log likelihood of final AMICA decomposition (replication A).", + "logl_B": "Log likelihood of final AMICA decomposition (replication B).", + "logl_C": "Log likelihood of final AMICA decomposition (replication C).", + "ic_sd2": "Pipeline decision flag indicating that time points were too often outliers across final components compared to other time points for the measure of component standard deviation of voltage within one second epochs.", + "ic_dt": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral Theta within one second epochs.", + "ic_a": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral Alpha within one second epochs.", + "ic_b": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral Beta within one second epochs.", + "ic_lg": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral Low Gama within one second epochs." + }, + "SamplingFrequency": 512, + "StartTime": 0, + "Columns": [ + "init_ind", + "mark_gap", + "ch_sd", + "low_r", + "logl_init", + "ic_sd1", + "logl_A", + "logl_B", + "logl_C", + "ic_sd2", + "ic_dt", + "ic_a", + "ic_b" + ] +} diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-005/eeg/sub-005_task-faceFO_annotations.mat b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-005/eeg/sub-005_task-faceFO_annotations.mat new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-005/eeg/sub-005_task-faceFO_annotations.tsv b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-005/eeg/sub-005_task-faceFO_annotations.tsv new file mode 100644 index 000000000..7e1e97c1d --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-005/eeg/sub-005_task-faceFO_annotations.tsv @@ -0,0 +1,55 @@ +onset duration label channels +n/a n/a chan_manual ["EEG009","EEG010","EEG011","EEG012","EEG014","EEG015","EEG023","EEG024","EEG025","EEG026","EEG036","EEG037","EEG042","EEG048","EEG049","EEG094","EEG095","EEG097","EEG098","EEG119","EEG120","EEG123","EEG124","EEG125","EEG126","EEG127","EEG128"] +n/a n/a chan_ch_sd ["EEG042","EEG119","EEG120","EEG123","EEG124","EEG125","EEG126"] +n/a n/a chan_bridge ["EEG009","EEG010","EEG011","EEG012","EEG014","EEG015","EEG023","EEG024","EEG025","EEG026","EEG036","EEG037","EEG048","EEG049","EEG094","EEG095","EEG097","EEG098","EEG127","EEG128"] +n/a n/a chan_rank ["EEG121"] +n/a n/a comp_manual 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GitHub", + "Author": "Lossless", + "LabelDescription": { + "chan_manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which channels are considered artifactual for any reason.", + "chan_ch_sd": "Pipeline decision flag indicating that channels were too often outliers compared to other channels for the measure of standard deviation of voltage within one second epochs.", + "chan_low_r": "Pipeline decision flag indicating that channels were too often outliers compared to other channels for the measure of correlation coefficient to spatially neighbouring channels within one second epochs.", + "chan_bridge": "Pipeline decision flag indicating that channels were outliers for in terms of having high and invariant correlation coefficients to spatially neighbouring channels.", + "chan_rank": "Pipeline decision identifying the channel that has the least amount of unique information (highest average correlation coefficient to spatially neighbouring channels) to be ignored by ICA in order to account for the rank deficiency of the average referenced data.", + "comp_manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which components are considered artifactual for any reason.", + "comp_ic_rt": "Pipeline decision flag indicating that components had a poor dipole fit (typically > %15 residual variance).", + "comp_brain": "ICLabel 0.3 Mark indicating that the compnents have cortical characteristics.", + "comp_muscle": "ICLabel 0.3 Mark indicating that the compnents have EMG characteristics.", + "comp_eye": "ICLabel 0.3 Mark indicating that the compnents have EOG characteristics.", + "comp_heart": "ICLabel 0.3 Mark indicating that the compnents have ECG characteristics.", + "comp_line_noise": "ICLabel 0.3 Mark indicating that the compnents have electrical mains noise characteristics.", + "comp_chan_noise": "ICLabel 0.3 Mark indicating that the compnents have channel independence characteristics.", + "comp_other": "ICLabel 0.3 Mark indicating that the compnents could not be confidently classified within any other ICLabel classification.", + "comp_ambig": "QC procedure rater markup indicating that the compnents are difficult to classify as either artifact or not.", + "in_task_fhbc_onset": "Based on experimental task events indicating time points within the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "in_leadup_fhbc_onset": "Based on experimental task events indicating time points leading into (a few second before and a few seconds after the first experimental event in a block of trials) the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "out_task": "Based on experimental task events indicating time points outside the duration of any experimental tasks (e.g. breaks, start up and end off times, etc).", + "in_task_fhbc_fade": "Based on experimental task events indicating time points within the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "in_leadup_fhbc_fade": "Based on experimental task events indicating time points leading into (a few second before and a few seconds after the first experimental event in a block of trials) the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "ic_hg": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral High Gama within one second epochs.", + "manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which time points are considered artifactual for any reason." + } +} diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-005/eeg/sub-005_task-faceFO_desc-qc_annotations.mat b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-005/eeg/sub-005_task-faceFO_desc-qc_annotations.mat new file mode 100755 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-005/eeg/sub-005_task-faceFO_desc-qc_annotations.tsv b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-005/eeg/sub-005_task-faceFO_desc-qc_annotations.tsv new file mode 100755 index 000000000..6f29fab6b --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-005/eeg/sub-005_task-faceFO_desc-qc_annotations.tsv @@ -0,0 +1,57 @@ +onset duration label channels +n/a n/a chan_manual ["EEG009","EEG010","EEG011","EEG012","EEG014","EEG015","EEG023","EEG024","EEG025","EEG026","EEG036","EEG037","EEG042","EEG048","EEG049","EEG094","EEG095","EEG097","EEG098","EEG119","EEG120","EEG123","EEG124","EEG125","EEG126","EEG127","EEG128"] +n/a n/a chan_ch_sd 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"The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which channels are considered artifactual for any reason.", + "chan_ch_sd": "Pipeline decision flag indicating that channels were too often outliers compared to other channels for the measure of standard deviation of voltage within one second epochs.", + "chan_low_r": "Pipeline decision flag indicating that channels were too often outliers compared to other channels for the measure of correlation coefficient to spatially neighbouring channels within one second epochs.", + "chan_bridge": "Pipeline decision flag indicating that channels were outliers for in terms of having high and invariant correlation coefficients to spatially neighbouring channels.", + "chan_rank": "Pipeline decision identifying the channel that has the least amount of unique information (highest average correlation coefficient to spatially neighbouring channels) to be ignored by ICA in order to account for the rank deficiency of the average referenced data.", + "comp_manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which components are considered artifactual for any reason.", + "comp_ic_rt": "Pipeline decision flag indicating that components had a poor dipole fit (typically > %15 residual variance).", + "comp_brain": "ICLabel 0.3 Mark indicating that the compnents have cortical characteristics.", + "comp_muscle": "ICLabel 0.3 Mark indicating that the compnents have EMG characteristics.", + "comp_eye": "ICLabel 0.3 Mark indicating that the compnents have EOG characteristics.", + "comp_heart": "ICLabel 0.3 Mark indicating that the compnents have ECG characteristics.", + "comp_line_noise": "ICLabel 0.3 Mark indicating that the compnents have electrical mains noise characteristics.", + "comp_chan_noise": "ICLabel 0.3 Mark indicating that the compnents have channel independence characteristics.", + "comp_other": "ICLabel 0.3 Mark indicating that the compnents could not be confidently classified within any other ICLabel classification.", + "comp_ambig": "QC procedure rater markup indicating that the compnents are difficult to classify as either artifact or not.", + "in_task_fhbc_onset": "Based on experimental task events indicating time points within the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "in_leadup_fhbc_onset": "Based on experimental task events indicating time points leading into (a few second before and a few seconds after the first experimental event in a block of trials) the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "out_task": "Based on experimental task events indicating time points outside the duration of any experimental tasks (e.g. breaks, start up and end off times, etc).", + "in_task_fhbc_fade": "Based on experimental task events indicating time points within the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "in_leadup_fhbc_fade": "Based on experimental task events indicating time points leading into (a few second before and a few seconds after the first experimental event in a block of trials) the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "ic_hg": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral High Gama within one second epochs.", + "manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which time points are considered artifactual for any reason.", + "init_ind": "Continuous variable indicating the initial time point index within the session.", + "mark_gap": "Pipeline decision flag indicating that time points are within a short gap between other annotations.", + "ch_sd": "Pipeline decision flag indicating that time points were too often outliers across channels compared to other time points for the measure of standard deviation of voltage within one second epochs.", + "low_r": "Pipeline decision flag indicating that time points were too often outliers across channels compared to other time points for the measure of correlation coefficient to spatially neighbouring channels within one second epochs.", + "logl_init": "Log likelihood of initial AMICA decomposition.", + "ic_sd1": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component standard deviation of voltage within one second epochs.", + "logl_A": "Log likelihood of final AMICA decomposition (replication A).", + "logl_B": "Log likelihood of final AMICA decomposition (replication B).", + "logl_C": "Log likelihood of final AMICA decomposition (replication C).", + "ic_sd2": "Pipeline decision flag indicating that time points were too often outliers across final components compared to other time points for the measure of component standard deviation of voltage within one second epochs.", + "ic_dt": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral Theta within one second epochs.", + "ic_a": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral Alpha within one second epochs.", + "ic_b": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral Beta within one second epochs.", + "ic_lg": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral Low Gama within one second epochs." + }, + "SamplingFrequency": 512, + "StartTime": 0, + "Columns": [ + "init_ind", + "mark_gap", + "ch_sd", + "low_r", + "logl_init", + "ic_sd1", + "logl_A", + "logl_B", + "logl_C", + "ic_sd2", + "ic_dt", + "ic_a", + "ic_b", + "ic_lg", + "ic_hg" + ] +} diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-006/eeg/sub-006_task-faceFO_annotations.mat b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-006/eeg/sub-006_task-faceFO_annotations.mat new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-006/eeg/sub-006_task-faceFO_annotations.tsv b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-006/eeg/sub-006_task-faceFO_annotations.tsv new file mode 100644 index 000000000..9f09a85cd --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-006/eeg/sub-006_task-faceFO_annotations.tsv @@ -0,0 +1,46 @@ +onset duration label channels +n/a n/a chan_manual ["EEG009","EEG010","EEG011","EEG012","EEG025","EEG026","EEG128"] +n/a n/a chan_bridge ["EEG009","EEG010","EEG011","EEG012","EEG025","EEG026","EEG128"] +n/a n/a chan_rank ["EEG127"] +n/a n/a comp_manual ["ICA002","ICA007","ICA010","ICA013","ICA014","ICA016","ICA020","ICA021","ICA024","ICA031","ICA033","ICA034","ICA035","ICA038","ICA039","ICA040","ICA042","ICA044","ICA045","ICA048","ICA049","ICA050","ICA053","ICA054","ICA055","ICA058","ICA059","ICA060","ICA061","ICA062","ICA063","ICA066","ICA067","ICA068","ICA070","ICA071","ICA072","ICA073","ICA074","ICA075","ICA077","ICA078","ICA079","ICA080","ICA081","ICA082","ICA083","ICA084","ICA085","ICA086","ICA087","ICA088","ICA090","ICA091","ICA092","ICA093","ICA094","ICA095","ICA096","ICA097","ICA099","ICA100","ICA101","ICA103","ICA104","ICA105","ICA106","ICA107","ICA108","ICA110","ICA111","ICA112","ICA113","ICA114","ICA115","ICA117","ICA118","ICA119","ICA120"] +n/a n/a comp_ic_rt ["ICA005","ICA009","ICA011","ICA012","ICA015","ICA017","ICA018","ICA022","ICA023","ICA025","ICA026","ICA027","ICA028","ICA029","ICA030","ICA031","ICA033","ICA035","ICA036","ICA037","ICA038","ICA039","ICA041","ICA043","ICA044","ICA045","ICA046","ICA047","ICA049","ICA050","ICA051","ICA052","ICA054","ICA057","ICA058","ICA062","ICA063","ICA064","ICA065","ICA069","ICA070","ICA072","ICA073","ICA076","ICA077","ICA082","ICA085","ICA089","ICA091","ICA093","ICA097","ICA098","ICA102","ICA106","ICA107","ICA108","ICA109","ICA112","ICA113","ICA115","ICA117","ICA118","ICA119"] +n/a n/a comp_brain ["ICA001","ICA003","ICA004","ICA005","ICA006","ICA008","ICA009","ICA011","ICA012","ICA015","ICA017","ICA018","ICA019","ICA022","ICA023","ICA025","ICA026","ICA027","ICA028","ICA029","ICA030","ICA032","ICA037","ICA041","ICA043","ICA046","ICA047","ICA051","ICA052","ICA057","ICA064","ICA069","ICA076","ICA089","ICA098","ICA109","ICA116"] +n/a n/a comp_muscle ["ICA010","ICA013","ICA014","ICA016","ICA020","ICA021","ICA031","ICA040","ICA055","ICA060","ICA075","ICA080","ICA081","ICA084","ICA096","ICA100"] +n/a n/a comp_eye ["ICA002","ICA007","ICA048","ICA101"] +n/a n/a comp_chan_noise ["ICA024","ICA033","ICA034","ICA035","ICA038","ICA039","ICA042","ICA044","ICA045","ICA049","ICA050","ICA053","ICA054","ICA058","ICA059","ICA061","ICA062","ICA063","ICA066","ICA067","ICA068","ICA070","ICA071","ICA072","ICA073","ICA074","ICA077","ICA078","ICA079","ICA082","ICA083","ICA085","ICA086","ICA087","ICA088","ICA090","ICA091","ICA092","ICA093","ICA094","ICA095","ICA097","ICA099","ICA103","ICA104","ICA105","ICA106","ICA107","ICA108","ICA110","ICA111","ICA112","ICA113","ICA114","ICA115","ICA117","ICA118","ICA119","ICA120"] +n/a n/a comp_other ["ICA036","ICA056","ICA065","ICA102"] +0.0019531 21.3633 manual n/a +0.0019531 14.3633 out_task n/a +13.3633 8.002 in_leadup_fhbc_fade n/a +14.3652 225.3086 in_task_fhbc_fade n/a +239.6738 40.9102 manual n/a +239.6738 33.9102 out_task n/a +272.582 8.002 in_leadup_fhbc_fade n/a +273.584 224.9082 in_task_fhbc_fade n/a +498.4922 37.0879 manual n/a +498.4922 30.0879 out_task n/a +527.5781 8.002 in_leadup_fhbc_fade n/a +528.5801 221.6953 in_task_fhbc_fade n/a +750.2754 39.9785 manual n/a +750.2754 32.9785 out_task n/a +782.252 8.002 in_leadup_fhbc_fade n/a +783.2539 223.8594 in_task_fhbc_fade n/a +1007.1133 33.0918 manual n/a +1007.1133 26.0918 out_task n/a +1032.2031 8.002 in_leadup_fhbc_onset n/a +1033.2051 272.209 in_task_fhbc_onset n/a +1305.4141 31.8066 manual n/a +1305.4141 24.8066 out_task n/a +1329.2188 8.002 in_leadup_fhbc_onset n/a +1330.2207 269.8438 in_task_fhbc_onset n/a +1598.002 40.541 manual n/a +1600.0645 31.4785 out_task n/a +1630.541 8.002 in_leadup_fhbc_onset n/a +1631.543 271.2402 in_task_fhbc_onset n/a +1902.7832 39.4941 manual n/a +1902.7832 32.4941 out_task n/a +1934.2754 8.002 in_leadup_fhbc_onset n/a +1935.2773 269.5938 in_task_fhbc_onset n/a +2000.002 4 manual n/a +2204.8711 15.1309 manual n/a +2204.8711 15.1309 out_task n/a diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-006/eeg/sub-006_task-faceFO_desc-qc_annotations.json b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-006/eeg/sub-006_task-faceFO_desc-qc_annotations.json new file mode 100755 index 000000000..2c21bdddb --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-006/eeg/sub-006_task-faceFO_desc-qc_annotations.json @@ -0,0 +1,30 @@ +{ + "Description": "Lossless state channel and component annotations.", + "IntendedFor": "derivatives/BIDS-Lossless-EEG//./sub-006/eeg/sub-006_task-faceFO_eeg.edf", + "Sources": "See BUCANL GitHub", + "Author": "Lossless", + "LabelDescription": { + "chan_manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which channels are considered artifactual for any reason.", + "chan_ch_sd": "Pipeline decision flag indicating that channels were too often outliers compared to other channels for the measure of standard deviation of voltage within one second epochs.", + "chan_low_r": "Pipeline decision flag indicating that channels were too often outliers compared to other channels for the measure of correlation coefficient to spatially neighbouring channels within one second epochs.", + "chan_bridge": "Pipeline decision flag indicating that channels were outliers for in terms of having high and invariant correlation coefficients to spatially neighbouring channels.", + "chan_rank": "Pipeline decision identifying the channel that has the least amount of unique information (highest average correlation coefficient to spatially neighbouring channels) to be ignored by ICA in order to account for the rank deficiency of the average referenced data.", + "comp_manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which components are considered artifactual for any reason.", + "comp_ic_rt": "Pipeline decision flag indicating that components had a poor dipole fit (typically > %15 residual variance).", + "comp_brain": "ICLabel 0.3 Mark indicating that the compnents have cortical characteristics.", + "comp_muscle": "ICLabel 0.3 Mark indicating that the compnents have EMG characteristics.", + "comp_eye": "ICLabel 0.3 Mark indicating that the compnents have EOG characteristics.", + "comp_heart": "ICLabel 0.3 Mark indicating that the compnents have ECG characteristics.", + "comp_line_noise": "ICLabel 0.3 Mark indicating that the compnents have electrical mains noise characteristics.", + "comp_chan_noise": "ICLabel 0.3 Mark indicating that the compnents have channel independence characteristics.", + "comp_other": "ICLabel 0.3 Mark indicating that the compnents could not be confidently classified within any other ICLabel classification.", + "comp_ambig": "QC procedure rater markup indicating that the compnents are difficult to classify as either artifact or not.", + "in_task_fhbc_onset": "Based on experimental task events indicating time points within the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "in_leadup_fhbc_onset": "Based on experimental task events indicating time points leading into (a few second before and a few seconds after the first experimental event in a block of trials) the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "out_task": "Based on experimental task events indicating time points outside the duration of any experimental tasks (e.g. breaks, start up and end off times, etc).", + "in_task_fhbc_fade": "Based on experimental task events indicating time points within the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "in_leadup_fhbc_fade": "Based on experimental task events indicating time points leading into (a few second before and a few seconds after the first experimental event in a block of trials) the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "ic_hg": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral High Gama within one second epochs.", + "manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which time points are considered artifactual for any reason." + } +} diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-006/eeg/sub-006_task-faceFO_desc-qc_annotations.mat b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-006/eeg/sub-006_task-faceFO_desc-qc_annotations.mat new file mode 100755 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-006/eeg/sub-006_task-faceFO_desc-qc_annotations.tsv b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-006/eeg/sub-006_task-faceFO_desc-qc_annotations.tsv new file mode 100755 index 000000000..4146b3987 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-006/eeg/sub-006_task-faceFO_desc-qc_annotations.tsv @@ -0,0 +1,47 @@ +onset duration label channels +n/a n/a chan_manual ["EEG009","EEG010","EEG011","EEG012","EEG025","EEG026","EEG128"] +n/a n/a chan_bridge ["EEG009","EEG010","EEG011","EEG012","EEG025","EEG026","EEG128"] +n/a n/a chan_rank ["EEG127"] +n/a n/a comp_manual ["ICA002","ICA004","ICA006","ICA007","ICA011","ICA013","ICA014","ICA016","ICA020","ICA022","ICA024","ICA030","ICA031","ICA034","ICA036","ICA038","ICA039","ICA040","ICA042","ICA044","ICA045","ICA046","ICA047","ICA048","ICA049","ICA051","ICA052","ICA053","ICA054","ICA055","ICA056","ICA057","ICA058","ICA059","ICA060","ICA061","ICA062","ICA063","ICA064","ICA065","ICA066","ICA067","ICA068","ICA069","ICA070","ICA071","ICA072","ICA073","ICA074","ICA075","ICA076","ICA077","ICA078","ICA079","ICA080","ICA081","ICA082","ICA083","ICA084","ICA085","ICA086","ICA087","ICA088","ICA089","ICA090","ICA091","ICA092","ICA093","ICA094","ICA095","ICA096","ICA097","ICA098","ICA099","ICA100","ICA101","ICA102","ICA103","ICA104","ICA105","ICA106","ICA107","ICA108","ICA109","ICA110","ICA111","ICA112","ICA113","ICA114","ICA115","ICA116","ICA117","ICA118","ICA119","ICA120"] +n/a n/a comp_ic_rt ["ICA008","ICA009","ICA010","ICA012","ICA015","ICA017","ICA018","ICA021","ICA023","ICA025","ICA026","ICA027","ICA028","ICA029","ICA031","ICA032","ICA033","ICA035","ICA036","ICA037","ICA039","ICA041","ICA043","ICA044","ICA045","ICA047","ICA048","ICA049","ICA050","ICA052","ICA053","ICA054","ICA057","ICA059","ICA062","ICA063","ICA065","ICA067","ICA070","ICA071","ICA078","ICA080","ICA083","ICA088","ICA090","ICA091","ICA093","ICA097","ICA099","ICA102","ICA106","ICA107","ICA108","ICA109","ICA112","ICA113","ICA116","ICA118","ICA119"] +n/a n/a comp_brain ["ICA001","ICA003","ICA004","ICA005","ICA006","ICA008","ICA009","ICA010","ICA012","ICA015","ICA017","ICA018","ICA019","ICA021","ICA023","ICA025","ICA026","ICA027","ICA028","ICA029","ICA030","ICA032","ICA037","ICA041","ICA043","ICA044","ICA049","ICA050","ICA054","ICA059","ICA063","ICA067","ICA078","ICA097","ICA109","ICA115"] +n/a n/a comp_muscle ["ICA011","ICA013","ICA014","ICA016","ICA020","ICA022","ICA031","ICA040","ICA055","ICA060","ICA074","ICA079","ICA081","ICA084","ICA096","ICA100"] +n/a n/a comp_eye ["ICA002","ICA007","ICA046","ICA101"] +n/a n/a comp_chan_noise ["ICA024","ICA033","ICA034","ICA035","ICA038","ICA039","ICA042","ICA045","ICA047","ICA048","ICA051","ICA052","ICA053","ICA057","ICA058","ICA061","ICA062","ICA065","ICA066","ICA068","ICA069","ICA070","ICA071","ICA072","ICA073","ICA075","ICA076","ICA077","ICA080","ICA082","ICA083","ICA085","ICA086","ICA087","ICA088","ICA089","ICA090","ICA091","ICA092","ICA093","ICA094","ICA095","ICA098","ICA099","ICA103","ICA104","ICA105","ICA106","ICA107","ICA108","ICA110","ICA111","ICA112","ICA113","ICA114","ICA116","ICA117","ICA118","ICA119","ICA120"] +n/a n/a comp_other ["ICA036","ICA056","ICA064","ICA102"] +0.0019531 21.3652 manual n/a +0.0019531 14.3652 out_task n/a +13.3633 8.0039 in_leadup_fhbc_fade n/a +14.3652 225.3105 in_task_fhbc_fade n/a +30.8516 0.71289 manual n/a 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0.001707199566653528 -0.0003852527375156271 0.004181119130997679 -0.0004662218276522744 diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-006/eeg/sub-006_task-faceFO_iclabel.mat b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-006/eeg/sub-006_task-faceFO_iclabel.mat new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-007/eeg/sub-007_task-faceFO_annotations.json b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-007/eeg/sub-007_task-faceFO_annotations.json new file mode 100644 index 000000000..19fcb1c8a --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-007/eeg/sub-007_task-faceFO_annotations.json @@ -0,0 +1,61 @@ +{ + "Description": "Lossless state channel and component annotations.", + "IntendedFor": "./derivatives/BIDS-Lossless-EEG/./sub-007/eeg/sub-007_task-faceFO_eeg.edf", + "Sources": "See BUCANL GitHub", + "Author": "Lossless", + "LabelDescription": { + "chan_manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which channels are considered artifactual for any reason.", + "chan_ch_sd": "Pipeline decision flag indicating that channels were too often outliers compared to other channels for the measure of standard deviation of voltage within one second epochs.", + "chan_low_r": "Pipeline decision flag indicating that channels were too often outliers compared to other channels for the measure of correlation coefficient to spatially neighbouring channels within one second epochs.", + "chan_bridge": "Pipeline decision flag indicating that channels were outliers for in terms of having high and invariant correlation coefficients to spatially neighbouring channels.", + "chan_rank": "Pipeline decision identifying the channel that has the least amount of unique information (highest average correlation coefficient to spatially neighbouring channels) to be ignored by ICA in order to account for the rank deficiency of the average referenced data.", + "comp_manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which components are considered artifactual for any reason.", + "comp_ic_rt": "Pipeline decision flag indicating that components had a poor dipole fit (typically > %15 residual variance).", + "comp_brain": "ICLabel 0.3 Mark indicating that the compnents have cortical characteristics.", + "comp_muscle": "ICLabel 0.3 Mark indicating that the compnents have EMG characteristics.", + "comp_eye": "ICLabel 0.3 Mark indicating that the compnents have EOG characteristics.", + "comp_heart": "ICLabel 0.3 Mark indicating that the compnents have ECG characteristics.", + "comp_line_noise": "ICLabel 0.3 Mark indicating that the compnents have electrical mains noise characteristics.", + "comp_chan_noise": "ICLabel 0.3 Mark indicating that the compnents have channel independence characteristics.", + "comp_other": "ICLabel 0.3 Mark indicating that the compnents could not be confidently classified within any other ICLabel classification.", + "comp_ambig": "QC procedure rater markup indicating that the compnents are difficult to classify as either artifact or not.", + "in_task_fhbc_onset": "Based on experimental task events indicating time points within the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "in_leadup_fhbc_onset": "Based on experimental task events indicating time points leading into (a few second before and a few seconds after the first experimental event in a block of trials) the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "out_task": "Based on experimental task events indicating time points outside the duration of any experimental tasks (e.g. breaks, start up and end off times, etc).", + "in_task_fhbc_fade": "Based on experimental task events indicating time points within the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "in_leadup_fhbc_fade": "Based on experimental task events indicating time points leading into (a few second before and a few seconds after the first experimental event in a block of trials) the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "ic_hg": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral High Gama within one second epochs.", + "manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which time points are considered artifactual for any reason.", + "init_ind": "Continuous variable indicating the initial time point index within the session.", + "mark_gap": "Pipeline decision flag indicating that time points are within a short gap between other annotations.", + "ch_sd": "Pipeline decision flag indicating that time points were too often outliers across channels compared to other time points for the measure of standard deviation of voltage within one second epochs.", + "low_r": "Pipeline decision flag indicating that time points were too often outliers across channels compared to other time points for the measure of correlation coefficient to spatially neighbouring channels within one second epochs.", + "logl_init": "Log likelihood of initial AMICA decomposition.", + "ic_sd1": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component standard deviation of voltage within one second epochs.", + "logl_A": "Log likelihood of final AMICA decomposition (replication A).", + "logl_B": "Log likelihood of final AMICA decomposition (replication B).", + "logl_C": "Log likelihood of final AMICA decomposition (replication C).", + "ic_sd2": "Pipeline decision flag indicating that time points were too often outliers across final components compared to other time points for the measure of component standard deviation of voltage within one second epochs.", + "ic_dt": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral Theta within one second epochs.", + "ic_a": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral Alpha within one second epochs.", + "ic_b": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral Beta within one second epochs.", + "ic_lg": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral Low Gama within one second epochs." + }, + "SamplingFrequency": 512, + "StartTime": 0, + "Columns": [ + "init_ind", + "ch_sd", + "low_r", + "logl_init", + "ic_sd1", + "logl_A", + "logl_B", + "logl_C", + "ic_dt", + "ic_a", + "ic_b", + "ic_lg", + "ic_hg" + ] +} diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-007/eeg/sub-007_task-faceFO_annotations.mat b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-007/eeg/sub-007_task-faceFO_annotations.mat new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-007/eeg/sub-007_task-faceFO_annotations.tsv b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-007/eeg/sub-007_task-faceFO_annotations.tsv new file mode 100644 index 000000000..86c5834b3 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-007/eeg/sub-007_task-faceFO_annotations.tsv @@ -0,0 +1,59 @@ +onset duration label 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experimental task events indicating time points outside the duration of any experimental tasks (e.g. breaks, start up and end off times, etc).", + "in_task_fhbc_fade": "Based on experimental task events indicating time points within the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "in_leadup_fhbc_fade": "Based on experimental task events indicating time points leading into (a few second before and a few seconds after the first experimental event in a block of trials) the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "ic_hg": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral High Gama within one second epochs.", + "manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which time points are considered artifactual for any reason." + } +} diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-007/eeg/sub-007_task-faceFO_desc-qc_annotations.mat b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-007/eeg/sub-007_task-faceFO_desc-qc_annotations.mat new file mode 100755 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-007/eeg/sub-007_task-faceFO_desc-qc_annotations.tsv b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-007/eeg/sub-007_task-faceFO_desc-qc_annotations.tsv new file mode 100755 index 000000000..c5175438c --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-007/eeg/sub-007_task-faceFO_desc-qc_annotations.tsv @@ -0,0 +1,62 @@ +onset duration label channels +n/a n/a chan_manual ["EEG008","EEG009","EEG011","EEG013","EEG023","EEG024","EEG026","EEG028","EEG029","EEG030","EEG048","EEG049","EEG050","EEG051","EEG106","EEG107","EEG126","EEG127"] +n/a n/a chan_bridge 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-0.005725625633009165 0.0001722685950439866 -0.00487609220311841 0.0005379349639706918 0.0006332850510531306 diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-007/eeg/sub-007_task-faceFO_iclabel.mat b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-007/eeg/sub-007_task-faceFO_iclabel.mat new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-008/eeg/sub-008_task-faceFO_annotations.json b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-008/eeg/sub-008_task-faceFO_annotations.json new file mode 100644 index 000000000..c42edff79 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-008/eeg/sub-008_task-faceFO_annotations.json @@ -0,0 +1,59 @@ +{ + "Description": "Lossless state channel and component annotations.", + "IntendedFor": "./derivatives/BIDS-Lossless-EEG/./sub-008/eeg/sub-008_task-faceFO_eeg.edf", + "Sources": "See BUCANL GitHub", + "Author": "Lossless", + "LabelDescription": { + "chan_manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which channels are considered artifactual for any reason.", + "chan_ch_sd": "Pipeline decision flag indicating that channels were too often outliers compared to other channels for the measure of standard deviation of voltage within one second epochs.", + "chan_low_r": "Pipeline decision flag indicating that channels were too often outliers compared to other channels for the measure of correlation coefficient to spatially neighbouring channels within one second epochs.", + "chan_bridge": "Pipeline decision flag indicating that channels were outliers for in terms of having high and invariant correlation coefficients to spatially neighbouring channels.", + "chan_rank": "Pipeline decision identifying the channel that has the least amount of unique information (highest average correlation coefficient to spatially neighbouring channels) to be ignored by ICA in order to account for the rank deficiency of the average referenced data.", + "comp_manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which components are considered artifactual for any reason.", + "comp_ic_rt": "Pipeline decision flag indicating that components had a poor dipole fit (typically > %15 residual variance).", + "comp_brain": "ICLabel 0.3 Mark indicating that the compnents have cortical characteristics.", + "comp_muscle": "ICLabel 0.3 Mark indicating that the compnents have EMG characteristics.", + "comp_eye": "ICLabel 0.3 Mark indicating that the compnents have EOG characteristics.", + "comp_heart": "ICLabel 0.3 Mark indicating that the compnents have ECG characteristics.", + "comp_line_noise": "ICLabel 0.3 Mark indicating that the compnents have electrical mains noise characteristics.", + "comp_chan_noise": "ICLabel 0.3 Mark indicating that the compnents have channel independence characteristics.", + "comp_other": "ICLabel 0.3 Mark indicating that the compnents could not be confidently classified within any other ICLabel classification.", + "comp_ambig": "QC procedure rater markup indicating that the compnents are difficult to classify as either artifact or not.", + "in_task_fhbc_onset": "Based on experimental task events indicating time points within the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "in_leadup_fhbc_onset": "Based on experimental task events indicating time points leading into (a few second before and a few seconds after the first experimental event in a block of trials) the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "out_task": "Based on experimental task events indicating time points outside the duration of any experimental tasks (e.g. breaks, start up and end off times, etc).", + "in_task_fhbc_fade": "Based on experimental task events indicating time points within the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "in_leadup_fhbc_fade": "Based on experimental task events indicating time points leading into (a few second before and a few seconds after the first experimental event in a block of trials) the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "ic_hg": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral High Gama within one second epochs.", + "manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which time points are considered artifactual for any reason.", + "init_ind": "Continuous variable indicating the initial time point index within the session.", + "mark_gap": "Pipeline decision flag indicating that time points are within a short gap between other annotations.", + "ch_sd": "Pipeline decision flag indicating that time points were too often outliers across channels compared to other time points for the measure of standard deviation of voltage within one second epochs.", + "low_r": "Pipeline decision flag indicating that time points were too often outliers across channels compared to other time points for the measure of correlation coefficient to spatially neighbouring channels within one second epochs.", + "logl_init": "Log likelihood of initial AMICA decomposition.", + "ic_sd1": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component standard deviation of voltage within one second epochs.", + "logl_A": "Log likelihood of final AMICA decomposition (replication A).", + "logl_B": "Log likelihood of final AMICA decomposition (replication B).", + "logl_C": "Log likelihood of final AMICA decomposition (replication C).", + "ic_sd2": "Pipeline decision flag indicating that time points were too often outliers across final components compared to other time points for the measure of component standard deviation of voltage within one second epochs.", + "ic_dt": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral Theta within one second epochs.", + "ic_a": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral Alpha within one second epochs.", + "ic_b": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral Beta within one second epochs.", + "ic_lg": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral Low Gama within one second epochs." + }, + "SamplingFrequency": 512, + "StartTime": 0, + "Columns": [ + "init_ind", + "ch_sd", + "low_r", + "logl_init", + "ic_sd1", + "logl_A", + "logl_B", + "logl_C", + "ic_dt", + "ic_a", + "ic_b" + ] +} diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-008/eeg/sub-008_task-faceFO_annotations.mat b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-008/eeg/sub-008_task-faceFO_annotations.mat new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-008/eeg/sub-008_task-faceFO_annotations.tsv b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-008/eeg/sub-008_task-faceFO_annotations.tsv new file mode 100644 index 000000000..21cca601d --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-008/eeg/sub-008_task-faceFO_annotations.tsv @@ -0,0 +1,65 @@ +onset duration label channels +n/a n/a chan_manual ["EEG009","EEG010","EEG048","EEG049","EEG050","EEG051","EEG059","EEG060","EEG106","EEG107","EEG127","EEG128"] +n/a n/a chan_bridge ["EEG009","EEG010","EEG048","EEG049","EEG050","EEG051","EEG059","EEG060","EEG106","EEG107","EEG127","EEG128"] +n/a n/a chan_rank ["EEG065"] +n/a n/a comp_manual 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"derivatives/BIDS-Lossless-EEG//./sub-008/eeg/sub-008_task-faceFO_eeg.edf", + "Sources": "See BUCANL GitHub", + "Author": "Lossless", + "LabelDescription": { + "chan_manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which channels are considered artifactual for any reason.", + "chan_ch_sd": "Pipeline decision flag indicating that channels were too often outliers compared to other channels for the measure of standard deviation of voltage within one second epochs.", + "chan_low_r": "Pipeline decision flag indicating that channels were too often outliers compared to other channels for the measure of correlation coefficient to spatially neighbouring channels within one second epochs.", + "chan_bridge": "Pipeline decision flag indicating that channels were outliers for in terms of having high and invariant correlation coefficients to spatially neighbouring channels.", + "chan_rank": "Pipeline decision identifying the channel that has the least amount of unique information (highest average correlation coefficient to spatially neighbouring channels) to be ignored by ICA in order to account for the rank deficiency of the average referenced data.", + "comp_manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which components are considered artifactual for any reason.", + "comp_ic_rt": "Pipeline decision flag indicating that components had a poor dipole fit (typically > %15 residual variance).", + "comp_brain": "ICLabel 0.3 Mark indicating that the compnents have cortical characteristics.", + "comp_muscle": "ICLabel 0.3 Mark indicating that the compnents have EMG characteristics.", + "comp_eye": "ICLabel 0.3 Mark indicating that the compnents have EOG characteristics.", + "comp_heart": "ICLabel 0.3 Mark indicating that the compnents have ECG characteristics.", + "comp_line_noise": 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experimental task events indicating time points outside the duration of any experimental tasks (e.g. breaks, start up and end off times, etc).", + "in_task_fhbc_fade": "Based on experimental task events indicating time points within the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "in_leadup_fhbc_fade": "Based on experimental task events indicating time points leading into (a few second before and a few seconds after the first experimental event in a block of trials) the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "ic_hg": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral High Gama within one second epochs.", + "manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which time points are considered artifactual for any reason." + } +} diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-008/eeg/sub-008_task-faceFO_desc-qc_annotations.mat b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-008/eeg/sub-008_task-faceFO_desc-qc_annotations.mat new file mode 100755 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-008/eeg/sub-008_task-faceFO_desc-qc_annotations.tsv b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-008/eeg/sub-008_task-faceFO_desc-qc_annotations.tsv new file mode 100755 index 000000000..0cc4601cf --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-008/eeg/sub-008_task-faceFO_desc-qc_annotations.tsv @@ -0,0 +1,63 @@ +onset duration label channels +n/a n/a chan_manual ["EEG009","EEG010","EEG048","EEG049","EEG050","EEG051","EEG059","EEG060","EEG106","EEG107","EEG127","EEG128"] +n/a n/a chan_bridge 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/dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-009/eeg/sub-009_task-faceFO_annotations.json @@ -0,0 +1,57 @@ +{ + "Description": "Lossless state channel and component annotations.", + "IntendedFor": "./derivatives/BIDS-Lossless-EEG/./sub-009/eeg/sub-009_task-faceFO_eeg.edf", + "Sources": "See BUCANL GitHub", + "Author": "Lossless", + "LabelDescription": { + "chan_manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which channels are considered artifactual for any reason.", + "chan_ch_sd": "Pipeline decision flag indicating that channels were too often outliers compared to other channels for the measure of standard deviation of voltage within one second epochs.", + "chan_low_r": "Pipeline decision flag indicating that channels were too often outliers compared to other channels for the measure of correlation coefficient to spatially neighbouring channels within one second epochs.", + "chan_bridge": "Pipeline decision flag indicating that channels were outliers for in terms of having high and invariant correlation coefficients to spatially neighbouring channels.", + "chan_rank": "Pipeline decision identifying the channel that has the least amount of unique information (highest average correlation coefficient to spatially neighbouring channels) to be ignored by ICA in order to account for the rank deficiency of the average referenced data.", + "comp_manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which components are considered artifactual for any reason.", + "comp_ic_rt": "Pipeline decision flag indicating that components had a poor dipole fit (typically > %15 residual variance).", + "comp_brain": "ICLabel 0.3 Mark indicating that the compnents have cortical characteristics.", + "comp_muscle": "ICLabel 0.3 Mark indicating that the compnents have EMG characteristics.", + "comp_eye": "ICLabel 0.3 Mark indicating that the compnents have EOG characteristics.", + "comp_heart": "ICLabel 0.3 Mark indicating that the compnents have ECG characteristics.", + "comp_line_noise": "ICLabel 0.3 Mark indicating that the compnents have electrical mains noise characteristics.", + "comp_chan_noise": "ICLabel 0.3 Mark indicating that the compnents have channel independence characteristics.", + "comp_other": "ICLabel 0.3 Mark indicating that the compnents could not be confidently classified within any other ICLabel classification.", + "comp_ambig": "QC procedure rater markup indicating that the compnents are difficult to classify as either artifact or not.", + "in_task_fhbc_onset": "Based on experimental task events indicating time points within the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "in_leadup_fhbc_onset": "Based on experimental task events indicating time points leading into (a few second before and a few seconds after the first experimental event in a block of trials) the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "out_task": "Based on experimental task events indicating time points outside the duration of any experimental tasks (e.g. breaks, start up and end off times, etc).", + "in_task_fhbc_fade": "Based on experimental task events indicating time points within the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "in_leadup_fhbc_fade": "Based on experimental task events indicating time points leading into (a few second before and a few seconds after the first experimental event in a block of trials) the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "ic_hg": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of 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"ic_sd1": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component standard deviation of voltage within one second epochs.", + "logl_A": "Log likelihood of final AMICA decomposition (replication A).", + "logl_B": "Log likelihood of final AMICA decomposition (replication B).", + "logl_C": "Log likelihood of final AMICA decomposition (replication C).", + "ic_sd2": "Pipeline decision flag indicating that time points were too often outliers across final components compared to other time points for the measure of component standard deviation of voltage within one second epochs.", + "ic_dt": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral Theta within one second epochs.", + "ic_a": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral Alpha within one second epochs.", + "ic_b": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral Beta within one second epochs.", + "ic_lg": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral Low Gama within one second epochs." + }, + "SamplingFrequency": 512, + "StartTime": 0, + "Columns": [ + "init_ind", + "ch_sd", + "low_r", + "logl_init", + "ic_sd1", + "logl_A", + "logl_B", + "logl_C", + "ic_sd2" + ] +} diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-009/eeg/sub-009_task-faceFO_annotations.mat b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-009/eeg/sub-009_task-faceFO_annotations.mat new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-009/eeg/sub-009_task-faceFO_annotations.tsv b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-009/eeg/sub-009_task-faceFO_annotations.tsv new file mode 100644 index 000000000..7dcc61978 --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-009/eeg/sub-009_task-faceFO_annotations.tsv @@ -0,0 +1,86 @@ +onset duration label channels +n/a n/a chan_manual ["EEG025","EEG026","EEG042","EEG048","EEG049","EEG059","EEG060","EEG097","EEG098","EEG101","EEG102","EEG106","EEG107","EEG125","EEG126","EEG127","EEG128"] +n/a n/a chan_low_r ["EEG042"] +n/a n/a chan_bridge ["EEG025","EEG026","EEG048","EEG049","EEG059","EEG060","EEG097","EEG098","EEG101","EEG102","EEG106","EEG107","EEG125","EEG126","EEG127","EEG128"] +n/a n/a chan_rank ["EEG078"] +n/a n/a comp_manual 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state channel and component annotations.", + "IntendedFor": "derivatives/BIDS-Lossless-EEG//./sub-009/eeg/sub-009_task-faceFO_eeg.edf", + "Sources": "See BUCANL GitHub", + "Author": "Lossless", + "LabelDescription": { + "chan_manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which channels are considered artifactual for any reason.", + "chan_ch_sd": "Pipeline decision flag indicating that channels were too often outliers compared to other channels for the measure of standard deviation of voltage within one second epochs.", + "chan_low_r": "Pipeline decision flag indicating that channels were too often outliers compared to other channels for the measure of correlation coefficient to spatially neighbouring channels within one second epochs.", + "chan_bridge": "Pipeline decision flag indicating that channels were outliers for in terms of having high and invariant correlation coefficients to spatially neighbouring channels.", + "chan_rank": "Pipeline decision identifying the channel that has the least amount of unique information (highest average correlation coefficient to spatially neighbouring channels) to be ignored by ICA in order to account for the rank deficiency of the average referenced data.", + "comp_manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which components are considered artifactual for any reason.", + "comp_ic_rt": "Pipeline decision flag indicating that components had a poor dipole fit (typically > %15 residual variance).", + "comp_brain": "ICLabel 0.3 Mark indicating that the compnents have cortical characteristics.", + "comp_muscle": "ICLabel 0.3 Mark indicating that the compnents have EMG characteristics.", + "comp_eye": "ICLabel 0.3 Mark indicating that the compnents have EOG characteristics.", + "comp_heart": "ICLabel 0.3 Mark indicating that the 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typical onset stimulus series).", + "out_task": "Based on experimental task events indicating time points outside the duration of any experimental tasks (e.g. breaks, start up and end off times, etc).", + "in_task_fhbc_fade": "Based on experimental task events indicating time points within the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "in_leadup_fhbc_fade": "Based on experimental task events indicating time points leading into (a few second before and a few seconds after the first experimental event in a block of trials) the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "ic_hg": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral High Gama within one second epochs.", + "manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which time points are considered artifactual for any reason." + } +} diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-009/eeg/sub-009_task-faceFO_desc-qc_annotations.mat b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-009/eeg/sub-009_task-faceFO_desc-qc_annotations.mat new file mode 100755 index 000000000..e69de29bb diff --git a/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-009/eeg/sub-009_task-faceFO_desc-qc_annotations.tsv b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-009/eeg/sub-009_task-faceFO_desc-qc_annotations.tsv new file mode 100755 index 000000000..46fc046ca --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-009/eeg/sub-009_task-faceFO_desc-qc_annotations.tsv @@ -0,0 +1,91 @@ +onset duration label channels +n/a n/a chan_manual ["EEG025","EEG026","EEG042","EEG048","EEG049","EEG059","EEG060","EEG097","EEG098","EEG101","EEG102","EEG106","EEG107","EEG125","EEG126","EEG127","EEG128"] +n/a 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state channel and component annotations.", + "IntendedFor": "./derivatives/BIDS-Lossless-EEG/./sub-010/eeg/sub-010_task-faceFO_eeg.edf", + "Sources": "See BUCANL GitHub", + "Author": "Lossless", + "LabelDescription": { + "chan_manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which channels are considered artifactual for any reason.", + "chan_ch_sd": "Pipeline decision flag indicating that channels were too often outliers compared to other channels for the measure of standard deviation of voltage within one second epochs.", + "chan_low_r": "Pipeline decision flag indicating that channels were too often outliers compared to other channels for the measure of correlation coefficient to spatially neighbouring channels within one second epochs.", + "chan_bridge": "Pipeline decision flag indicating that channels were outliers for in terms of having high and invariant correlation coefficients to spatially neighbouring channels.", + "chan_rank": "Pipeline decision identifying the channel that has the least amount of unique information (highest average correlation coefficient to spatially neighbouring channels) to be ignored by ICA in order to account for the rank deficiency of the average referenced data.", + "comp_manual": "The interactive label (modified by analysts interactively or by a pipeline decision along with other labels) typically used to indicate which components are considered artifactual for any reason.", + "comp_ic_rt": "Pipeline decision flag indicating that components had a poor dipole fit (typically > %15 residual variance).", + "comp_brain": "ICLabel 0.3 Mark indicating that the compnents have cortical characteristics.", + "comp_muscle": "ICLabel 0.3 Mark indicating that the compnents have EMG characteristics.", + "comp_eye": "ICLabel 0.3 Mark indicating that the compnents have EOG characteristics.", + "comp_heart": "ICLabel 0.3 Mark indicating that the compnents have ECG characteristics.", + "comp_line_noise": "ICLabel 0.3 Mark indicating that the compnents have electrical mains noise characteristics.", + "comp_chan_noise": "ICLabel 0.3 Mark indicating that the compnents have channel independence characteristics.", + "comp_other": "ICLabel 0.3 Mark indicating that the compnents could not be confidently classified within any other ICLabel classification.", + "comp_ambig": "QC procedure rater markup indicating that the compnents are difficult to classify as either artifact or not.", + "in_task_fhbc_onset": "Based on experimental task events indicating time points within the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "in_leadup_fhbc_onset": "Based on experimental task events indicating time points leading into (a few second before and a few seconds after the first experimental event in a block of trials) the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "out_task": "Based on experimental task events indicating time points outside the duration of any experimental tasks (e.g. breaks, start up and end off times, etc).", + "in_task_fhbc_fade": "Based on experimental task events indicating time points within the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "in_leadup_fhbc_fade": "Based on experimental task events indicating time points leading into (a few second before and a few seconds after the first experimental event in a block of trials) the duration of the fhbc_onset tasks (Face House Butterfly Checkerboard typical onset stimulus series).", + "ic_hg": "Pipeline decision flag indicating that time points were too often outliers across initial components compared to other time points for the measure of component spectral High Gama within one second epochs.", + "manual": "The interactive label (modified by analysts interactively or by a 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b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-010/eeg/sub-010_task-faceFO_annotations.tsv new file mode 100644 index 000000000..aeedbd5bb --- /dev/null +++ b/eeg_face13/derivatives/BIDS-Lossless-EEG/sub-010/eeg/sub-010_task-faceFO_annotations.tsv @@ -0,0 +1,53 @@ +onset duration label channels +n/a n/a chan_manual ["EEG009","EEG010","EEG018","EEG042","EEG108"] +n/a n/a chan_ch_sd ["EEG018","EEG042","EEG108"] +n/a n/a chan_bridge ["EEG009","EEG010"] +n/a n/a chan_rank ["EEG074"] +n/a n/a comp_manual 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analysts interactively or by a pipeline decision along with other labels) typically used to indicate which components are considered artifactual for any reason.", + "comp_ic_rt": "Pipeline decision flag indicating that components had a poor dipole fit (typically > %15 residual variance).", + "comp_brain": "ICLabel 0.3 Mark indicating that the compnents have cortical characteristics.", + "comp_muscle": "ICLabel 0.3 Mark indicating that the compnents have EMG characteristics.", + "comp_eye": "ICLabel 0.3 Mark indicating that the compnents have EOG characteristics.", + "comp_heart": "ICLabel 0.3 Mark indicating that the compnents have ECG characteristics.", + "comp_line_noise": "ICLabel 0.3 Mark indicating that the compnents have electrical mains noise characteristics.", + "comp_chan_noise": "ICLabel 0.3 Mark indicating that the compnents have channel independence characteristics.", + "comp_other": "ICLabel 0.3 Mark indicating that the compnents could not be confidently classified within 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b/eeg_face13/sourcedata/eeg/IC_trn_P07_O_3.bdf new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/eeg/IC_trn_P07_O_4.bdf b/eeg_face13/sourcedata/eeg/IC_trn_P07_O_4.bdf new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/eeg/IC_trn_P08_F_1.bdf b/eeg_face13/sourcedata/eeg/IC_trn_P08_F_1.bdf new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/eeg/IC_trn_P08_F_2.bdf b/eeg_face13/sourcedata/eeg/IC_trn_P08_F_2.bdf new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/eeg/IC_trn_P08_F_3.bdf b/eeg_face13/sourcedata/eeg/IC_trn_P08_F_3.bdf new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/eeg/IC_trn_P08_F_4.bdf b/eeg_face13/sourcedata/eeg/IC_trn_P08_F_4.bdf new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/eeg/IC_trn_P08_O_1.bdf b/eeg_face13/sourcedata/eeg/IC_trn_P08_O_1.bdf new file mode 100644 index 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b/eeg_face13/sourcedata/eeg/IC_trn_P09_F_4.bdf new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/eeg/IC_trn_P09_O_1.bdf b/eeg_face13/sourcedata/eeg/IC_trn_P09_O_1.bdf new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/eeg/IC_trn_P09_O_2.bdf b/eeg_face13/sourcedata/eeg/IC_trn_P09_O_2.bdf new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/eeg/IC_trn_P09_O_3.bdf b/eeg_face13/sourcedata/eeg/IC_trn_P09_O_3.bdf new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/eeg/IC_trn_P09_O_4.bdf b/eeg_face13/sourcedata/eeg/IC_trn_P09_O_4.bdf new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/eeg/IC_trn_P10_F_1.bdf b/eeg_face13/sourcedata/eeg/IC_trn_P10_F_1.bdf new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/eeg/IC_trn_P10_F_2.bdf b/eeg_face13/sourcedata/eeg/IC_trn_P10_F_2.bdf new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/eeg/IC_trn_P10_F_3.bdf b/eeg_face13/sourcedata/eeg/IC_trn_P10_F_3.bdf new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/eeg/IC_trn_P10_F_4.bdf b/eeg_face13/sourcedata/eeg/IC_trn_P10_F_4.bdf new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/eeg/IC_trn_P10_O_1.bdf b/eeg_face13/sourcedata/eeg/IC_trn_P10_O_1.bdf new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/eeg/IC_trn_P10_O_2.bdf b/eeg_face13/sourcedata/eeg/IC_trn_P10_O_2.bdf new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/eeg/IC_trn_P10_O_3.bdf b/eeg_face13/sourcedata/eeg/IC_trn_P10_O_3.bdf new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/eeg/IC_trn_P10_O_4.bdf b/eeg_face13/sourcedata/eeg/IC_trn_P10_O_4.bdf new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/eeg/RawData_NameChange_log.txt b/eeg_face13/sourcedata/eeg/RawData_NameChange_log.txt new file mode 100644 index 000000000..f4e0ee59e --- /dev/null +++ b/eeg_face13/sourcedata/eeg/RawData_NameChange_log.txt @@ -0,0 +1,3 @@ +File name changes in this folder: + +*P01b* = *P01* diff --git a/eeg_face13/sourcedata/eeg/sub-s01.log b/eeg_face13/sourcedata/eeg/sub-s01.log new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/eeg/sub-s01_task-faceFO_eeg.fdt b/eeg_face13/sourcedata/eeg/sub-s01_task-faceFO_eeg.fdt new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/eeg/sub-s01_task-faceFO_eeg.set b/eeg_face13/sourcedata/eeg/sub-s01_task-faceFO_eeg.set new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/eeg/sub-s02.log b/eeg_face13/sourcedata/eeg/sub-s02.log new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/eeg/sub-s02_task-faceFO_eeg.fdt b/eeg_face13/sourcedata/eeg/sub-s02_task-faceFO_eeg.fdt new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/eeg/sub-s02_task-faceFO_eeg.set b/eeg_face13/sourcedata/eeg/sub-s02_task-faceFO_eeg.set new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/eeg/sub-s03.log b/eeg_face13/sourcedata/eeg/sub-s03.log new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/eeg/sub-s03_task-faceFO_eeg.fdt b/eeg_face13/sourcedata/eeg/sub-s03_task-faceFO_eeg.fdt new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/eeg/sub-s03_task-faceFO_eeg.set b/eeg_face13/sourcedata/eeg/sub-s03_task-faceFO_eeg.set new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/eeg/sub-s04.log b/eeg_face13/sourcedata/eeg/sub-s04.log new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/eeg/sub-s04_task-faceFO_eeg.fdt b/eeg_face13/sourcedata/eeg/sub-s04_task-faceFO_eeg.fdt new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/eeg/sub-s04_task-faceFO_eeg.set b/eeg_face13/sourcedata/eeg/sub-s04_task-faceFO_eeg.set new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/eeg/sub-s05.log b/eeg_face13/sourcedata/eeg/sub-s05.log new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/eeg/sub-s05_task-faceFO_eeg.fdt b/eeg_face13/sourcedata/eeg/sub-s05_task-faceFO_eeg.fdt new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/eeg/sub-s05_task-faceFO_eeg.set b/eeg_face13/sourcedata/eeg/sub-s05_task-faceFO_eeg.set new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/eeg/sub-s06.log b/eeg_face13/sourcedata/eeg/sub-s06.log new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/eeg/sub-s06_task-faceFO_eeg.fdt b/eeg_face13/sourcedata/eeg/sub-s06_task-faceFO_eeg.fdt new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/eeg/sub-s06_task-faceFO_eeg.set b/eeg_face13/sourcedata/eeg/sub-s06_task-faceFO_eeg.set new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/eeg/sub-s07.log b/eeg_face13/sourcedata/eeg/sub-s07.log new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/eeg/sub-s07_task-faceFO_eeg.fdt b/eeg_face13/sourcedata/eeg/sub-s07_task-faceFO_eeg.fdt new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/eeg/sub-s07_task-faceFO_eeg.set b/eeg_face13/sourcedata/eeg/sub-s07_task-faceFO_eeg.set new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/eeg/sub-s08.log b/eeg_face13/sourcedata/eeg/sub-s08.log new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/eeg/sub-s08_task-faceFO_eeg.fdt b/eeg_face13/sourcedata/eeg/sub-s08_task-faceFO_eeg.fdt new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/eeg/sub-s08_task-faceFO_eeg.set b/eeg_face13/sourcedata/eeg/sub-s08_task-faceFO_eeg.set new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/eeg/sub-s09.log b/eeg_face13/sourcedata/eeg/sub-s09.log new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/eeg/sub-s09_task-faceFO_eeg.fdt b/eeg_face13/sourcedata/eeg/sub-s09_task-faceFO_eeg.fdt new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/eeg/sub-s09_task-faceFO_eeg.set b/eeg_face13/sourcedata/eeg/sub-s09_task-faceFO_eeg.set new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/eeg/sub-s10.log b/eeg_face13/sourcedata/eeg/sub-s10.log new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/eeg/sub-s10_task-faceFO_eeg.fdt b/eeg_face13/sourcedata/eeg/sub-s10_task-faceFO_eeg.fdt new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/eeg/sub-s10_task-faceFO_eeg.set b/eeg_face13/sourcedata/eeg/sub-s10_task-faceFO_eeg.set new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/misc/BioSemi_BUCANL_EEGLAB.sfp b/eeg_face13/sourcedata/misc/BioSemi_BUCANL_EEGLAB.sfp new file mode 100644 index 000000000..9fbcb53ce --- /dev/null +++ b/eeg_face13/sourcedata/misc/BioSemi_BUCANL_EEGLAB.sfp @@ -0,0 +1,132 @@ + LPA -0.069295 0.00087368 0 + Nz 0 0.098284 0 + RPA 0.069295 -0.00087368 0 + A1 -0.00068421 -0.0050053 0.12047 + A2 -0.00075263 -0.026495 0.11643 + A3 -0.026032 -0.028968 0.1102 + A4 -0.017532 -0.049416 0.10142 + A5 -0.024447 -0.064237 0.084753 + A6 -0.024058 -0.076089 0.067879 + A7 -0.023958 -0.0822 0.048742 + A8 -0.021821 -0.083279 0.029479 + A9 -0.021284 -0.080247 0.010511 + A10 -0.018879 -0.077363 -0.0073474 + A11 0.0004 -0.081574 -0.0054526 + A12 -0.00015263 -0.084058 0.011289 + A13 -6.3158e-05 -0.087337 0.030005 + A14 -0.00025789 -0.087205 0.047021 + A15 -0.00036316 -0.082616 0.064374 + A16 -0.00051579 -0.073474 0.0805 + A17 -0.00035789 -0.060221 0.095589 + A18 -0.00066316 -0.044963 0.10774 + A19 0.025121 -0.027437 0.11193 + A20 0.016642 -0.050311 0.10164 + A21 0.023258 -0.064947 0.085653 + A22 0.023168 -0.076816 0.068605 + A23 0.022847 -0.0827 0.0502 + A24 0.022411 -0.083447 0.028742 + A25 0.020195 -0.081216 0.0101 + A26 0.020274 -0.078279 -0.0074579 + A27 0.038342 -0.064895 -0.0093789 + A28 0.039784 -0.069511 0.0083789 + A29 0.042142 -0.070911 0.028811 + A30 0.042042 -0.069674 0.048821 + A31 0.042368 -0.063579 0.067089 + A32 0.043389 -0.051779 0.084226 + B1 0.0532 -0.050474 0.062232 + B2 0.055426 -0.053695 0.041968 + B3 0.056789 -0.052505 0.021868 + B4 0.054263 -0.048853 0.0044 + B5 0.051379 -0.045274 -0.013963 + B6 0.065216 -0.026211 0.020884 + B7 0.064053 -0.025979 0.042011 + B8 0.061268 -0.029421 0.061095 + B9 0.055837 -0.032558 0.078947 + B10 0.045284 -0.035168 0.095274 + B11 0.046789 -0.014026 0.10136 + B12 0.058242 -0.0071421 0.085832 + B13 0.064195 -0.0034632 0.068279 + B14 0.066863 -8.9474e-05 0.049863 + B15 0.066879 0.0058316 0.031937 + B16 0.063532 0.010347 0.013795 + B17 0.061247 0.012358 -0.0032316 + B18 0.059905 0.038921 0.0099263 + B19 0.062047 0.036647 0.028353 + B20 0.063495 0.032005 0.047147 + B21 0.061884 0.028468 0.065932 + B22 0.055979 0.022642 0.084074 + B23 0.045195 0.0072895 0.10255 + B24 0.042837 0.029479 0.098268 + B25 0.054037 0.0484 0.072147 + B26 0.055779 0.058579 0.054221 + B27 0.055274 0.062979 0.036016 + B28 0.053532 0.064426 0.017832 + B29 0.042495 0.086237 0.043689 + B30 0.041795 0.079932 0.061589 + B31 0.042858 0.066205 0.076895 + B32 0.041889 0.0505 0.089753 + C1 0.041005 0.088516 0.026542 + C2 0.022263 0.10298 0.031558 + C3 0.023679 0.10131 0.050453 + C4 0.023505 0.092426 0.070316 + C5 0.024068 0.077321 0.085989 + C6 0.022674 0.059763 0.098579 + C7 0.023579 0.038284 0.10807 + C8 0.024958 0.015721 0.11438 + C9 0.025558 -0.0063368 0.11615 + C10 0.00063158 0.016174 0.11971 + C11 0.00067368 0.036342 0.11419 + C12 -0.00028947 0.056126 0.10593 + C13 -0.00022632 0.074268 0.095479 + C14 -0.00048947 0.089042 0.083421 + C15 -0.00052632 0.10061 0.068442 + C16 -0.0010474 0.10725 0.0502 + C17 -0.00099474 0.10682 0.032221 + C18 -0.022516 0.10249 0.032837 + C19 -0.023126 0.10057 0.052663 + C20 -0.023053 0.091684 0.071363 + C21 -0.022563 0.076895 0.087105 + C22 -0.022653 0.058579 0.099605 + C23 -0.022474 0.038263 0.10854 + C24 -0.024253 0.015968 0.11422 + C25 -0.024784 -0.0076579 0.11585 + C26 -0.045111 0.0077053 0.10184 + C27 -0.042911 0.030321 0.097821 + C28 -0.042116 0.050153 0.0896 + C29 -0.041621 0.066742 0.078089 + C30 -0.041932 0.0798 0.062442 + C31 -0.042121 0.087211 0.044584 + C32 -0.042095 0.088884 0.027595 + D1 -0.053426 0.065174 0.019889 + D2 -0.055321 0.062747 0.037605 + D3 -0.054489 0.058963 0.055742 + D4 -0.053242 0.049 0.073321 + D5 -0.055663 0.023411 0.084158 + D6 -0.061558 0.028705 0.066226 + D7 -0.063063 0.032963 0.046826 + D8 -0.062005 0.036416 0.028205 + D9 -0.060174 0.038832 0.010495 + D10 -0.061437 0.0123 -0.0052737 + D11 -0.064416 0.010353 0.013316 + D12 -0.066811 0.0055684 0.032042 + D13 -0.066626 -0.00023684 0.050426 + D14 -0.0637 -0.0029632 0.068584 + D15 -0.057626 -0.0061737 0.085726 + D16 -0.046411 -0.013926 0.10048 + D17 -0.046047 -0.035263 0.093179 + D18 -0.056442 -0.032068 0.077426 + D19 -0.061805 -0.029042 0.059384 + D20 -0.064163 -0.026542 0.041121 + D21 -0.064116 -0.0261 0.022211 + D22 -0.051247 -0.044953 -0.013 + D23 -0.054268 -0.048916 0.0044263 + D24 -0.055879 -0.051495 0.023379 + D25 -0.054416 -0.053011 0.041837 + D26 -0.053168 -0.049495 0.061979 + D27 -0.044026 -0.051532 0.081732 + D28 -0.042368 -0.063337 0.065716 + D29 -0.041374 -0.069084 0.047074 + D30 -0.040295 -0.070432 0.027689 + D31 -0.038247 -0.069311 0.0086947 + D32 -0.035721 -0.065042 -0.0084632 + diff --git a/eeg_face13/sourcedata/misc/standard_1005.elc b/eeg_face13/sourcedata/misc/standard_1005.elc new file mode 100644 index 000000000..4e695328b --- /dev/null +++ b/eeg_face13/sourcedata/misc/standard_1005.elc @@ -0,0 +1,698 @@ +# ASA electrode file +ReferenceLabel avg +UnitPosition mm +NumberPositions= 346 +Positions +-86.0761 -19.9897 -47.9860 +85.7939 -20.0093 -48.0310 +0.0083 86.8110 -39.9830 +-29.4367 83.9171 -6.9900 +0.1123 88.2470 -1.7130 +29.8723 84.8959 -7.0800 +-48.9708 64.0872 -47.6830 +-54.8397 68.5722 -10.5900 +-45.4307 72.8622 5.9780 +-33.7007 76.8371 21.2270 +-18.4717 79.9041 32.7520 +0.2313 80.7710 35.4170 +19.8203 80.3019 32.7640 +35.7123 77.7259 21.9560 +46.5843 73.8078 6.0340 +55.7433 69.6568 -10.7550 +50.4352 63.8698 -48.0050 +-70.1019 41.6523 -49.9520 +-70.2629 42.4743 -11.4200 +-64.4658 48.0353 16.9210 +-50.2438 53.1112 42.1920 +-27.4958 56.9311 60.3420 +0.3122 58.5120 66.4620 +29.5142 57.6019 59.5400 +51.8362 54.3048 40.8140 +67.9142 49.8297 16.3670 +73.0431 44.4217 -12.0000 +72.1141 42.0667 -50.4520 +-84.0759 14.5673 -50.4290 +-80.7750 14.1203 -11.1350 +-77.2149 18.6433 24.4600 +-60.1819 22.7162 55.5440 +-34.0619 26.0111 79.9870 +0.3761 27.3900 88.6680 +34.7841 26.4379 78.8080 +62.2931 23.7228 55.6300 +79.5341 19.9357 24.4380 +81.8151 15.4167 -11.3300 +84.1131 14.3647 -50.5380 +-85.8941 -15.8287 -48.2830 +-84.1611 -16.0187 -9.3460 +-80.2801 -13.7597 29.1600 +-65.3581 -11.6317 64.3580 +-36.1580 -9.9839 89.7520 +0.4009 -9.1670 100.2440 +37.6720 -9.6241 88.4120 +67.1179 -10.9003 63.5800 +83.4559 -12.7763 29.2080 +85.0799 -15.0203 -9.4900 +85.5599 -16.3613 -48.2710 +-85.6192 -46.5147 -45.7070 +-84.8302 -46.0217 -7.0560 +-79.5922 -46.5507 30.9490 +-63.5562 -47.0088 65.6240 +-35.5131 -47.2919 91.3150 +0.3858 -47.3180 99.4320 +38.3838 -47.0731 90.6950 +66.6118 -46.6372 65.5800 +83.3218 -46.1013 31.2060 +85.5488 -45.5453 -7.1300 +86.1618 -47.0353 -45.8690 +-73.0093 -73.7657 -40.9980 +-72.4343 -73.4527 -2.4870 +-67.2723 -76.2907 28.3820 +-53.0073 -78.7878 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41.1880 +-11.4357 70.7561 50.3480 +13.4793 71.2010 51.1750 +36.1833 69.1509 41.2540 +52.3972 65.0708 22.8620 +62.9152 60.0448 0.6300 +64.3342 54.5998 -30.4440 +-79.0669 28.0813 -31.2530 +-74.4999 31.3003 4.8460 +-65.2379 36.4282 36.1440 +-44.4098 40.7622 61.6900 +-15.4238 43.6600 77.6820 +17.5922 44.0540 77.7880 +45.8532 41.6228 60.6470 +67.1281 37.7998 35.2960 +78.0531 32.9817 4.4830 +80.0971 28.5137 -31.3380 +-84.1250 -1.8467 -29.7940 +-82.3550 0.8263 8.5790 +-74.6920 4.3033 45.3070 +-51.0509 7.1772 74.3770 +-18.2190 9.0941 92.5290 +18.7870 9.2479 91.5620 +51.8851 7.7978 73.5070 +77.0020 5.3357 45.3500 +83.8880 1.9457 8.5010 +84.1230 -1.8083 -29.6380 +-86.9731 -32.2157 -27.8480 +-85.5651 -30.6287 11.1530 +-76.4071 -29.7307 49.2170 +-52.9281 -28.9058 80.3040 +-18.3541 -28.3219 98.2200 +20.2199 -28.1481 98.1720 +55.1139 -28.3862 80.4740 +79.0059 -28.9863 49.6280 +85.9999 -29.8203 11.2480 +88.6249 -32.2723 -28.0000 +-78.1602 -60.7567 -23.8240 +-76.6802 -60.8317 12.8800 +-68.1152 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generated with E-Studio interface. +' E-Prime Copyright © 1996-2005 Psychology Software Tools. +' ALL RIGHTS RESERVED +' +' Legal use of this experiment script requires a full E-Prime or Runtime License. +' +' +' Author: +' (Brock University) +' +' +' +' + +Option CStrings On +Dim ebContext as Context + + +'-------------------------------------------------------------------------- +' Class Declarations +'-------------------------------------------------------------------------- + + + +'-------------------------------------------------------------------------- +'InitSlideStateDefaults +'-------------------------------------------------------------------------- +Sub InitSlideStateDefaults(theSlideState As SlideState) + + If theSlideState Is Nothing Then Exit Sub + + theSlideState.X = "center" + theSlideState.Y = "center" + theSlideState.Width = "100%" + theSlideState.Height = "100%" + theSlideState.BackColor = CColor("white") + theSlideState.BackStyle = "opaque" + theSlideState.BorderColor = CColor("black") + theSlideState.BorderWidth = CLng("0") + theSlideState.XAlign = "center" + theSlideState.YAlign = "center" + theSlideState.ClearAfter = CLogical("No") + theSlideState.Enabled = CLogical("Yes") + +End Sub + +'-------------------------------------------------------------------------- +'InitSlideVisualStimDefaults +'-------------------------------------------------------------------------- +Sub InitSlideVisualStimDefaults(theSlideVisualStim As SlideVisualStim) + + If theSlideVisualStim Is Nothing Then Exit Sub + + theSlideVisualStim.X = "center" + theSlideVisualStim.Y = "center" + theSlideVisualStim.Width = "25%" + theSlideVisualStim.Height = "25%" + theSlideVisualStim.ForeColor = CColor("black") + theSlideVisualStim.BackColor = CColor("white") + theSlideVisualStim.BackStyle = "opaque" + theSlideVisualStim.BorderColor = CColor("black") + theSlideVisualStim.BorderWidth = CLng("0") + theSlideVisualStim.XAlign = "center" + theSlideVisualStim.YAlign = "center" + theSlideVisualStim.AlignHorizontal = "center" + theSlideVisualStim.AlignVertical = "center" + +End Sub + +'-------------------------------------------------------------------------- +'InitSlideTextDefaults +'-------------------------------------------------------------------------- +Sub InitSlideTextDefaults(theSlideText As SlideText) + + If theSlideText Is Nothing Then Exit Sub + + InitSlideVisualStimDefaults theSlideText + + theSlideText.WordWrap = True + theSlideText.FontName = "Courier New" + theSlideText.FontSize = "18" + theSlideText.FontBold = CLogical("Yes") + theSlideText.FontItalic = CLogical("No") + theSlideText.FontUnderline = CLogical("No") + theSlideText.FontStrikeout = CLogical("No") + +End Sub + +'-------------------------------------------------------------------------- +'InitSlideImageDefaults +'-------------------------------------------------------------------------- +Sub InitSlideImageDefaults(theSlideImage As SlideImage) + + If theSlideImage Is Nothing Then Exit Sub + + InitSlideVisualStimDefaults theSlideImage + + theSlideImage.Stretch = CLogical("No") + theSlideImage.MirrorLeftRight = CLogical("No") + theSlideImage.MirrorUpDown = CLogical("No") + theSlideImage.UseSourceColorKey = CLogical("No") + theSlideImage.SourceColorKey = CColor("black") + +End Sub + +'-------------------------------------------------------------------------- +'InitSlideSoundBufferDefaults +'-------------------------------------------------------------------------- +Sub InitSlideSoundBufferDefaults(theSoundBuffer As SoundBuffer) + + If theSoundBuffer Is Nothing Then Exit Sub + + theSoundBuffer.Loop = CLogical("No") + theSoundBuffer.StopAfter = CLogical("Yes") + theSoundBuffer.StartOffset = "0" + theSoundBuffer.StopOffset = "0" + +End Sub + + + + + + +'-------------------------------------------------------------------------- +'InitTextDisplayDefaults +'-------------------------------------------------------------------------- +Sub InitTextDisplayDefaults(theTextDisplay As TextDisplay) + + If theTextDisplay Is Nothing Then Exit Sub + + theTextDisplay.X = "center" + theTextDisplay.Y = "center" + theTextDisplay.Width = "100%" + theTextDisplay.Height = "100%" + theTextDisplay.ForeColor = CColor("black") + theTextDisplay.BackColor = CColor("white") + theTextDisplay.BackStyle = "opaque" + theTextDisplay.BorderColor = CColor("black") + theTextDisplay.BorderWidth = CLng("0") + theTextDisplay.XAlign = "center" + theTextDisplay.YAlign = "center" + theTextDisplay.AlignHorizontal = "center" + theTextDisplay.AlignVertical = "center" + theTextDisplay.WordWrap = True + theTextDisplay.ClearAfter = CLogical("No") + theTextDisplay.FontName = "Courier New" + theTextDisplay.FontSize = "18" + theTextDisplay.FontBold = CLogical("Yes") + theTextDisplay.FontItalic = CLogical("No") + theTextDisplay.FontUnderline = CLogical("No") + theTextDisplay.FontStrikeout = CLogical("No") + +End Sub + + + + + + + +'-------------------------------------------------------------------------- +' Instance Declarations +'-------------------------------------------------------------------------- +Dim Display As DisplayDevice +Dim Sound As SoundDevice +Dim Keyboard As KeyboardDevice +Dim Mouse As MouseDevice +Dim SRBOX As SRBOXDevice +Dim SessionProc As Procedure + +Dim MainList As List + +Dim TestList As List + +Dim TrialProc As Procedure + +Dim StimDisp As Slide +Dim StimDispEchoClients As EchoClientCollection +Dim StimDisp_State As SlideState +Dim StimDisp_SlideText As SlideText +Dim StimDisp_SlideImage As SlideImage + +Dim StimISI As Slide +Dim StimISI_State As SlideState +Dim StimISI_SlideText As SlideText + +Dim InitProc As Procedure + +Dim CellList As List + +Dim StartProc As Procedure + +Dim EndProc As Procedure + +Dim StartText As TextDisplay +Dim StartTextEchoClients As EchoClientCollection + +Dim EndText As TextDisplay +Dim EndTextEchoClients As EchoClientCollection + +Dim StartSlide As Slide +Dim StartSlide_State As SlideState +Dim StartSlide_SlideText As SlideText + +Dim EndSlide As Slide +Dim EndSlide_State As SlideState +Dim EndSlide_SlideText As SlideText + + + +'-------------------------------------------------------------------------- +' Package Declare Script +'-------------------------------------------------------------------------- + + + +'-------------------------------------------------------------------------- +' User Script +'-------------------------------------------------------------------------- +Dim CellN as integer +Dim CatVal as integer +Dim CatStr as string +Dim IDVal as integer +Dim EmotVal as integer +Dim EmotStr as string +Dim InvVal as integer +Dim FqVal as integer +Dim SideStr as string + +Dim StimInMskMngr as InputMaskManager + +Dim NResp1 as integer + +Dim StimClear as boolean + +Dim StimImage As SlideImage + + +'-------------------------------------------------------------------------- +' Package Global Script +'-------------------------------------------------------------------------- + + + +'-------------------------------------------------------------------------- +' Package Routines +'-------------------------------------------------------------------------- + + + +'-------------------------------------------------------------------------- +' Implementation +'-------------------------------------------------------------------------- +Sub SessionProc_Run(c as Context) + MainList.Run c + + c.Log +End Sub + +Sub TrialProc_Run(c as Context) + + ''''''''''''''''''''''''''''''''''''''''''''' + ' InLine - TrnN170Init BEGIN + ''''''''''''''''''''''''''''''''''''''''''''' + +Set StimImage = CSlideImage(StimDisp.States(StimDisp.ActiveState).Objects("Image1")) +StimClear=False +NResp1 = 0 + +c.SetAttrib "ISI_Dur", random(500,750) + +CellN=c.GetAttrib("CellNumber") + +StimDisp.OnsetSignalData = CellN + +'define category variable +if CellN<213 then + CatStr="F" +end if +if CellN>212 and CellN<215 then + CatStr="H" +end if +if CellN>214 then + CatStr="C" +end if + + +'Define ID variable. +IDVal=random(1,5) +if CellN>214then + IDVal=1 +end if + +'Define emotion variable. +EmotVal=random(1,2) +if EmotVal = 1 then + EmotStr = "F" +else + EmotStr = "A" +end if +if CellN>212 then + EmotStr="" +end if + +'Define inversion variable +InvVal=1 +if CellN=212 or CellN=214 then + InvVal=2 +end if + +FqVal=0 + +SideStr="" +if CellN=215 then + SideStr="_L" +end if +if CellN=216 then + SideStr="_R" +end if + +c.SetAttrib "TrnN170FName1", "Stim\\" & CatStr & CStr(IDVal) & EmotStr & "O" & CStr(InvVal) & "_F" & CStr(FqVal) & SideStr & ".bmp" + + + + ''''''''''''''''''''''''''''''''''''''''''''' + ' InLine - TrnN170Init END + ''''''''''''''''''''''''''''''''''''''''''''' + + + + Select Case StimDisp.ActiveState + Case "Default" + + + Set StimDisp_SlideText = CSlideText(StimDisp.States.Item("Default").Objects(1)) + Set StimDisp_SlideText = Nothing + + Set StimDisp_SlideImage = CSlideImage(StimDisp.States.Item("Default").Objects(2)) + StimDisp_SlideImage.Filename = c.GetAttrib("TrnN170FName1") + StimDisp_SlideImage.Load + Set StimDisp_SlideImage = Nothing + End Select + + + StimDisp.InputMasks.Reset + + If SRBOX.GetState() = ebStateOpen Then + StimDispEchoClients.RemoveAll + StimDisp.InputMasks.Add SRBOX.CreateInputMask("{ANY}", "", CLng("-1"), CLng("1"), ebEndResponseActionNone, CLogical("Yes"), "", "", "") + + End If + + StimDisp.Run + + + ''''''''''''''''''''''''''''''''''''''''''''' + ' InLine - TrialAdj BEGIN + ''''''''''''''''''''''''''''''''''''''''''''' + Sleep(10) +WritePort &H378, 0 + +While Clock.Read < StimDisp.OnsetTime + 250 + c.GetAttrib("ISI_Dur") + + If StimDisp.InputMasks.Item(1).Responses.Count > NResp1 Then + NResp1 = StimDisp.InputMasks.Item(1).Responses.Count + WritePort &H378, CStr("20" & StimDisp.InputMasks.Item(1).Responses(NResp1).RESP) + Sleep(2) + WritePort &H378, 0 + End if + + If Clock.Read > StimDisp.OnsetTime + 250 and StimClear = False Then + WritePort &H378, 0 + StimImage.Filename="Stim//Black.bmp" + StimImage.Load + StimDisp.Draw + StimClear = True + End if + +WEnd + + + ''''''''''''''''''''''''''''''''''''''''''''' + ' InLine - TrialAdj END + ''''''''''''''''''''''''''''''''''''''''''''' + + + c.Log +End Sub + + + + + +Sub InitProc_Run(c as Context) + + ''''''''''''''''''''''''''''''''''''''''''''' + ' InLine - Init BEGIN + ''''''''''''''''''''''''''''''''''''''''''''' + StimDisp.OnsetSignalEnabled = True +StimDisp.OnsetSignalPort = &H378 +'StimDisp.OffsetSignalEnabled = True +'StimDisp.OffsetSignalPort = &H378 +'StimDisp.OffsetSignalData = 0 + +Set StimInMskMngr = StimDisp.InputMasks + +NResp1=0 + +StimClear=False + ''''''''''''''''''''''''''''''''''''''''''''' + ' InLine - Init END + ''''''''''''''''''''''''''''''''''''''''''''' + + + c.Log +End Sub + +Sub StartProc_Run(c as Context) + + + StartText.InputMasks.Reset + + If Keyboard.GetState() = ebStateOpen Then + StartTextEchoClients.RemoveAll + StartText.InputMasks.Add Keyboard.CreateInputMask("{ANY}", "", CLng(StartText.Duration), CLng("1"), ebEndResponseActionTerminate, CLogical("Yes"), "", "", "ResponseMode:All ProcessBackspace:Yes") + + End If + + StartText.Run + + ''''''''''''''''''''''''''''''''''''''''''''' + ' InLine - StartRec BEGIN + ''''''''''''''''''''''''''''''''''''''''''''' + 'WritePort &H378, 254 + ''''''''''''''''''''''''''''''''''''''''''''' + ' InLine - StartRec END + ''''''''''''''''''''''''''''''''''''''''''''' + + + + Select Case StartSlide.ActiveState + Case "Default" + + + Set StartSlide_SlideText = CSlideText(StartSlide.States.Item("Default").Objects(1)) + Set StartSlide_SlideText = Nothing + End Select + + StartSlide.Run + + + c.Log +End Sub + +Sub EndProc_Run(c as Context) + + + Select Case EndSlide.ActiveState + Case "Default" + + + Set EndSlide_SlideText = CSlideText(EndSlide.States.Item("Default").Objects(1)) + Set EndSlide_SlideText = Nothing + End Select + + EndSlide.Run + + + ''''''''''''''''''''''''''''''''''''''''''''' + ' InLine - StopRec BEGIN + ''''''''''''''''''''''''''''''''''''''''''''' + 'WritePort &H378, 255 + ''''''''''''''''''''''''''''''''''''''''''''' + ' InLine - StopRec END + ''''''''''''''''''''''''''''''''''''''''''''' + + + + EndText.InputMasks.Reset + + If Keyboard.GetState() = ebStateOpen Then + EndTextEchoClients.RemoveAll + EndText.InputMasks.Add Keyboard.CreateInputMask("{ANY}", "", CLng(EndText.Duration), CLng("1"), ebEndResponseActionTerminate, CLogical("Yes"), "", "", "ResponseMode:All ProcessBackspace:Yes") + + End If + + EndText.Run + + c.Log +End Sub + + + + + + + +'-------------------------------------------------------------------------- +' InitDevices +' +'-------------------------------------------------------------------------- +Sub InitDevices(c As Context) + + + + SetOSThreadPriority 3 + + + Set Display = New DisplayDevice + Display.Name = "Display" + + Dim DisplayDisplayDeviceInfo As DisplayDeviceInfo + DisplayDisplayDeviceInfo.XRes = 1024 + DisplayDisplayDeviceInfo.YRes = 768 + DisplayDisplayDeviceInfo.ColorDepth = 16 + DisplayDisplayDeviceInfo.RefreshRate = 0 + DisplayDisplayDeviceInfo.NumPages = 0 + + 'Load values from context if they exist + If c.AttribExists("Display.XRes") Then DisplayDisplayDeviceInfo.XRes = c.GetAttrib("Display.XRes") + If c.AttribExists("Display.YRes") Then DisplayDisplayDeviceInfo.YRes = c.GetAttrib("Display.YRes") + If c.AttribExists("Display.ColorDepth") Then DisplayDisplayDeviceInfo.ColorDepth = c.GetAttrib("Display.ColorDepth") + + 'Open the device, unless the context values indicate otherwise + Dim DisplayOpen As Boolean + DisplayOpen = True + If c.AttribExists("Display.Open") Then DisplayOpen = CLogical(c.GetAttrib("Display.Open")) + If DisplayOpen = True Then + Display.Open DisplayDisplayDeviceInfo + c.SetAttrib "Display.RefreshRate", Format$(Display.CalculatedRefreshRate, "0.000") + End If + + Set Sound = New SoundDevice + Sound.Name = "Sound" + + Dim SoundSoundOutDeviceInfo As SoundDeviceInfo + SoundSoundOutDeviceInfo.Channels = 2 + SoundSoundOutDeviceInfo.SamplesPerSecond = 22050 + SoundSoundOutDeviceInfo.BitsPerSample = 16 + 'Load values from context if they exist + If c.AttribExists("Sound.Channels") Then SoundSoundOutDeviceInfo.Channels = c.GetAttrib("Sound.Channels") + If c.AttribExists("Sound.SamplesPerSecond") Then SoundSoundOutDeviceInfo.SamplesPerSecond = c.GetAttrib("Sound.SamplesPerSecond") + If c.AttribExists("Sound.BitsPerSample") Then SoundSoundOutDeviceInfo.BitsPerSample = c.GetAttrib("Sound.BitsPerSample") + + Set Keyboard = New KeyboardDevice + Keyboard.Name = "Keyboard" + + Dim KeyboardKeyboardDeviceInfo as KeyboardDeviceInfo + KeyboardKeyboardDeviceInfo.CollectionMode = ebPressesOnly + KeyboardKeyboardDeviceInfo.CapsLock = ebCapsLockOff + KeyboardKeyboardDeviceInfo.NumLock = ebNumLockOn + 'Load values from context if they exist + If c.AttribExists("Keyboard.CollectionMode") Then KeyboardKeyboardDeviceInfo.CollectionMode = c.GetAttrib("Keyboard.CollectionMode") + If c.AttribExists("Keyboard.CapsLock") Then KeyboardKeyboardDeviceInfo.CapsLock = c.GetAttrib("Keyboard.CapsLock") + If c.AttribExists("Keyboard.NumLock") Then KeyboardKeyboardDeviceInfo.NumLock = c.GetAttrib("Keyboard.NumLock") + If c.AttribExists("Keyboard.EmulateDeviceName") Then KeyboardKeyboardDeviceInfo.EmulateDeviceName = c.GetAttrib("Keyboard.EmulateDeviceName") + + 'Open the device, unless the context values indicate otherwise + Dim KeyboardOpen As Boolean + KeyboardOpen = True + If c.AttribExists("Keyboard.Open") Then KeyboardOpen = CLogical(c.GetAttrib("Keyboard.Open")) + If KeyboardOpen = True Then + Keyboard.Open KeyboardKeyboardDeviceInfo + End If + + Set Mouse = New MouseDevice + Mouse.Name = "Mouse" + + Dim MouseMouseDeviceInfo as MouseDeviceInfo + MouseMouseDeviceInfo.OpenMode = ebMouseOpenModeDirect + MouseMouseDeviceInfo.CollectionMode = ebPressesOnly + MouseMouseDeviceInfo.ShowCursor = False + 'Load values from context if they exist + If c.AttribExists("Mouse.OpenMode") Then MouseMouseDeviceInfo.OpenMode = c.GetAttrib("Mouse.OpenMode") + If c.AttribExists("Mouse.CollectionMode") Then MouseMouseDeviceInfo.CollectionMode = c.GetAttrib("Mouse.CollectionMode") + If c.AttribExists("Mouse.ShowCursor") Then MouseMouseDeviceInfo.ShowCursor = c.GetAttrib("Mouse.ShowCursor") + If c.AttribExists("Mouse.EmulateDeviceName") Then MouseMouseDeviceInfo.EmulateDeviceName = c.GetAttrib("Mouse.EmulateDeviceName") + + 'Open the device, unless the context values indicate otherwise + Dim MouseOpen As Boolean + MouseOpen = True + If c.AttribExists("Mouse.Open") Then MouseOpen = CLogical(c.GetAttrib("Mouse.Open")) + If MouseOpen = True Then + Mouse.Open MouseMouseDeviceInfo + End If + + Set SRBOX = New SRBoxDevice + SRBOX.Name = "SRBOX" + + Dim SRBOXSRBoxDeviceInfo As SRBoxDeviceInfo + SRBOXSRBoxDeviceInfo.Model = 200 + SRBOXSRBoxDeviceInfo.Revision = 1 + SRBOXSRBoxDeviceInfo.CommPort = 1 + SRBOXSRBoxDeviceInfo.BaudRate = 19200 + SRBOXSRBoxDeviceInfo.CharacterRate = 800 + SRBOXSRBoxDeviceInfo.Configuration = ebSRBoxConfigStandard + SRBOXSRBoxDeviceInfo.CollectionMode = ebPressesOnly + 'Load values from context if they exist + If c.AttribExists("SRBOX.CommPort") Then SRBOXSRBoxDeviceInfo.CommPort = c.GetAttrib("SRBOX.CommPort") + If c.AttribExists("SRBOX.BaudRate") Then SRBOXSRBoxDeviceInfo.BaudRate = c.GetAttrib("SRBOX.BaudRate") + If c.AttribExists("SRBOX.CharacterRate") Then SRBOXSRBoxDeviceInfo.CharacterRate = c.GetAttrib("SRBOX.CharacterRate") + If c.AttribExists("SRBOX.Configuration") Then SRBOXSRBoxDeviceInfo.Configuration = c.GetAttrib("SRBOX.Configuration") + If c.AttribExists("SRBOX.CollectionMode") Then SRBOXSRBoxDeviceInfo.CollectionMode = c.GetAttrib("SRBOX.CollectionMode") + If c.AttribExists("SRBOX.EmulateDeviceName") Then SRBOXSRBoxDeviceInfo.EmulateDeviceName = c.GetAttrib("SRBOX.EmulateDeviceName") + + 'Open the device, unless the context values indicate otherwise + Dim SRBOXOpen As Boolean + SRBOXOpen = True + If c.AttribExists("SRBOX.Open") Then SRBOXOpen = CLogical(c.GetAttrib("SRBOX.Open")) + If SRBOXOpen = True Then + SRBOX.Open SRBOXSRBoxDeviceInfo + End If + + + SetOSThreadPriority 0 + +End Sub + + + +'-------------------------------------------------------------------------- +' InitObjects +' +'-------------------------------------------------------------------------- +Sub InitObjects(c As Context) + + Set SessionProc = New Procedure + SessionProc.Name = "SessionProc" + SessionProc.Tag = "" + SessionProc.Subroutine = "SessionProc_Run" + + Set MainList = New List + MainList.Name = "MainList" + MainList.Tag = "" + + 'Initialization for MainList + + Set MainList.Order = New SequentialOrder + Set MainList.Deletion = NoDeletion + MainList.ResetEveryRun = False + + ' Create the column headings + MainList.Filename = "" + MainList.LoadMethod = ebLoadMethodEmbedded + + MainList.Load + + Set MainList.TerminateCondition = Cycles(1) + Set MainList.ResetCondition = Samples(13) + MainList.Reset + + Set TestList = New List + TestList.Name = "TestList" + TestList.Tag = "" + + 'Initialization for TestList + + Set TestList.Order = New RandomOrder + Set TestList.Deletion = NoDeletion + TestList.ResetEveryRun = False + + ' Create the column headings + TestList.AddAttrib "CellNumber" + TestList.Filename = "" + TestList.LoadMethod = ebLoadMethodEmbedded + + TestList.Load + + Set TestList.TerminateCondition = Cycles(1) + Set TestList.ResetCondition = Samples(6) + TestList.Reset + + Set TrialProc = New Procedure + TrialProc.Name = "TrialProc" + TrialProc.Tag = "" + TrialProc.Subroutine = "TrialProc_Run" + + Set StimDisp = New Slide + StimDisp.Name = "StimDisp" + StimDisp.Tag = "at02" + + Set StimDispEchoClients = New EchoClientCollection + StimDisp.Duration = CLng("0") + StimDisp.TimingMode = ebTimingModeEvent + StimDisp.PreRelease = Val("40") + + StimDisp.OnsetSync = 1 + StimDisp.OffsetSync = 0 + + Set StimDisp_State = New SlideState + StimDisp_State.Name = "Default" + + InitSlideStateDefaults StimDisp_State + + StimDisp_State.BackColor = CColor("black") + + StimDisp.States.Add StimDisp_State, "Default" + + + Set StimDisp_SlideText = New SlideText + StimDisp_SlideText.Name = "Text1" + + InitSlideTextDefaults StimDisp_SlideText + + StimDisp_SlideText.Text = "+" + StimDisp_SlideText.Width = "10%" + StimDisp_SlideText.Height = "10%" + StimDisp_SlideText.ForeColor = CColor("silver") + StimDisp_SlideText.BackStyle = "transparent" + StimDisp.States.Item("Default").Objects.Add StimDisp_SlideText, "Text1" + + Set StimDisp_SlideImage = New SlideImage + StimDisp_SlideImage.Name = "Image1" + + InitSlideImageDefaults StimDisp_SlideImage + + StimDisp_SlideImage.Stretch = CLogical("Yes") + StimDisp_SlideImage.Width = "204" + StimDisp_SlideImage.Height = "256" + StimDisp.States.Item("Default").Objects.Add StimDisp_SlideImage, "Image1" + StimDisp.ActiveState = "Default" + + Set StimISI = New Slide + StimISI.Name = "StimISI" + StimISI.Tag = "st03" + StimISI.TimingMode = ebTimingModeEvent + StimISI.PreRelease = Val("40") + + StimISI.OnsetSync = 1 + StimISI.OffsetSync = 0 + + Set StimISI_State = New SlideState + StimISI_State.Name = "Default" + + InitSlideStateDefaults StimISI_State + + StimISI_State.BackColor = CColor("black") + + StimISI.States.Add StimISI_State, "Default" + + + Set StimISI_SlideText = New SlideText + StimISI_SlideText.Name = "Text1" + + InitSlideTextDefaults StimISI_SlideText + + StimISI_SlideText.Text = "+" + StimISI_SlideText.Width = "10%" + StimISI_SlideText.Height = "10%" + StimISI_SlideText.ForeColor = CColor("silver") + StimISI_SlideText.BackStyle = "transparent" + StimISI.States.Item("Default").Objects.Add StimISI_SlideText, "Text1" + StimISI.ActiveState = "Default" + + Set InitProc = New Procedure + InitProc.Name = "InitProc" + InitProc.Tag = "" + InitProc.Subroutine = "InitProc_Run" + + Set CellList = New List + CellList.Name = "CellList" + CellList.Tag = "" + + 'Initialization for CellList + + Set CellList.Order = New SequentialOrder + Set CellList.Deletion = NoDeletion + CellList.ResetEveryRun = False + + ' Create the column headings + CellList.AddAttrib "CellNumber" + CellList.AddAttrib "CellLabel" + CellList.Filename = "" + CellList.LoadMethod = ebLoadMethodEmbedded + + CellList.Load + + Set CellList.TerminateCondition = Cycles(1) + Set CellList.ResetCondition = Samples(6) + CellList.Reset + + Set StartProc = New Procedure + StartProc.Name = "StartProc" + StartProc.Tag = "" + StartProc.Subroutine = "StartProc_Run" + + Set EndProc = New Procedure + EndProc.Name = "EndProc" + EndProc.Tag = "" + EndProc.Subroutine = "EndProc_Run" + + Set StartText = New TextDisplay + StartText.Name = "StartText" + StartText.Tag = "" + + Set StartTextEchoClients = New EchoClientCollection + + InitTextDisplayDefaults StartText + + StartText.Text = "The task will start shortly." + StartText.BackColor = CColor("black") + StartText.ForeColor = CColor("silver") + StartText.Duration = CLng("-1") + StartText.TimingMode = ebTimingModeEvent + StartText.PreRelease = Val("0") + + StartText.OnsetSync = 1 + StartText.OffsetSync = 0 + + Set EndText = New TextDisplay + EndText.Name = "EndText" + EndText.Tag = "" + + Set EndTextEchoClients = New EchoClientCollection + + InitTextDisplayDefaults EndText + + EndText.Text = "Block complete." + EndText.BackColor = CColor("black") + EndText.ForeColor = CColor("silver") + EndText.Duration = CLng("-1") + EndText.TimingMode = ebTimingModeEvent + EndText.PreRelease = Val("0") + + EndText.OnsetSync = 1 + EndText.OffsetSync = 0 + + Set StartSlide = New Slide + StartSlide.Name = "StartSlide" + StartSlide.Tag = "st01" + StartSlide.Duration = CLng("5000") + StartSlide.TimingMode = ebTimingModeEvent + StartSlide.PreRelease = Val("40") + + StartSlide.OnsetSync = 1 + StartSlide.OffsetSync = 0 + + Set StartSlide_State = New SlideState + StartSlide_State.Name = "Default" + + InitSlideStateDefaults StartSlide_State + + StartSlide_State.BackColor = CColor("black") + + StartSlide.States.Add StartSlide_State, "Default" + + + Set StartSlide_SlideText = New SlideText + StartSlide_SlideText.Name = "Text1" + + InitSlideTextDefaults StartSlide_SlideText + + StartSlide_SlideText.Text = "+" + StartSlide_SlideText.Width = "10%" + StartSlide_SlideText.Height = "10%" + StartSlide_SlideText.ForeColor = CColor("silver") + StartSlide_SlideText.BackColor = CColor("silver") + StartSlide_SlideText.BackStyle = "transparent" + StartSlide.States.Item("Default").Objects.Add StartSlide_SlideText, "Text1" + StartSlide.ActiveState = "Default" + + Set EndSlide = New Slide + EndSlide.Name = "EndSlide" + EndSlide.Tag = "st01" + EndSlide.Duration = CLng("5000") + EndSlide.TimingMode = ebTimingModeEvent + EndSlide.PreRelease = Val("40") + + EndSlide.OnsetSync = 1 + EndSlide.OffsetSync = 0 + + Set EndSlide_State = New SlideState + EndSlide_State.Name = "Default" + + InitSlideStateDefaults EndSlide_State + + EndSlide_State.BackColor = CColor("black") + + EndSlide.States.Add EndSlide_State, "Default" + + + Set EndSlide_SlideText = New SlideText + EndSlide_SlideText.Name = "Text1" + + InitSlideTextDefaults EndSlide_SlideText + + EndSlide_SlideText.Text = "+" + EndSlide_SlideText.Width = "10%" + EndSlide_SlideText.Height = "10%" + EndSlide_SlideText.ForeColor = CColor("silver") + EndSlide_SlideText.BackColor = CColor("silver") + EndSlide_SlideText.BackStyle = "transparent" + EndSlide.States.Item("Default").Objects.Add EndSlide_SlideText, "Text1" + EndSlide.ActiveState = "Default" + + +End Sub + + +'-------------------------------------------------------------------------- +' InitPackages +' +'-------------------------------------------------------------------------- +Sub InitPackages(c As Context) + +End Sub + + + +'-------------------------------------------------------------------------- +'-------------------------------------------------------------------------- +' UnInitDevices +'-------------------------------------------------------------------------- +Sub UnInitDevices() + Display.Close + Set Display = Nothing + + Keyboard.Close + Set Keyboard = Nothing + + Mouse.Close + Set Mouse = Nothing + + + SRBOX.Close + Set SRBOX = Nothing + +End Sub + + +'-------------------------------------------------------------------------- +'-------------------------------------------------------------------------- +' UnInitPackages +'-------------------------------------------------------------------------- +Sub UnInitPackages() +End Sub + + +'-------------------------------------------------------------------------- +' UnInitObjects +' +'-------------------------------------------------------------------------- +Sub UnInitObjects() + + Set SessionProc = Nothing + + Set MainList = Nothing + + Set TestList = Nothing + + Set TrialProc = Nothing + + Set StimDisp = Nothing + + Set StimDispEchoClients = Nothing + + Set StimISI = Nothing + + Set InitProc = Nothing + + Set CellList = Nothing + + Set StartProc = Nothing + + Set EndProc = Nothing + + Set StartText = Nothing + + Set StartTextEchoClients = Nothing + + Set EndText = Nothing + + Set EndTextEchoClients = Nothing + + Set StartSlide = Nothing + + Set EndSlide = Nothing + + +End Sub + + +'-------------------------------------------------------------------------- +' Main +' +'-------------------------------------------------------------------------- +Sub Main() + + CreateDefaultPort + + ' Create and initialize the default context, data file, + ' and provide global access to the context. + Dim c As Context + Set c = New Context + Set c.DataFile = New DataFile + c.PushNewFrame + Set ebContext = c + + ' Set the log level names + c.SetLogLevelName 1, "Session" + c.SetLogLevelName 2, "Block" + c.SetLogLevelName 3, "Trial" + c.SetLogLevelName 4, "SubTrial" + c.SetLogLevelName 5, "LogLevel5" + c.SetLogLevelName 6, "LogLevel6" + c.SetLogLevelName 7, "LogLevel7" + c.SetLogLevelName 8, "LogLevel8" + c.SetLogLevelName 9, "LogLevel9" + c.SetLogLevelName 10, "LogLevel10" + + ' Set standard logging items + ebContext.SetAttrib "Experiment", "ICTrn_FHC_Onset_LR_300Blk" + ebContext.SetAttrib "SessionDate", Date$ + ebContext.SetAttrib "SessionTime", Time$ + ebContext.SetAttrib "RandomSeed", PRNG.GetSeed() + + If Basic.OS = ebWin32 Then + WinActivate "E-Run Experiment Window" + End If + + ' Get the StartupInfo + ' Set default for GroupNumber + c.SetAttrib "Group", "1" + + + ' Set the defaults for all of the StartupInfo + c.SetAttrib "Subject", "1" + c.SetAttrib "Session", "1" + c.SetAttrib "Group", "1" + c.SetAttrib "Name", "" + c.SetAttrib "Age", "0" + c.SetAttrib "Sex", "male" + c.SetAttrib "Handedness", "left" + c.SetAttrib "ResearcherID", "1" + + Dim vAnswer As Variant +StartupInfo_Begin: + +StartupInfoPrompt_Subject: + vAnswer = AskBox("Please enter the Subject Number (1-32767, 0=No Data Logging):", c.GetAttrib("Subject")) + If Not IsEmpty(vAnswer) then + If Not IsNumeric(vAnswer) then + MsgBox "Please enter an integer value" + GoTo StartupInfoPrompt_Subject + ElseIf CLng(vAnswer) < 0 Then + MsgBox "The value for Subject must not be less than 0" + GoTo StartupInfoPrompt_Subject + ElseIf CLng(vAnswer) > 32767 Then + MsgBox "The value for Subject must be not be greater than 32767" + GoTo StartupInfoPrompt_Subject + End If + Else + GoTo ExperimentAbort + End if + + c.SetAttrib "Subject", CStr(vAnswer) + +StartupInfoPrompt_Session: + vAnswer = AskBox("Please enter the Session Number (1-32767):", c.GetAttrib("Session")) + If Not IsEmpty(vAnswer) then + If Not IsNumeric(vAnswer) then + MsgBox "Please enter an integer value" + GoTo StartupInfoPrompt_Session + ElseIf CLng(vAnswer) < 1 Then + MsgBox "The value for Session must not be less than 1" + GoTo StartupInfoPrompt_Session + ElseIf CLng(vAnswer) > 32767 Then + MsgBox "The value for Session must be not be greater than 32767" + GoTo StartupInfoPrompt_Session + End If + Else + GoTo ExperimentAbort + End if + + c.SetAttrib "Session", CStr(vAnswer) + +StartupInfoPrompt_Group: + vAnswer = AskBox("Please enter Subject's Group (0-32767):", c.GetAttrib("Group")) + If Not IsEmpty(vAnswer) then + If Not IsNumeric(vAnswer) then + MsgBox "Please enter an integer value" + GoTo StartupInfoPrompt_Group + ElseIf CLng(vAnswer) < 0 Then + MsgBox "The value for Group must not be less than 0" + GoTo StartupInfoPrompt_Group + ElseIf CLng(vAnswer) > 32767 Then + MsgBox "The value for Group must be not be greater than 32767" + GoTo StartupInfoPrompt_Group + End If + Else + GoTo ExperimentAbort + End if + + c.SetAttrib "Group", CStr(vAnswer) + +StartupInfoPrompt_Name: + vAnswer = AskBox("Please enter Subject's Name:", c.GetAttrib("Name")) + If Not IsEmpty(vAnswer) then + If Len(CStr(vAnswer)) > 255 Then + MsgBox "The length for Name must be not be greater than 255" + GoTo StartupInfoPrompt_Name + End If + Else + GoTo ExperimentAbort + End if + + c.SetAttrib "Name", CStr(vAnswer) + +StartupInfoPrompt_Age: + vAnswer = AskBox("Please enter Subject's Age (0-150):", c.GetAttrib("Age")) + If Not IsEmpty(vAnswer) then + If Not IsNumeric(vAnswer) then + MsgBox "Please enter an integer value" + GoTo StartupInfoPrompt_Age + ElseIf CLng(vAnswer) < 0 Then + MsgBox "The value for Age must not be less than 0" + GoTo StartupInfoPrompt_Age + ElseIf CLng(vAnswer) > 150 Then + MsgBox "The value for Age must be not be greater than 150" + GoTo StartupInfoPrompt_Age + End If + Else + GoTo ExperimentAbort + End if + + c.SetAttrib "Age", CStr(vAnswer) + +StartupInfoPrompt_Sex: +Dim arrayChoicesSex(2) + arrayChoicesSex(0) = "male" + arrayChoicesSex(1) = "female" + vAnswer = SelectBox(, "Please enter Subject's Sex:", arrayChoicesSex) + If vAnswer >= 0 then + c.SetAttrib "Sex", arrayChoicesSex(vAnswer) + Else + GoTo ExperimentAbort + End If + +StartupInfoPrompt_Handedness: +Dim arrayChoicesHandedness(2) + arrayChoicesHandedness(0) = "left" + arrayChoicesHandedness(1) = "right" + vAnswer = SelectBox(, "Enter Subject's Handedness:", arrayChoicesHandedness) + If vAnswer >= 0 then + c.SetAttrib "Handedness", arrayChoicesHandedness(vAnswer) + Else + GoTo ExperimentAbort + End If + +StartupInfoPrompt_ResearcherID: + vAnswer = AskBox("Please enter Researcher's ID:", c.GetAttrib("ResearcherID")) + If Not IsEmpty(vAnswer) then + If Not IsNumeric(vAnswer) then + MsgBox "Please enter an integer value" + GoTo StartupInfoPrompt_ResearcherID + ElseIf CLng(vAnswer) < 0 Then + MsgBox "The value for ResearcherID must not be less than 0" + GoTo StartupInfoPrompt_ResearcherID + ElseIf CLng(vAnswer) > 32767 Then + MsgBox "The value for ResearcherID must be not be greater than 32767" + GoTo StartupInfoPrompt_ResearcherID + End If + Else + GoTo ExperimentAbort + End if + + c.SetAttrib "ResearcherID", CStr(vAnswer) + + ' Display the summary + Dim strSummary As String + strSummary = "Subject: " & c.GetAttrib("Subject") & "\n" + strSummary = strSummary & "Session: " & c.GetAttrib("Session") & "\n" + strSummary = strSummary & "Group: " & c.GetAttrib("Group") & "\n" + strSummary = strSummary & "Name: " & c.GetAttrib("Name") & "\n" + strSummary = strSummary & "Age: " & c.GetAttrib("Age") & "\n" + strSummary = strSummary & "Sex: " & c.GetAttrib("Sex") & "\n" + strSummary = strSummary & "Handedness: " & c.GetAttrib("Handedness") & "\n" + strSummary = strSummary & "ResearcherID: " & c.GetAttrib("ResearcherID") & "\n" + strSummary = strSummary & "\nContinue with the above startup info?" + + Dim nSummaryAnswer As Integer + nSummaryAnswer = MsgBox(strSummary, ebYesNoCancel + ebQuestion, "Summary of Startup Info") + If nSummaryAnswer = ebNo Then + GoTo StartupInfo_Begin + ElseIf nSummaryAnswer = ebCancel Then + GoTo ExperimentAbort + End If + + + ' Set the default Data Filename + c.DataFile.Filename = CStr(c.GetAttrib("Experiment")) & "-" & CStr(c.GetAttrib("Subject")) & "-" & CStr(c.GetAttrib("Session")) & ".txt" + + ' If we are logging data, then prompt to overwrite the data file if it exists + If CLng(c.GetAttrib("Subject")) <> 0 Then + Dim strFilenameEDAT As String + strFilenameEDAT = CStr(c.GetAttrib("Experiment")) & "-" & CStr(c.GetAttrib("Subject")) & "-" & CStr(c.GetAttrib("Session")) & ".edat" + If FileExists(c.DataFile.Filename) Or FileExists(strFilenameEDAT) Then + If ebYes <> MsgBox("WARNING: The data file and/or recovery file already exists:\nFILE: " & c.DataFile.Filename & "\n\nDo you want to overwrite?", ebYesNo + ebQuestion) Then + GoTo ExperimentAbort + End If + End If + End If + + ' Initialize all system devices, packages, and objects + InitDevices c + InitPackages c + InitObjects c + + ' If we are logging data, then open the datafile + If CLng(c.GetAttrib("Subject")) <> 0 Then + c.DataFile.Open + c.LogHeader + End If + + + +#If RUNTIME_VERSION_MAJOR > 1 Or (RUNTIME_VERSION_MAJOR = 1 And RUNTIME_VERSION_MINOR >= 2) Then + ' Log clock timing information + c.SetAttrib "Clock.Information", Clock.Information +#End If + + ' Start the running of the Experiment + SessionProc.Run c + + ' Clean up the context and close the datafile + If CLng(c.GetAttrib("Subject")) <> 0 Then + c.DataFile.Close + ' Attempt to convert the recovery file into a data file + Dim nConvert As Long + nConvert = c.DataFile.Convert(ebProgressSimple) + If nConvert = 0 Then + ' Settings in E-Studio are set to not remove E-Recovery file + Else + ' The datafile failed to convert! + MsgBox "ERROR: The datafile did not convert!\nFILE: " & c.DataFile.Filename & "\n\nIt is recommended that you recover your data with the E-Recovery utility" + MsgBox c.DataFile.GetLastErrorMessage() + End If + End If +ExperimentFinish: + + UnInitObjects + + UnInitPackages + UnInitDevices + +ExperimentAbort: + + ' Clean up the context + c.PopFrame + Set c = Nothing + Set ebContext = Nothing + + DestroyDefaultPort + +End Sub + + +[DataSection_MainList(1)] +Weight Nested Procedure +1 InitProc +1 StartProc +10 TestList TrialProc +1 EndProc\0 + +[DataSection_TestList(1)] +Weight Nested Procedure CellNumber +1 211 +1 212 +1 213 +1 214 +1 215 +1 216\0 + +[DataSection_CellList(1)] +Weight Nested Procedure CellNumber CellLabel +1 1 Face_up +1 2 Face_down +1 3 House_up +1 4 House_up +1 5 Check_L +1 6 Check_R\0 \ No newline at end of file diff --git a/eeg_face13/sourcedata/tasks/ICTrn_FHC_Onset_LR_300Blk.es b/eeg_face13/sourcedata/tasks/ICTrn_FHC_Onset_LR_300Blk.es new file mode 100644 index 000000000..928f42ea2 --- /dev/null +++ b/eeg_face13/sourcedata/tasks/ICTrn_FHC_Onset_LR_300Blk.es @@ -0,0 +1,1087 @@ +[WARNING] +WARNING: DO NOT EDIT THIS FILE DIRECTLY! +Use only E-Studio to edit this file. +Editing of this file from any other means is not supported +and may corrupt the experiment design specification. +Technical support will not be able to address any issue in +regards to this file format. + +[Experiment] +VersionMajor=1 +VersionMinor=2 +VersionInternal=1 +VersionBuild=33 +_VersionPersist=6 +Root="SessionProc" +Author=" (Brock University)" +Abstract="" +Notes="" +Creation=1251730965 + +[Device0] +_VersionPersist=1 +OpenDevice=1 +Width=1024 +Height=768 +Bpp=16 +Name="Display" +Class="Display" + +[Device1] +_VersionPersist=1 +OpenDevice=0 +Channels=2 +Samples=22050 +Bps=16 +Name="Sound" +Class="Sound" + +[Device2] +_VersionPersist=1 +OpenDevice=1 +CollectionMode=1 +EmulateDeviceName="" +CapsLock=0 +NumLock=1 +Name="Keyboard" +Class="Keyboard" + +[Device3] +_VersionPersist=1 +OpenDevice=1 +CollectionMode=1 +EmulateDeviceName="" +OpenMode=0 +ShowCursor=0 +Name="Mouse" +Class="Mouse" + +[Device4] +_VersionPersist=1 +OpenDevice=1 +CollectionMode=1 +EmulateDeviceName="" +Port=1 +BaudRate=19200 +Cps=800 +Configuration=0 +Name="SRBOX" +Class="SRBOX" + +[DataFile] +WarnBeforeOverwrite=1 +WarnIfConvertFailed=1 +RemoveERecoveryFile=0 +ReceiveFeedbackDuringDataFileConversion=1 +LogLevel(2).Name="Block" +LogLevel(3).Name="Trial" +LogLevel(4).Name="SubTrial" +LogLevel(5).Name="LogLevel5" +LogLevel(6).Name="LogLevel6" +LogLevel(7).Name="LogLevel7" +LogLevel(8).Name="LogLevel8" +LogLevel(9).Name="LogLevel9" +LogLevel(10).Name="LogLevel10" + +[StartupInfo] +DisplaySummary=1 +UseDefaults=0 + +[StartupInfo1] +Name="Subject" +Prompt="Please enter the Subject Number (1-32767, 0=No Data Logging):" +DataType=0 +Enabled=1 +PromptEnabled=1 +Default="1" +Min=0 +Max=32767 +PrivateFlags=268435477 +PrivateInfo=1 + +[StartupInfo2] +Name="Session" +Prompt="Please enter the Session Number (1-32767):" +DataType=0 +Enabled=1 +PromptEnabled=1 +Default="1" +Min=1 +Max=32767 +PrivateFlags=268435477 +PrivateInfo=1 + +[StartupInfo3] +Name="Group" +Prompt="Please enter Subject's Group (0-32767):" +DataType=0 +Enabled=1 +PromptEnabled=1 +Default="1" +Min=0 +Max=32767 + +[StartupInfo4] +Name="Name" +Prompt="Please enter Subject's Name:" +DataType=1 +Enabled=1 +PromptEnabled=1 +Default="" +MaxLength=255 + +[StartupInfo5] +Name="Age" +Prompt="Please enter Subject's Age (0-150):" +DataType=0 +Enabled=1 +PromptEnabled=1 +Default="0" +Min=0 +Max=150 + +[StartupInfo6] +Name="Sex" +Prompt="Please enter Subject's Sex:" +DataType=2 +Enabled=1 +PromptEnabled=1 +Default="male" +Choice(1).Value="male" +Choice(2).Value="female" + +[StartupInfo7] +Name="Handedness" +Prompt="Enter Subject's Handedness:" +DataType=2 +Enabled=1 +PromptEnabled=1 +Default="left" +Choice(1).Value="left" +Choice(2).Value="right" + +[StartupInfo8] +Name="ResearcherID" +Prompt="Please enter Researcher's ID:" +DataType=0 +Enabled=1 +PromptEnabled=1 +Default="1" +Min=0 +Max=32767 + +[Object0] +_Version=65536 +_ExtentX=2646 +_ExtentY=1323 +_StockProps=0 +VersionMajor=1 +VersionMinor=2 +VersionInternal=1 +VersionBuild=21 +_ItemList="MainList" +_VersionPersist=1 +FlowLines(0).Count=1 +FlowLines(0).FlowItem(0).Name="MainList" +LogData=1 +Name="SessionProc" +TypeName="Procedure" +Tag="" +Notes="" + +[Object1] +_Version=65536 +_ExtentX=2646 +_ExtentY=1323 +_StockProps=0 +VersionMajor=1 +VersionMinor=2 +VersionInternal=1 +VersionBuild=21 +_ItemList="TestList;InitProc;StartProc;TrialProc;EndProc" +_VersionPersist=1 +VersionMajor=1 +VersionMinor=2 +VersionInternal=1 +VersionBuild=21 +Order=0 +OrderBy=0 +ResetEveryRun=0 +HideLevelsWithZeroWeight=0 +CycleDef=0 +CycleValueSamples=0 +CycleValueCondition="" +ExitDef=0 +ExitValueCycles=1 +ExitValueSamples=1 +ExitValueSeconds=0 +ExitValueCondition="" +LoadMethod=0 +Filename="" +UserAttributes=0 +Attributes("Weight").DefaultValue="1" +Attributes("Weight").Visible=1 +Attributes("Weight").Width=75 +Attributes("Weight").OrderIndex=0 +Attributes("Nested").DefaultValue="" +Attributes("Nested").Visible=1 +Attributes("Nested").Width=75 +Attributes("Nested").OrderIndex=1 +Attributes("Procedure").DefaultValue="trn_N170_Proc" +Attributes("Procedure").Visible=1 +Attributes("Procedure").Width=161 +Attributes("Procedure").OrderIndex=2 +Levels=4 +Levels(1).ValueString="1\t\tInitProc\t" +Levels(2).ValueString="1\t\tStartProc\t" +Levels(3).ValueString="10\tTestList\tTrialProc\t" +Levels(4).ValueString="1\t\tEndProc\t" +Name="MainList" +TypeName="List" +Tag="" +Notes="" + +[Object2] +_Version=65536 +_ExtentX=2646 +_ExtentY=1323 +_StockProps=0 +VersionMajor=1 +VersionMinor=2 +VersionInternal=1 +VersionBuild=21 +Logging("CellNumber").Log=-1 +_VersionPersist=1 +VersionMajor=1 +VersionMinor=2 +VersionInternal=1 +VersionBuild=21 +Order=1 +OrderBy=0 +ResetEveryRun=0 +HideLevelsWithZeroWeight=0 +CycleDef=0 +CycleValueSamples=0 +CycleValueCondition="" +ExitDef=0 +ExitValueCycles=1 +ExitValueSamples=1 +ExitValueSeconds=0 +ExitValueCondition="" +LoadMethod=0 +Filename="" +UserAttributes=1 +Attributes("Weight").DefaultValue="1" +Attributes("Weight").Visible=1 +Attributes("Weight").Width=75 +Attributes("Weight").OrderIndex=0 +Attributes("Nested").DefaultValue="" +Attributes("Nested").Visible=1 +Attributes("Nested").Width=75 +Attributes("Nested").OrderIndex=1 +Attributes("Procedure").DefaultValue="TrnN170TrialProc" +Attributes("Procedure").Visible=1 +Attributes("Procedure").Width=75 +Attributes("Procedure").OrderIndex=2 +Attributes(1).Name="CellNumber" +Attributes(1).DefaultValue="1" +Attributes(1).VarType=8 +Attributes(1).VarTypeInternal=8 +Attributes(1).Visible=1 +Attributes(1).Width=75 +Levels=6 +Levels(1).ValueString="1\t\t\t211\t" +Levels(2).ValueString="1\t\t\t212\t" +Levels(3).ValueString="1\t\t\t213\t" +Levels(4).ValueString="1\t\t\t214\t" +Levels(5).ValueString="1\t\t\t215\t" +Levels(6).ValueString="1\t\t\t216\t" +Name="TestList" +TypeName="List" +Tag="" +Notes="" + +[Object3] +_Version=65536 +_ExtentX=2646 +_ExtentY=1323 +_StockProps=0 +VersionMajor=1 +VersionMinor=2 +VersionInternal=1 +VersionBuild=21 +_ItemList="TrnN170Init;StimDisp;TrialAdj" +_VersionPersist=1 +FlowLines(0).Count=3 +FlowLines(0).FlowItem(0).Name="TrnN170Init" +FlowLines(0).FlowItem(1).Name="StimDisp" +FlowLines(0).FlowItem(2).Name="TrialAdj" +LogData=1 +Name="TrialProc" +TypeName="Procedure" +Tag="" +Notes="" + +[Object4] +_Version=131072 +_ExtentX=2646 +_ExtentY=1323 +_StockProps=0 +VersionMajor=1 +VersionMinor=2 +VersionInternal=1 +VersionBuild=21 +_VersionPersist=1 +Code="\nSet StimImage = CSlideImage(StimDisp.States(StimDisp.ActiveState).Objects(\"Image1\"))\nStimClear=False\nNResp1 = 0\n\nc.SetAttrib \"ISI_Dur\", random(500,750)\n\nCellN=c.GetAttrib(\"CellNumber\")\n\nStimDisp.OnsetSignalData = CellN\n\n'define category variable\nif CellN<213 then\n\tCatStr=\"F\"\nend if\nif CellN>212 and CellN<215 then\n\tCatStr=\"H\"\nend if\nif CellN>214 then\n\tCatStr=\"C\"\nend if\n\n\n'Define ID variable.\nIDVal=random(1,5)\nif CellN>214then\n\tIDVal=1\nend if\n\n'Define emotion variable.\nEmotVal=random(1,2)\nif EmotVal = 1 then\n\tEmotStr = \"F\"\nelse\n\tEmotStr = \"A\"\nend if\nif CellN>212 then\n\tEmotStr=\"\"\nend if\n\n'Define inversion variable\nInvVal=1\nif CellN=212 or CellN=214 then\n\tInvVal=2\nend if\n\nFqVal=0\n\nSideStr=\"\"\nif CellN=215 then\n\tSideStr=\"_L\"\nend if\nif CellN=216 then\n\tSideStr=\"_R\"\nend if\n\nc.SetAttrib \"TrnN170FName1\", \"Stim\\\\\" & CatStr & CStr(IDVal) & EmotStr & \"O\" & CStr(InvVal) & \"_F\" & CStr(FqVal) & SideStr & \".bmp\"\n\n\n" +Name="TrnN170Init" +TypeName="InLine" +Tag="" +Notes="" + +[Object5] +_Version=65536 +_ExtentX=2646 +_ExtentY=1323 +_StockProps=0 +VersionMajor=1 +VersionMinor=2 +VersionInternal=1 +VersionBuild=21 +Logging("OnsetDelay").Log=0 +Logging("OnsetTime").Log=0 +Logging("DurationError").Log=0 +Logging("PreRelease").Log=0 +Logging("Duration").Log=0 +Logging("StartTime").Log=0 +Logging("OffsetTime").Log=0 +Logging("FinishTime").Log=0 +Logging("TimingMode").Log=0 +Logging("CustomOnsetTime").Log=0 +Logging("CustomOffsetTime").Log=0 +Logging("ActionDelay").Log=0 +Logging("ActionTime").Log=0 +Logging("TargetOffsetTime").Log=0 +Logging("TargetOnsetTime").Log=0 +Logging("OffsetDelay").Log=0 +Logging("RTTime").Log=0 +Logging("ACC").Log=0 +Logging("RT").Log=0 +Logging("RESP").Log=0 +Logging("CRESP").Log=0 +Logging("Tag").Log=0 +Input(0)=!Data0 +_VersionPersist=2 +ActiveState="Default" +States.Count=1 +States(1).Name="Default" +States("Default").X="center" +States("Default").Y="center" +States("Default").Width="100%" +States("Default").Height="100%" +States("Default").XAlign="center" +States("Default").YAlign="center" +States("Default").BackColor="black" +States("Default").BackStyle="opaque" +States("Default").BorderColor="black" +States("Default").BorderWidth="0" +States("Default").ClearAfter="No" +States("Default").Enabled="Yes" +States("Default").ChildCount=2 +States("Default").Objects(1).Type="Image" +States("Default").Objects(1).Filename="[TrnN170FName1]" +States("Default").Objects(1).Stretch="Yes" +States("Default").Objects(1).MirrorLeftRight="No" +States("Default").Objects(1).MirrorUpDown="No" +States("Default").Objects(1).Width="204" +States("Default").Objects(1).Height="256" +States("Default").Objects(1).X="center" +States("Default").Objects(1).Y="center" +States("Default").Objects(1).AlignHorizontal="center" +States("Default").Objects(1).AlignVertical="center" +States("Default").Objects(1).ForeColor="black" +States("Default").Objects(1).BackColor="white" +States("Default").Objects(1).BackStyle="opaque" +States("Default").Objects(1).BorderWidth="0" +States("Default").Objects(1).BorderColor="black" +States("Default").Objects(1).XAlign="center" +States("Default").Objects(1).YAlign="center" +States("Default").Objects(1).SourceColorKey="black" +States("Default").Objects(1).UseSourceColorKey="No" +States("Default").Objects(1).Name="Image1" +States("Default").Objects(2).Type="Text" +States("Default").Objects(2).Text="+" +States("Default").Objects(2).Width="10%" +States("Default").Objects(2).Height="10%" +States("Default").Objects(2).X="center" +States("Default").Objects(2).Y="center" +States("Default").Objects(2).AlignHorizontal="center" +States("Default").Objects(2).AlignVertical="center" +States("Default").Objects(2).FontName="Courier New" +States("Default").Objects(2).FontSize="18" +States("Default").Objects(2).FontBold="Yes" +States("Default").Objects(2).FontUnderline="No" +States("Default").Objects(2).FontStrikeout="No" +States("Default").Objects(2).FontItalic="No" +States("Default").Objects(2).ForeColor="silver" +States("Default").Objects(2).BackColor="white" +States("Default").Objects(2).BackStyle="transparent" +States("Default").Objects(2).BorderWidth="0" +States("Default").Objects(2).BorderColor="black" +States("Default").Objects(2).XAlign="center" +States("Default").Objects(2).YAlign="center" +States("Default").Objects(2).WordWrap=1 +States("Default").Objects(2).Name="Text1" +Duration="0" +JumpLabel="" +TimingMode=0 +PreRelease="40" +OnsetSync=1 +OffsetSync=0 +Name="StimDisp" +TypeName="Slide" +Tag="at02" +Notes="" + +[Data0] +DeviceName="SRBOX" +DeviceClass="SRBOX" +AllowableInput="{ANY}" +CorrectInput="" +TimeLimit="-1" +MaxCount="1" +InputAction=0 +SyncOwnerDuration=0 +Enabled=1 +FlushInputBuffer="Yes" +TerminationInput="" +UserTag="" + +[Object6] +_Version=65536 +_ExtentX=2646 +_ExtentY=1323 +_StockProps=0 +VersionMajor=1 +VersionMinor=2 +VersionInternal=1 +VersionBuild=21 +Logging("OnsetDelay").Log=0 +Logging("OnsetTime").Log=0 +Logging("DurationError").Log=0 +Logging("PreRelease").Log=0 +Logging("Duration").Log=0 +Logging("StartTime").Log=0 +Logging("OffsetTime").Log=0 +Logging("FinishTime").Log=0 +Logging("TimingMode").Log=0 +Logging("CustomOnsetTime").Log=0 +Logging("CustomOffsetTime").Log=0 +Logging("ActionDelay").Log=0 +Logging("ActionTime").Log=0 +Logging("TargetOffsetTime").Log=0 +Logging("TargetOnsetTime").Log=0 +Logging("OffsetDelay").Log=0 +Logging("RTTime").Log=0 +Logging("ACC").Log=0 +Logging("RT").Log=0 +Logging("RESP").Log=0 +Logging("CRESP").Log=0 +Logging("Tag").Log=0 +_VersionPersist=2 +ActiveState="Default" +States.Count=1 +States(1).Name="Default" +States("Default").X="center" +States("Default").Y="center" +States("Default").Width="100%" +States("Default").Height="100%" +States("Default").XAlign="center" +States("Default").YAlign="center" +States("Default").BackColor="black" +States("Default").BackStyle="opaque" +States("Default").BorderColor="black" +States("Default").BorderWidth="0" +States("Default").ClearAfter="No" +States("Default").Enabled="Yes" +States("Default").ChildCount=1 +States("Default").Objects(1).Type="Text" +States("Default").Objects(1).Text="+" +States("Default").Objects(1).Width="10%" +States("Default").Objects(1).Height="10%" +States("Default").Objects(1).X="center" +States("Default").Objects(1).Y="center" +States("Default").Objects(1).AlignHorizontal="center" +States("Default").Objects(1).AlignVertical="center" +States("Default").Objects(1).FontName="Courier New" +States("Default").Objects(1).FontSize="18" +States("Default").Objects(1).FontBold="Yes" +States("Default").Objects(1).FontUnderline="No" +States("Default").Objects(1).FontStrikeout="No" +States("Default").Objects(1).FontItalic="No" +States("Default").Objects(1).ForeColor="silver" +States("Default").Objects(1).BackColor="white" +States("Default").Objects(1).BackStyle="transparent" +States("Default").Objects(1).BorderWidth="0" +States("Default").Objects(1).BorderColor="black" +States("Default").Objects(1).XAlign="center" +States("Default").Objects(1).YAlign="center" +States("Default").Objects(1).WordWrap=1 +States("Default").Objects(1).Name="Text1" +Duration="[ISI_Dur]" +JumpLabel="" +TimingMode=0 +PreRelease="40" +OnsetSync=1 +OffsetSync=0 +Name="StimISI" +TypeName="Slide" +Tag="st03" +Notes="" + +[Object7] +_Version=65536 +_ExtentX=2646 +_ExtentY=1323 +_StockProps=0 +VersionMajor=1 +VersionMinor=2 +VersionInternal=1 +VersionBuild=21 +_ItemList="Init" +_VersionPersist=1 +FlowLines(0).Count=1 +FlowLines(0).FlowItem(0).Name="Init" +LogData=1 +Name="InitProc" +TypeName="Procedure" +Tag="" +Notes="" + +[Object8] +_Version=131072 +_ExtentX=2646 +_ExtentY=1323 +_StockProps=0 +VersionMajor=1 +VersionMinor=2 +VersionInternal=1 +VersionBuild=21 +_VersionPersist=1 +Code="StimDisp.OnsetSignalEnabled = True\nStimDisp.OnsetSignalPort = &H378\n'StimDisp.OffsetSignalEnabled = True\n'StimDisp.OffsetSignalPort = &H378\n'StimDisp.OffsetSignalData = 0\n\nSet StimInMskMngr = StimDisp.InputMasks\n\nNResp1=0\n\nStimClear=False" +Name="Init" +TypeName="InLine" +Tag="" +Notes="" + +[Object9] +_Version=65536 +_ExtentX=2646 +_ExtentY=1323 +_StockProps=0 +VersionMajor=1 +VersionMinor=2 +VersionInternal=1 +VersionBuild=21 +Logging("CellNumber").Log=-1 +Logging("CellLabel").Log=-1 +_VersionPersist=1 +VersionMajor=1 +VersionMinor=2 +VersionInternal=1 +VersionBuild=21 +Order=0 +OrderBy=0 +ResetEveryRun=0 +HideLevelsWithZeroWeight=0 +CycleDef=0 +CycleValueSamples=0 +CycleValueCondition="" +ExitDef=0 +ExitValueCycles=1 +ExitValueSamples=1 +ExitValueSeconds=0 +ExitValueCondition="" +LoadMethod=0 +Filename="" +UserAttributes=2 +Attributes("Weight").DefaultValue="1" +Attributes("Weight").Visible=1 +Attributes("Weight").Width=75 +Attributes("Weight").OrderIndex=0 +Attributes("Nested").DefaultValue="" +Attributes("Nested").Visible=1 +Attributes("Nested").Width=75 +Attributes("Nested").OrderIndex=1 +Attributes("Procedure").DefaultValue="" +Attributes("Procedure").Visible=1 +Attributes("Procedure").Width=75 +Attributes("Procedure").OrderIndex=2 +Attributes(1).Name="CellNumber" +Attributes(1).DefaultValue="1" +Attributes(1).VarType=8 +Attributes(1).VarTypeInternal=8 +Attributes(1).Visible=1 +Attributes(1).Width=75 +Attributes(2).Name="CellLabel" +Attributes(2).DefaultValue="House_up" +Attributes(2).VarType=8 +Attributes(2).VarTypeInternal=8 +Attributes(2).Visible=1 +Attributes(2).Width=75 +Levels=6 +Levels(1).ValueString="1\t\t\t1\tFace_up\t" +Levels(2).ValueString="1\t\t\t2\tFace_down\t" +Levels(3).ValueString="1\t\t\t3\tHouse_up\t" +Levels(4).ValueString="1\t\t\t4\tHouse_up\t" +Levels(5).ValueString="1\t\t\t5\tCheck_L\t" +Levels(6).ValueString="1\t\t\t6\tCheck_R\t" +Name="CellList" +TypeName="List" +Tag="" +Notes="" + +[Object10] +_Version=65536 +_ExtentX=2646 +_ExtentY=1323 +_StockProps=0 +VersionMajor=1 +VersionMinor=2 +VersionInternal=1 +VersionBuild=21 +_ItemList="StartText;StartRec;StartSlide" +_VersionPersist=1 +FlowLines(0).Count=3 +FlowLines(0).FlowItem(0).Name="StartText" +FlowLines(0).FlowItem(1).Name="StartRec" +FlowLines(0).FlowItem(2).Name="StartSlide" +LogData=1 +Name="StartProc" +TypeName="Procedure" +Tag="" +Notes="" + +[Object11] +_Version=65536 +_ExtentX=2646 +_ExtentY=1323 +_StockProps=0 +VersionMajor=1 +VersionMinor=2 +VersionInternal=1 +VersionBuild=21 +_ItemList="EndSlide;StopRec;EndText" +_VersionPersist=1 +FlowLines(0).Count=3 +FlowLines(0).FlowItem(0).Name="EndSlide" +FlowLines(0).FlowItem(1).Name="StopRec" +FlowLines(0).FlowItem(2).Name="EndText" +LogData=1 +Name="EndProc" +TypeName="Procedure" +Tag="" +Notes="" + +[Object12] +_Version=65536 +_ExtentX=2646 +_ExtentY=1323 +_StockProps=0 +VersionMajor=1 +VersionMinor=2 +VersionInternal=1 +VersionBuild=21 +Logging("OnsetDelay").Log=0 +Logging("OnsetTime").Log=0 +Logging("DurationError").Log=0 +Logging("PreRelease").Log=0 +Logging("Duration").Log=0 +Logging("StartTime").Log=0 +Logging("OffsetTime").Log=0 +Logging("FinishTime").Log=0 +Logging("TimingMode").Log=0 +Logging("CustomOnsetTime").Log=0 +Logging("CustomOffsetTime").Log=0 +Logging("ActionDelay").Log=0 +Logging("ActionTime").Log=0 +Logging("TargetOffsetTime").Log=0 +Logging("TargetOnsetTime").Log=0 +Logging("OffsetDelay").Log=0 +Logging("RTTime").Log=0 +Logging("ACC").Log=0 +Logging("RT").Log=0 +Logging("RESP").Log=0 +Logging("CRESP").Log=0 +Logging("Tag").Log=0 +Input(0)=!Data1 +_VersionPersist=1 +VersionMajor=1 +VersionMinor=2 +VersionInternal=1 +VersionBuild=21 +Text="The task will start shortly." +ForeColor="silver" +BackColor="black" +BackStyle="opaque" +BorderColor="black" +BorderWidth="0" +X="center" +Y="center" +Width="100%" +Height="100%" +XAlign="center" +YAlign="center" +AlignHorizontal="center" +AlignVertical="center" +FontName="Courier New" +FontSize="18" +FontBold="Yes" +FontItalic="No" +FontUnderline="No" +FontStrikeout="No" +WordWrap=1 +ClearAfter="No" +Duration="-1" +JumpLabel="" +TimingMode=0 +PreRelease="0" +OnsetSync=1 +OffsetSync=0 +Name="StartText" +TypeName="TextDisplay" +Tag="" +Notes="" + +[Data1] +DeviceName="Keyboard" +DeviceClass="Keyboard" +AllowableInput="{ANY}" +CorrectInput="" +TimeLimit="" +MaxCount="1" +InputAction=1 +SyncOwnerDuration=1 +Enabled=1 +FlushInputBuffer="Yes" +TerminationInput="" +UserTag="" +ResponseMode="All" +ProcessBackspace="Yes" + +[Object13] +_Version=65536 +_ExtentX=2646 +_ExtentY=1323 +_StockProps=0 +VersionMajor=1 +VersionMinor=2 +VersionInternal=1 +VersionBuild=21 +Logging("OnsetDelay").Log=0 +Logging("OnsetTime").Log=0 +Logging("DurationError").Log=0 +Logging("PreRelease").Log=0 +Logging("Duration").Log=0 +Logging("StartTime").Log=0 +Logging("OffsetTime").Log=0 +Logging("FinishTime").Log=0 +Logging("TimingMode").Log=0 +Logging("CustomOnsetTime").Log=0 +Logging("CustomOffsetTime").Log=0 +Logging("ActionDelay").Log=0 +Logging("ActionTime").Log=0 +Logging("TargetOffsetTime").Log=0 +Logging("TargetOnsetTime").Log=0 +Logging("OffsetDelay").Log=0 +Logging("RTTime").Log=0 +Logging("ACC").Log=0 +Logging("RT").Log=0 +Logging("RESP").Log=0 +Logging("CRESP").Log=0 +Logging("Tag").Log=0 +Input(0)=!Data2 +_VersionPersist=1 +VersionMajor=1 +VersionMinor=2 +VersionInternal=1 +VersionBuild=21 +Text="Block complete." +ForeColor="silver" +BackColor="black" +BackStyle="opaque" +BorderColor="black" +BorderWidth="0" +X="center" +Y="center" +Width="100%" +Height="100%" +XAlign="center" +YAlign="center" +AlignHorizontal="center" +AlignVertical="center" +FontName="Courier New" +FontSize="18" +FontBold="Yes" +FontItalic="No" +FontUnderline="No" +FontStrikeout="No" +WordWrap=1 +ClearAfter="No" +Duration="-1" +JumpLabel="" +TimingMode=0 +PreRelease="0" +OnsetSync=1 +OffsetSync=0 +Name="EndText" +TypeName="TextDisplay" +Tag="" +Notes="" + +[Data2] +DeviceName="Keyboard" +DeviceClass="Keyboard" +AllowableInput="{ANY}" +CorrectInput="" +TimeLimit="" +MaxCount="1" +InputAction=1 +SyncOwnerDuration=1 +Enabled=1 +FlushInputBuffer="Yes" +TerminationInput="" +UserTag="" +ResponseMode="All" +ProcessBackspace="Yes" + +[Object14] +_Version=65536 +_ExtentX=2646 +_ExtentY=1323 +_StockProps=0 +VersionMajor=1 +VersionMinor=2 +VersionInternal=1 +VersionBuild=21 +Logging("OnsetDelay").Log=0 +Logging("OnsetTime").Log=0 +Logging("DurationError").Log=0 +Logging("PreRelease").Log=0 +Logging("Duration").Log=0 +Logging("StartTime").Log=0 +Logging("OffsetTime").Log=0 +Logging("FinishTime").Log=0 +Logging("TimingMode").Log=0 +Logging("CustomOnsetTime").Log=0 +Logging("CustomOffsetTime").Log=0 +Logging("ActionDelay").Log=0 +Logging("ActionTime").Log=0 +Logging("TargetOffsetTime").Log=0 +Logging("TargetOnsetTime").Log=0 +Logging("OffsetDelay").Log=0 +Logging("RTTime").Log=0 +Logging("ACC").Log=0 +Logging("RT").Log=0 +Logging("RESP").Log=0 +Logging("CRESP").Log=0 +Logging("Tag").Log=0 +_VersionPersist=2 +ActiveState="Default" +States.Count=1 +States(1).Name="Default" +States("Default").X="center" +States("Default").Y="center" +States("Default").Width="100%" +States("Default").Height="100%" +States("Default").XAlign="center" +States("Default").YAlign="center" +States("Default").BackColor="black" +States("Default").BackStyle="opaque" +States("Default").BorderColor="black" +States("Default").BorderWidth="0" +States("Default").ClearAfter="No" +States("Default").Enabled="Yes" +States("Default").ChildCount=1 +States("Default").Objects(1).Type="Text" +States("Default").Objects(1).Text="+" +States("Default").Objects(1).Width="10%" +States("Default").Objects(1).Height="10%" +States("Default").Objects(1).X="center" +States("Default").Objects(1).Y="center" +States("Default").Objects(1).AlignHorizontal="center" +States("Default").Objects(1).AlignVertical="center" +States("Default").Objects(1).FontName="Courier New" +States("Default").Objects(1).FontSize="18" +States("Default").Objects(1).FontBold="Yes" +States("Default").Objects(1).FontUnderline="No" +States("Default").Objects(1).FontStrikeout="No" +States("Default").Objects(1).FontItalic="No" +States("Default").Objects(1).ForeColor="silver" +States("Default").Objects(1).BackColor="silver" +States("Default").Objects(1).BackStyle="transparent" +States("Default").Objects(1).BorderWidth="0" +States("Default").Objects(1).BorderColor="black" +States("Default").Objects(1).XAlign="center" +States("Default").Objects(1).YAlign="center" +States("Default").Objects(1).WordWrap=1 +States("Default").Objects(1).Name="Text1" +Duration="5000" +JumpLabel="" +TimingMode=0 +PreRelease="40" +OnsetSync=1 +OffsetSync=0 +Name="StartSlide" +TypeName="Slide" +Tag="st01" +Notes="" + +[Object15] +_Version=65536 +_ExtentX=2646 +_ExtentY=1323 +_StockProps=0 +VersionMajor=1 +VersionMinor=2 +VersionInternal=1 +VersionBuild=21 +Logging("OnsetDelay").Log=0 +Logging("OnsetTime").Log=0 +Logging("DurationError").Log=0 +Logging("PreRelease").Log=0 +Logging("Duration").Log=0 +Logging("StartTime").Log=0 +Logging("OffsetTime").Log=0 +Logging("FinishTime").Log=0 +Logging("TimingMode").Log=0 +Logging("CustomOnsetTime").Log=0 +Logging("CustomOffsetTime").Log=0 +Logging("ActionDelay").Log=0 +Logging("ActionTime").Log=0 +Logging("TargetOffsetTime").Log=0 +Logging("TargetOnsetTime").Log=0 +Logging("OffsetDelay").Log=0 +Logging("RTTime").Log=0 +Logging("ACC").Log=0 +Logging("RT").Log=0 +Logging("RESP").Log=0 +Logging("CRESP").Log=0 +Logging("Tag").Log=0 +_VersionPersist=2 +ActiveState="Default" +States.Count=1 +States(1).Name="Default" +States("Default").X="center" +States("Default").Y="center" +States("Default").Width="100%" +States("Default").Height="100%" +States("Default").XAlign="center" +States("Default").YAlign="center" +States("Default").BackColor="black" +States("Default").BackStyle="opaque" +States("Default").BorderColor="black" +States("Default").BorderWidth="0" +States("Default").ClearAfter="No" +States("Default").Enabled="Yes" +States("Default").ChildCount=1 +States("Default").Objects(1).Type="Text" +States("Default").Objects(1).Text="+" +States("Default").Objects(1).Width="10%" +States("Default").Objects(1).Height="10%" +States("Default").Objects(1).X="center" +States("Default").Objects(1).Y="center" +States("Default").Objects(1).AlignHorizontal="center" +States("Default").Objects(1).AlignVertical="center" +States("Default").Objects(1).FontName="Courier New" +States("Default").Objects(1).FontSize="18" +States("Default").Objects(1).FontBold="Yes" +States("Default").Objects(1).FontUnderline="No" +States("Default").Objects(1).FontStrikeout="No" +States("Default").Objects(1).FontItalic="No" +States("Default").Objects(1).ForeColor="silver" +States("Default").Objects(1).BackColor="silver" +States("Default").Objects(1).BackStyle="transparent" +States("Default").Objects(1).BorderWidth="0" +States("Default").Objects(1).BorderColor="black" +States("Default").Objects(1).XAlign="center" +States("Default").Objects(1).YAlign="center" +States("Default").Objects(1).WordWrap=1 +States("Default").Objects(1).Name="Text1" +Duration="5000" +JumpLabel="" +TimingMode=0 +PreRelease="40" +OnsetSync=1 +OffsetSync=0 +Name="EndSlide" +TypeName="Slide" +Tag="st01" +Notes="" + +[Object16] +_Version=131072 +_ExtentX=2117 +_ExtentY=1058 +_StockProps=0 +VersionMajor=1 +VersionMinor=2 +VersionInternal=1 +VersionBuild=21 +_VersionPersist=1 +Code="Sleep(10)\nWritePort &H378, 0\n\nWhile Clock.Read < StimDisp.OnsetTime + 250 + c.GetAttrib(\"ISI_Dur\")\n\t\n\tIf StimDisp.InputMasks.Item(1).Responses.Count > NResp1 Then\n\t\tNResp1 = StimDisp.InputMasks.Item(1).Responses.Count\n\t\tWritePort &H378, CStr(\"20\" & StimDisp.InputMasks.Item(1).Responses(NResp1).RESP)\n\t\tSleep(2)\n\t\tWritePort &H378, 0\n\tEnd if\n\n\tIf Clock.Read > StimDisp.OnsetTime + 250 and StimClear = False Then\n\t\tWritePort &H378, 0\n\t\tStimImage.Filename=\"Stim//Black.bmp\"\n\t\tStimImage.Load\n\t\tStimDisp.Draw\n\t\tStimClear = True\n\tEnd if\n\nWEnd\n\n" +Name="TrialAdj" +TypeName="InLine" +Tag="" +Notes="" + +[Object17] +_Version=131072 +_ExtentX=2117 +_ExtentY=1058 +_StockProps=0 +VersionMajor=1 +VersionMinor=2 +VersionInternal=1 +VersionBuild=21 +_VersionPersist=1 +Code="'WritePort &H378, 254" +Name="StartRec" +TypeName="InLine" +Tag="" +Notes="" + +[Object18] +_Version=131072 +_ExtentX=2117 +_ExtentY=1058 +_StockProps=0 +VersionMajor=1 +VersionMinor=2 +VersionInternal=1 +VersionBuild=21 +_VersionPersist=1 +Code="'WritePort &H378, 255" +Name="StopRec" +TypeName="InLine" +Tag="" +Notes="" + +[UserScript] +Dim CellN as integer +Dim CatVal as integer +Dim CatStr as string +Dim IDVal as integer +Dim EmotVal as integer +Dim EmotStr as string +Dim InvVal as integer +Dim FqVal as integer +Dim SideStr as string + +Dim StimInMskMngr as InputMaskManager + +Dim NResp1 as integer + +Dim StimClear as boolean + +Dim StimImage As SlideImage + diff --git a/eeg_face13/sourcedata/tasks/ICTrn_FHC_SpFq_250Blk.ebs b/eeg_face13/sourcedata/tasks/ICTrn_FHC_SpFq_250Blk.ebs new file mode 100644 index 000000000..82608862b --- /dev/null +++ b/eeg_face13/sourcedata/tasks/ICTrn_FHC_SpFq_250Blk.ebs @@ -0,0 +1,2015 @@ +' +' +' +' C:\EEGTasks\00_OldTasks_BeforeJul14 2014\ICTrain\N1_SFq\ICTrn_FHC_SpFq_250Blk.ebs +' Generated on: 3/24/2018 19:23:22 +' +' +' This file generated with E-Studio interface. +' E-Prime Copyright © 1996-2005 Psychology Software Tools. +' ALL RIGHTS RESERVED +' +' Legal use of this experiment script requires a full E-Prime or Runtime License. +' +' +' Author: +' (Brock University) +' +' +' +' + +Option CStrings On +Dim ebContext as Context + + +'-------------------------------------------------------------------------- +' Class Declarations +'-------------------------------------------------------------------------- + + + +'-------------------------------------------------------------------------- +'InitSlideStateDefaults +'-------------------------------------------------------------------------- +Sub InitSlideStateDefaults(theSlideState As SlideState) + + If theSlideState Is Nothing Then Exit Sub + + theSlideState.X = "center" + theSlideState.Y = "center" + theSlideState.Width = "100%" + theSlideState.Height = "100%" + theSlideState.BackColor = CColor("white") + theSlideState.BackStyle = "opaque" + theSlideState.BorderColor = CColor("black") + theSlideState.BorderWidth = CLng("0") + theSlideState.XAlign = "center" + theSlideState.YAlign = "center" + theSlideState.ClearAfter = CLogical("No") + theSlideState.Enabled = CLogical("Yes") + +End Sub + +'-------------------------------------------------------------------------- +'InitSlideVisualStimDefaults +'-------------------------------------------------------------------------- +Sub InitSlideVisualStimDefaults(theSlideVisualStim As SlideVisualStim) + + If theSlideVisualStim Is Nothing Then Exit Sub + + theSlideVisualStim.X = "center" + theSlideVisualStim.Y = "center" + theSlideVisualStim.Width = "25%" + theSlideVisualStim.Height = "25%" + theSlideVisualStim.ForeColor = CColor("black") + theSlideVisualStim.BackColor = CColor("white") + theSlideVisualStim.BackStyle = "opaque" + theSlideVisualStim.BorderColor = CColor("black") + theSlideVisualStim.BorderWidth = CLng("0") + theSlideVisualStim.XAlign = "center" + theSlideVisualStim.YAlign = "center" + theSlideVisualStim.AlignHorizontal = "center" + theSlideVisualStim.AlignVertical = "center" + +End Sub + +'-------------------------------------------------------------------------- +'InitSlideTextDefaults +'-------------------------------------------------------------------------- +Sub InitSlideTextDefaults(theSlideText As SlideText) + + If theSlideText Is Nothing Then Exit Sub + + InitSlideVisualStimDefaults theSlideText + + theSlideText.WordWrap = True + theSlideText.FontName = "Courier New" + theSlideText.FontSize = "18" + theSlideText.FontBold = CLogical("Yes") + theSlideText.FontItalic = CLogical("No") + theSlideText.FontUnderline = CLogical("No") + theSlideText.FontStrikeout = CLogical("No") + +End Sub + +'-------------------------------------------------------------------------- +'InitSlideImageDefaults +'-------------------------------------------------------------------------- +Sub InitSlideImageDefaults(theSlideImage As SlideImage) + + If theSlideImage Is Nothing Then Exit Sub + + InitSlideVisualStimDefaults theSlideImage + + theSlideImage.Stretch = CLogical("No") + theSlideImage.MirrorLeftRight = CLogical("No") + theSlideImage.MirrorUpDown = CLogical("No") + theSlideImage.UseSourceColorKey = CLogical("No") + theSlideImage.SourceColorKey = CColor("black") + +End Sub + +'-------------------------------------------------------------------------- +'InitSlideSoundBufferDefaults +'-------------------------------------------------------------------------- +Sub InitSlideSoundBufferDefaults(theSoundBuffer As SoundBuffer) + + If theSoundBuffer Is Nothing Then Exit Sub + + theSoundBuffer.Loop = CLogical("No") + theSoundBuffer.StopAfter = CLogical("Yes") + theSoundBuffer.StartOffset = "0" + theSoundBuffer.StopOffset = "0" + +End Sub + + + + + + + + + + + + + + + + +'-------------------------------------------------------------------------- +'InitTextDisplayDefaults +'-------------------------------------------------------------------------- +Sub InitTextDisplayDefaults(theTextDisplay As TextDisplay) + + If theTextDisplay Is Nothing Then Exit Sub + + theTextDisplay.X = "center" + theTextDisplay.Y = "center" + theTextDisplay.Width = "100%" + theTextDisplay.Height = "100%" + theTextDisplay.ForeColor = CColor("black") + theTextDisplay.BackColor = CColor("white") + theTextDisplay.BackStyle = "opaque" + theTextDisplay.BorderColor = CColor("black") + theTextDisplay.BorderWidth = CLng("0") + theTextDisplay.XAlign = "center" + theTextDisplay.YAlign = "center" + theTextDisplay.AlignHorizontal = "center" + theTextDisplay.AlignVertical = "center" + theTextDisplay.WordWrap = True + theTextDisplay.ClearAfter = CLogical("No") + theTextDisplay.FontName = "Courier New" + theTextDisplay.FontSize = "18" + theTextDisplay.FontBold = CLogical("Yes") + theTextDisplay.FontItalic = CLogical("No") + theTextDisplay.FontUnderline = CLogical("No") + theTextDisplay.FontStrikeout = CLogical("No") + +End Sub + + + + + + + +'-------------------------------------------------------------------------- +' Instance Declarations +'-------------------------------------------------------------------------- +Dim Display As DisplayDevice +Dim Sound As SoundDevice +Dim Keyboard As KeyboardDevice +Dim Mouse As MouseDevice +Dim SRBOX As SRBOXDevice +Dim SessionProc As Procedure + +Dim MainList As List + +Dim TestList As List + +Dim TrialProc As Procedure + +Dim StimDisp1 As Slide +Dim StimDisp1_State As SlideState +Dim StimDisp1_SlideText As SlideText +Dim StimDisp1_SlideImage As SlideImage + +Dim StimDisp2 As Slide +Dim StimDisp2_State As SlideState +Dim StimDisp2_SlideText As SlideText +Dim StimDisp2_SlideImage As SlideImage + +Dim StimDisp3 As Slide +Dim StimDisp3_State As SlideState +Dim StimDisp3_SlideText As SlideText +Dim StimDisp3_SlideImage As SlideImage + +Dim StimDisp4 As Slide +Dim StimDisp4_State As SlideState +Dim StimDisp4_SlideText As SlideText +Dim StimDisp4_SlideImage As SlideImage + +Dim StimDisp5 As Slide +Dim StimDisp5_State As SlideState +Dim StimDisp5_SlideText As SlideText +Dim StimDisp5_SlideImage As SlideImage + +Dim StimDisp6 As Slide +Dim StimDisp6_State As SlideState +Dim StimDisp6_SlideText As SlideText +Dim StimDisp6_SlideImage As SlideImage + +Dim StimDisp7 As Slide +Dim StimDisp7_State As SlideState +Dim StimDisp7_SlideText As SlideText +Dim StimDisp7_SlideImage As SlideImage + +Dim StimDisp8 As Slide +Dim StimDisp8_State As SlideState +Dim StimDisp8_SlideText As SlideText +Dim StimDisp8_SlideImage As SlideImage + +Dim StimDisp9 As Slide +Dim StimDisp9_State As SlideState +Dim StimDisp9_SlideText As SlideText +Dim StimDisp9_SlideImage As SlideImage + +Dim InitProc As Procedure + +Dim StimDisp10 As Slide +Dim StimDisp10_State As SlideState +Dim StimDisp10_SlideText As SlideText +Dim StimDisp10_SlideImage As SlideImage + +Dim StimDisp11 As Slide +Dim StimDisp11_State As SlideState +Dim StimDisp11_SlideText As SlideText +Dim StimDisp11_SlideImage As SlideImage + +Dim StimDisp12 As Slide +Dim StimDisp12_State As SlideState +Dim StimDisp12_SlideText As SlideText +Dim StimDisp12_SlideImage As SlideImage + +Dim CellList As List + +Dim StartProc As Procedure + +Dim EndProc As Procedure + +Dim StartText As TextDisplay +Dim StartTextEchoClients As EchoClientCollection + +Dim EndText As TextDisplay +Dim EndTextEchoClients As EchoClientCollection + +Dim StartSlide As Slide +Dim StartSlide_State As SlideState +Dim StartSlide_SlideText As SlideText +Dim StartSlide_SlideImage As SlideImage + +Dim EndSlide As Slide +Dim EndSlide_State As SlideState +Dim EndSlide_SlideText As SlideText +Dim EndSlide_SlideImage As SlideImage + + + +'-------------------------------------------------------------------------- +' Package Declare Script +'-------------------------------------------------------------------------- + + + +'-------------------------------------------------------------------------- +' User Script +'-------------------------------------------------------------------------- +Dim CellN as integer +Dim CatVal as integer +Dim CatStr as string +Dim IDVal as integer +Dim EmotVal as integer +Dim EmotStr as string +Dim InvVal as integer + + +'-------------------------------------------------------------------------- +' Package Global Script +'-------------------------------------------------------------------------- + + + +'-------------------------------------------------------------------------- +' Package Routines +'-------------------------------------------------------------------------- + + + +'-------------------------------------------------------------------------- +' Implementation +'-------------------------------------------------------------------------- +Sub SessionProc_Run(c as Context) + MainList.Run c + + c.Log +End Sub + +Sub TrialProc_Run(c as Context) + + ''''''''''''''''''''''''''''''''''''''''''''' + ' InLine - TrnN170Init BEGIN + ''''''''''''''''''''''''''''''''''''''''''''' + c.SetAttrib "StimSlide_Dur", random(50,80) +CellN=c.GetAttrib("CellNumber") + +'define category variable +if CellN=1 or CellN=2 then + CatStr="H" +end if +if CellN=3 or CellN=4 then + CatStr="F" +end if +if CellN=5 or CellN=6 then + CatStr="C" +end if + + +'Define ID variable. +IDVal=random(1,5) +if CellN>4 then + IDVal=1 +end if + +'Define emotion variable. +EmotVal=random(1,2) +if EmotVal = 1 then + EmotStr = "F" +else + EmotStr = "A" +end if +if CellN<3 or CellN>4 then + EmotStr="" +end if + +'Define inversion variable +InvVal=1 +if CellN=2 or CellN=4 or CellN=6 then + InvVal=2 +end if + + +c.SetAttrib "TrnN170FName1", "Stim\\" & CatStr & CStr(IDVal) & EmotStr & "O" & CStr(InvVal) & "_F" & "8.bmp" +c.SetAttrib "TrnN170FName2", "Stim\\" & CatStr & CStr(IDVal) & EmotStr & "O" & CStr(InvVal) & "_F" & "7.bmp" +c.SetAttrib "TrnN170FName3", "Stim\\" & CatStr & CStr(IDVal) & EmotStr & "O" & CStr(InvVal) & "_F" & "6.bmp" +c.SetAttrib "TrnN170FName4", "Stim\\" & CatStr & CStr(IDVal) & EmotStr & "O" & CStr(InvVal) & "_F" & "5.bmp" +c.SetAttrib "TrnN170FName5", "Stim\\" & CatStr & CStr(IDVal) & EmotStr & "O" & CStr(InvVal) & "_F" & "4.bmp" +c.SetAttrib "TrnN170FName6", "Stim\\" & CatStr & CStr(IDVal) & EmotStr & "O" & CStr(InvVal) & "_F" & "3.bmp" +c.SetAttrib "TrnN170FName7", "Stim\\" & CatStr & CStr(IDVal) & EmotStr & "O" & CStr(InvVal) & "_F" & "2.bmp" +c.SetAttrib "TrnN170FName8", "Stim\\" & CatStr & CStr(IDVal) & EmotStr & "O" & CStr(InvVal) & "_F" & "1.bmp" + +StimDisp1.OnsetSignalData = CellN*10 + 1 +StimDisp2.OnsetSignalData = CellN*10 + 2 +StimDisp3.OnsetSignalData = CellN*10 + 3 +StimDisp4.OnsetSignalData = CellN*10 + 4 +StimDisp5.OnsetSignalData = CellN*10 + 5 +StimDisp6.OnsetSignalData = CellN*10 + 6 + + ''''''''''''''''''''''''''''''''''''''''''''' + ' InLine - TrnN170Init END + ''''''''''''''''''''''''''''''''''''''''''''' + + + + Select Case StimDisp1.ActiveState + Case "Default" + + + Set StimDisp1_SlideText = CSlideText(StimDisp1.States.Item("Default").Objects(1)) + Set StimDisp1_SlideText = Nothing + + Set StimDisp1_SlideImage = CSlideImage(StimDisp1.States.Item("Default").Objects(2)) + StimDisp1_SlideImage.Filename = c.GetAttrib("TrnN170FName2") + StimDisp1_SlideImage.Load + Set StimDisp1_SlideImage = Nothing + End Select + + StimDisp1.Duration = CLng(c.GetAttrib("StimSlide_Dur")) + StimDisp1.Run + + + + Select Case StimDisp2.ActiveState + Case "Default" + + + Set StimDisp2_SlideText = CSlideText(StimDisp2.States.Item("Default").Objects(1)) + Set StimDisp2_SlideText = Nothing + + Set StimDisp2_SlideImage = CSlideImage(StimDisp2.States.Item("Default").Objects(2)) + StimDisp2_SlideImage.Filename = c.GetAttrib("TrnN170FName3") + StimDisp2_SlideImage.Load + Set StimDisp2_SlideImage = Nothing + End Select + + StimDisp2.Duration = CLng(c.GetAttrib("StimSlide_Dur")) + StimDisp2.Run + + + + Select Case StimDisp3.ActiveState + Case "Default" + + + Set StimDisp3_SlideText = CSlideText(StimDisp3.States.Item("Default").Objects(1)) + Set StimDisp3_SlideText = Nothing + + Set StimDisp3_SlideImage = CSlideImage(StimDisp3.States.Item("Default").Objects(2)) + StimDisp3_SlideImage.Filename = c.GetAttrib("TrnN170FName4") + StimDisp3_SlideImage.Load + Set StimDisp3_SlideImage = Nothing + End Select + + StimDisp3.Duration = CLng(c.GetAttrib("StimSlide_Dur")) + StimDisp3.Run + + + + Select Case StimDisp4.ActiveState + Case "Default" + + + Set StimDisp4_SlideText = CSlideText(StimDisp4.States.Item("Default").Objects(1)) + Set StimDisp4_SlideText = Nothing + + Set StimDisp4_SlideImage = CSlideImage(StimDisp4.States.Item("Default").Objects(2)) + StimDisp4_SlideImage.Filename = c.GetAttrib("TrnN170FName5") + StimDisp4_SlideImage.Load + Set StimDisp4_SlideImage = Nothing + End Select + + StimDisp4.Duration = CLng(c.GetAttrib("StimSlide_Dur")) + StimDisp4.Run + + + + Select Case StimDisp5.ActiveState + Case "Default" + + + Set StimDisp5_SlideText = CSlideText(StimDisp5.States.Item("Default").Objects(1)) + Set StimDisp5_SlideText = Nothing + + Set StimDisp5_SlideImage = CSlideImage(StimDisp5.States.Item("Default").Objects(2)) + StimDisp5_SlideImage.Filename = c.GetAttrib("TrnN170FName6") + StimDisp5_SlideImage.Load + Set StimDisp5_SlideImage = Nothing + End Select + + StimDisp5.Duration = CLng(c.GetAttrib("StimSlide_Dur")) + StimDisp5.Run + + + + Select Case StimDisp6.ActiveState + Case "Default" + + + Set StimDisp6_SlideText = CSlideText(StimDisp6.States.Item("Default").Objects(1)) + Set StimDisp6_SlideText = Nothing + + Set StimDisp6_SlideImage = CSlideImage(StimDisp6.States.Item("Default").Objects(2)) + StimDisp6_SlideImage.Filename = c.GetAttrib("TrnN170FName7") + StimDisp6_SlideImage.Load + Set StimDisp6_SlideImage = Nothing + End Select + + StimDisp6.Duration = CLng(c.GetAttrib("StimSlide_Dur")) + StimDisp6.Run + + + + Select Case StimDisp7.ActiveState + Case "Default" + + + Set StimDisp7_SlideText = CSlideText(StimDisp7.States.Item("Default").Objects(1)) + Set StimDisp7_SlideText = Nothing + + Set StimDisp7_SlideImage = CSlideImage(StimDisp7.States.Item("Default").Objects(2)) + StimDisp7_SlideImage.Filename = c.GetAttrib("TrnN170FName6") + StimDisp7_SlideImage.Load + Set StimDisp7_SlideImage = Nothing + End Select + + StimDisp7.Duration = CLng(c.GetAttrib("StimSlide_Dur")) + StimDisp7.Run + + + + Select Case StimDisp8.ActiveState + Case "Default" + + + Set StimDisp8_SlideText = CSlideText(StimDisp8.States.Item("Default").Objects(1)) + Set StimDisp8_SlideText = Nothing + + Set StimDisp8_SlideImage = CSlideImage(StimDisp8.States.Item("Default").Objects(2)) + StimDisp8_SlideImage.Filename = c.GetAttrib("TrnN170FName5") + StimDisp8_SlideImage.Load + Set StimDisp8_SlideImage = Nothing + End Select + + StimDisp8.Duration = CLng(c.GetAttrib("StimSlide_Dur")) + StimDisp8.Run + + + + Select Case StimDisp9.ActiveState + Case "Default" + + + Set StimDisp9_SlideText = CSlideText(StimDisp9.States.Item("Default").Objects(1)) + Set StimDisp9_SlideText = Nothing + + Set StimDisp9_SlideImage = CSlideImage(StimDisp9.States.Item("Default").Objects(2)) + StimDisp9_SlideImage.Filename = c.GetAttrib("TrnN170FName4") + StimDisp9_SlideImage.Load + Set StimDisp9_SlideImage = Nothing + End Select + + StimDisp9.Duration = CLng(c.GetAttrib("StimSlide_Dur")) + StimDisp9.Run + + + + Select Case StimDisp10.ActiveState + Case "Default" + + + Set StimDisp10_SlideText = CSlideText(StimDisp10.States.Item("Default").Objects(1)) + Set StimDisp10_SlideText = Nothing + + Set StimDisp10_SlideImage = CSlideImage(StimDisp10.States.Item("Default").Objects(2)) + StimDisp10_SlideImage.Filename = c.GetAttrib("TrnN170FName3") + StimDisp10_SlideImage.Load + Set StimDisp10_SlideImage = Nothing + End Select + + StimDisp10.Duration = CLng(c.GetAttrib("StimSlide_Dur")) + StimDisp10.Run + + + + Select Case StimDisp11.ActiveState + Case "Default" + + + Set StimDisp11_SlideText = CSlideText(StimDisp11.States.Item("Default").Objects(1)) + Set StimDisp11_SlideText = Nothing + + Set StimDisp11_SlideImage = CSlideImage(StimDisp11.States.Item("Default").Objects(2)) + StimDisp11_SlideImage.Filename = c.GetAttrib("TrnN170FName2") + StimDisp11_SlideImage.Load + Set StimDisp11_SlideImage = Nothing + End Select + + StimDisp11.Duration = CLng(c.GetAttrib("StimSlide_Dur")) + StimDisp11.Run + + + + Select Case StimDisp12.ActiveState + Case "Default" + + + Set StimDisp12_SlideText = CSlideText(StimDisp12.States.Item("Default").Objects(1)) + Set StimDisp12_SlideText = Nothing + + Set StimDisp12_SlideImage = CSlideImage(StimDisp12.States.Item("Default").Objects(2)) + StimDisp12_SlideImage.Filename = c.GetAttrib("TrnN170FName1") + StimDisp12_SlideImage.Load + Set StimDisp12_SlideImage = Nothing + End Select + + StimDisp12.Duration = CLng(c.GetAttrib("StimSlide_Dur")) + StimDisp12.Run + + + c.Log +End Sub + + + + + + + + + + + + + + + + + + + +Sub InitProc_Run(c as Context) + + ''''''''''''''''''''''''''''''''''''''''''''' + ' InLine - Init BEGIN + ''''''''''''''''''''''''''''''''''''''''''''' + StimDisp1.OnsetSignalEnabled = True +StimDisp1.OnsetSignalPort = &H378 +StimDisp1.OffsetSignalEnabled = True +StimDisp1.OffsetSignalPort = &H378 +StimDisp1.OffsetSignalData = 0 + +StimDisp2.OnsetSignalEnabled = True +StimDisp2.OnsetSignalPort = &H378 +StimDisp2.OffsetSignalEnabled = True +StimDisp2.OffsetSignalPort = &H378 +StimDisp2.OffsetSignalData = 0 + +StimDisp3.OnsetSignalEnabled = True +StimDisp3.OnsetSignalPort = &H378 +StimDisp3.OffsetSignalEnabled = True +StimDisp3.OffsetSignalPort = &H378 +StimDisp3.OffsetSignalData = 0 + +StimDisp4.OnsetSignalEnabled = True +StimDisp4.OnsetSignalPort = &H378 +StimDisp4.OffsetSignalEnabled = True +StimDisp4.OffsetSignalPort = &H378 +StimDisp4.OffsetSignalData = 0 + +StimDisp5.OnsetSignalEnabled = True +StimDisp5.OnsetSignalPort = &H378 +StimDisp5.OffsetSignalEnabled = True +StimDisp5.OffsetSignalPort = &H378 +StimDisp5.OffsetSignalData = 0 + +StimDisp6.OnsetSignalEnabled = True +StimDisp6.OnsetSignalPort = &H378 +StimDisp6.OffsetSignalEnabled = True +StimDisp6.OffsetSignalPort = &H378 +StimDisp6.OffsetSignalData = 0 + + + + ''''''''''''''''''''''''''''''''''''''''''''' + ' InLine - Init END + ''''''''''''''''''''''''''''''''''''''''''''' + + + c.Log +End Sub + + + + + + + +Sub StartProc_Run(c as Context) + + + StartText.InputMasks.Reset + + If Keyboard.GetState() = ebStateOpen Then + StartTextEchoClients.RemoveAll + StartText.InputMasks.Add Keyboard.CreateInputMask("{ANY}", "", CLng(StartText.Duration), CLng("1"), ebEndResponseActionTerminate, CLogical("Yes"), "", "", "ResponseMode:All ProcessBackspace:Yes") + + End If + + StartText.Run + + ''''''''''''''''''''''''''''''''''''''''''''' + ' InLine - StartRec BEGIN + ''''''''''''''''''''''''''''''''''''''''''''' + 'WritePort &H378, 254 + ''''''''''''''''''''''''''''''''''''''''''''' + ' InLine - StartRec END + ''''''''''''''''''''''''''''''''''''''''''''' + + + + Select Case StartSlide.ActiveState + Case "Default" + + + Set StartSlide_SlideText = CSlideText(StartSlide.States.Item("Default").Objects(1)) + Set StartSlide_SlideText = Nothing + + Set StartSlide_SlideImage = CSlideImage(StartSlide.States.Item("Default").Objects(2)) + Set StartSlide_SlideImage = Nothing + End Select + + StartSlide.Run + + + c.Log +End Sub + +Sub EndProc_Run(c as Context) + + + Select Case EndSlide.ActiveState + Case "Default" + + + Set EndSlide_SlideText = CSlideText(EndSlide.States.Item("Default").Objects(1)) + Set EndSlide_SlideText = Nothing + + Set EndSlide_SlideImage = CSlideImage(EndSlide.States.Item("Default").Objects(2)) + Set EndSlide_SlideImage = Nothing + End Select + + EndSlide.Run + + + ''''''''''''''''''''''''''''''''''''''''''''' + ' InLine - StopRec BEGIN + ''''''''''''''''''''''''''''''''''''''''''''' + 'WritePort &H378, 255 + ''''''''''''''''''''''''''''''''''''''''''''' + ' InLine - StopRec END + ''''''''''''''''''''''''''''''''''''''''''''' + + + + EndText.InputMasks.Reset + + If Keyboard.GetState() = ebStateOpen Then + EndTextEchoClients.RemoveAll + EndText.InputMasks.Add Keyboard.CreateInputMask("{ANY}", "", CLng(EndText.Duration), CLng("1"), ebEndResponseActionTerminate, CLogical("Yes"), "", "", "ResponseMode:All ProcessBackspace:Yes") + + End If + + EndText.Run + + c.Log +End Sub + + + + + + + +'-------------------------------------------------------------------------- +' InitDevices +' +'-------------------------------------------------------------------------- +Sub InitDevices(c As Context) + + + + SetOSThreadPriority 3 + + + Set Display = New DisplayDevice + Display.Name = "Display" + + Dim DisplayDisplayDeviceInfo As DisplayDeviceInfo + DisplayDisplayDeviceInfo.XRes = 1024 + DisplayDisplayDeviceInfo.YRes = 768 + DisplayDisplayDeviceInfo.ColorDepth = 16 + DisplayDisplayDeviceInfo.RefreshRate = 0 + DisplayDisplayDeviceInfo.NumPages = 0 + + 'Load values from context if they exist + If c.AttribExists("Display.XRes") Then DisplayDisplayDeviceInfo.XRes = c.GetAttrib("Display.XRes") + If c.AttribExists("Display.YRes") Then DisplayDisplayDeviceInfo.YRes = c.GetAttrib("Display.YRes") + If c.AttribExists("Display.ColorDepth") Then DisplayDisplayDeviceInfo.ColorDepth = c.GetAttrib("Display.ColorDepth") + + 'Open the device, unless the context values indicate otherwise + Dim DisplayOpen As Boolean + DisplayOpen = True + If c.AttribExists("Display.Open") Then DisplayOpen = CLogical(c.GetAttrib("Display.Open")) + If DisplayOpen = True Then + Display.Open DisplayDisplayDeviceInfo + c.SetAttrib "Display.RefreshRate", Format$(Display.CalculatedRefreshRate, "0.000") + End If + + Set Sound = New SoundDevice + Sound.Name = "Sound" + + Dim SoundSoundOutDeviceInfo As SoundDeviceInfo + SoundSoundOutDeviceInfo.Channels = 2 + SoundSoundOutDeviceInfo.SamplesPerSecond = 22050 + SoundSoundOutDeviceInfo.BitsPerSample = 16 + 'Load values from context if they exist + If c.AttribExists("Sound.Channels") Then SoundSoundOutDeviceInfo.Channels = c.GetAttrib("Sound.Channels") + If c.AttribExists("Sound.SamplesPerSecond") Then SoundSoundOutDeviceInfo.SamplesPerSecond = c.GetAttrib("Sound.SamplesPerSecond") + If c.AttribExists("Sound.BitsPerSample") Then SoundSoundOutDeviceInfo.BitsPerSample = c.GetAttrib("Sound.BitsPerSample") + + Set Keyboard = New KeyboardDevice + Keyboard.Name = "Keyboard" + + Dim KeyboardKeyboardDeviceInfo as KeyboardDeviceInfo + KeyboardKeyboardDeviceInfo.CollectionMode = ebPressesOnly + KeyboardKeyboardDeviceInfo.CapsLock = ebCapsLockOff + KeyboardKeyboardDeviceInfo.NumLock = ebNumLockOn + 'Load values from context if they exist + If c.AttribExists("Keyboard.CollectionMode") Then KeyboardKeyboardDeviceInfo.CollectionMode = c.GetAttrib("Keyboard.CollectionMode") + If c.AttribExists("Keyboard.CapsLock") Then KeyboardKeyboardDeviceInfo.CapsLock = c.GetAttrib("Keyboard.CapsLock") + If c.AttribExists("Keyboard.NumLock") Then KeyboardKeyboardDeviceInfo.NumLock = c.GetAttrib("Keyboard.NumLock") + If c.AttribExists("Keyboard.EmulateDeviceName") Then KeyboardKeyboardDeviceInfo.EmulateDeviceName = c.GetAttrib("Keyboard.EmulateDeviceName") + + 'Open the device, unless the context values indicate otherwise + Dim KeyboardOpen As Boolean + KeyboardOpen = True + If c.AttribExists("Keyboard.Open") Then KeyboardOpen = CLogical(c.GetAttrib("Keyboard.Open")) + If KeyboardOpen = True Then + Keyboard.Open KeyboardKeyboardDeviceInfo + End If + + Set Mouse = New MouseDevice + Mouse.Name = "Mouse" + + Dim MouseMouseDeviceInfo as MouseDeviceInfo + MouseMouseDeviceInfo.OpenMode = ebMouseOpenModeDirect + MouseMouseDeviceInfo.CollectionMode = ebPressesOnly + MouseMouseDeviceInfo.ShowCursor = False + 'Load values from context if they exist + If c.AttribExists("Mouse.OpenMode") Then MouseMouseDeviceInfo.OpenMode = c.GetAttrib("Mouse.OpenMode") + If c.AttribExists("Mouse.CollectionMode") Then MouseMouseDeviceInfo.CollectionMode = c.GetAttrib("Mouse.CollectionMode") + If c.AttribExists("Mouse.ShowCursor") Then MouseMouseDeviceInfo.ShowCursor = c.GetAttrib("Mouse.ShowCursor") + If c.AttribExists("Mouse.EmulateDeviceName") Then MouseMouseDeviceInfo.EmulateDeviceName = c.GetAttrib("Mouse.EmulateDeviceName") + + 'Open the device, unless the context values indicate otherwise + Dim MouseOpen As Boolean + MouseOpen = True + If c.AttribExists("Mouse.Open") Then MouseOpen = CLogical(c.GetAttrib("Mouse.Open")) + If MouseOpen = True Then + Mouse.Open MouseMouseDeviceInfo + End If + + Set SRBOX = New SRBoxDevice + SRBOX.Name = "SRBOX" + + Dim SRBOXSRBoxDeviceInfo As SRBoxDeviceInfo + SRBOXSRBoxDeviceInfo.Model = 200 + SRBOXSRBoxDeviceInfo.Revision = 1 + SRBOXSRBoxDeviceInfo.CommPort = 1 + SRBOXSRBoxDeviceInfo.BaudRate = 19200 + SRBOXSRBoxDeviceInfo.CharacterRate = 800 + SRBOXSRBoxDeviceInfo.Configuration = ebSRBoxConfigStandard + SRBOXSRBoxDeviceInfo.CollectionMode = ebPressesOnly + 'Load values from context if they exist + If c.AttribExists("SRBOX.CommPort") Then SRBOXSRBoxDeviceInfo.CommPort = c.GetAttrib("SRBOX.CommPort") + If c.AttribExists("SRBOX.BaudRate") Then SRBOXSRBoxDeviceInfo.BaudRate = c.GetAttrib("SRBOX.BaudRate") + If c.AttribExists("SRBOX.CharacterRate") Then SRBOXSRBoxDeviceInfo.CharacterRate = c.GetAttrib("SRBOX.CharacterRate") + If c.AttribExists("SRBOX.Configuration") Then SRBOXSRBoxDeviceInfo.Configuration = c.GetAttrib("SRBOX.Configuration") + If c.AttribExists("SRBOX.CollectionMode") Then SRBOXSRBoxDeviceInfo.CollectionMode = c.GetAttrib("SRBOX.CollectionMode") + If c.AttribExists("SRBOX.EmulateDeviceName") Then SRBOXSRBoxDeviceInfo.EmulateDeviceName = c.GetAttrib("SRBOX.EmulateDeviceName") + + 'Open the device, unless the context values indicate otherwise + Dim SRBOXOpen As Boolean + SRBOXOpen = True + If c.AttribExists("SRBOX.Open") Then SRBOXOpen = CLogical(c.GetAttrib("SRBOX.Open")) + If SRBOXOpen = True Then + SRBOX.Open SRBOXSRBoxDeviceInfo + End If + + + SetOSThreadPriority 0 + +End Sub + + + +'-------------------------------------------------------------------------- +' InitObjects +' +'-------------------------------------------------------------------------- +Sub InitObjects(c As Context) + + Set SessionProc = New Procedure + SessionProc.Name = "SessionProc" + SessionProc.Tag = "" + SessionProc.Subroutine = "SessionProc_Run" + + Set MainList = New List + MainList.Name = "MainList" + MainList.Tag = "" + + 'Initialization for MainList + + Set MainList.Order = New SequentialOrder + Set MainList.Deletion = NoDeletion + MainList.ResetEveryRun = False + + ' Create the column headings + MainList.Filename = "" + MainList.LoadMethod = ebLoadMethodEmbedded + + MainList.Load + + Set MainList.TerminateCondition = Cycles(1) + Set MainList.ResetCondition = Samples(253) + MainList.Reset + + Set TestList = New List + TestList.Name = "TestList" + TestList.Tag = "" + + 'Initialization for TestList + + Set TestList.Order = New RandomOrder + Set TestList.Deletion = NoDeletion + TestList.ResetEveryRun = False + + ' Create the column headings + TestList.AddAttrib "CellNumber" + TestList.Filename = "" + TestList.LoadMethod = ebLoadMethodEmbedded + + TestList.Load + + Set TestList.TerminateCondition = Cycles(1) + Set TestList.ResetCondition = Samples(5) + TestList.Reset + + Set TrialProc = New Procedure + TrialProc.Name = "TrialProc" + TrialProc.Tag = "" + TrialProc.Subroutine = "TrialProc_Run" + + Set StimDisp1 = New Slide + StimDisp1.Name = "StimDisp1" + StimDisp1.Tag = "at02" + StimDisp1.TimingMode = ebTimingModeEvent + StimDisp1.PreRelease = Val("40") + + StimDisp1.OnsetSync = 1 + StimDisp1.OffsetSync = 0 + + Set StimDisp1_State = New SlideState + StimDisp1_State.Name = "Default" + + InitSlideStateDefaults StimDisp1_State + + StimDisp1_State.BackColor = CColor("black") + + StimDisp1.States.Add StimDisp1_State, "Default" + + + Set StimDisp1_SlideText = New SlideText + StimDisp1_SlideText.Name = "Text1" + + InitSlideTextDefaults StimDisp1_SlideText + + StimDisp1_SlideText.Text = "+" + StimDisp1_SlideText.Width = "10%" + StimDisp1_SlideText.Height = "10%" + StimDisp1_SlideText.ForeColor = CColor("silver") + StimDisp1_SlideText.BackStyle = "transparent" + StimDisp1.States.Item("Default").Objects.Add StimDisp1_SlideText, "Text1" + + Set StimDisp1_SlideImage = New SlideImage + StimDisp1_SlideImage.Name = "Image1" + + InitSlideImageDefaults StimDisp1_SlideImage + + StimDisp1_SlideImage.Stretch = CLogical("Yes") + StimDisp1_SlideImage.Width = "204" + StimDisp1_SlideImage.Height = "256" + StimDisp1.States.Item("Default").Objects.Add StimDisp1_SlideImage, "Image1" + StimDisp1.ActiveState = "Default" + + Set StimDisp2 = New Slide + StimDisp2.Name = "StimDisp2" + StimDisp2.Tag = "st03" + StimDisp2.TimingMode = ebTimingModeEvent + StimDisp2.PreRelease = Val("40") + + StimDisp2.OnsetSync = 1 + StimDisp2.OffsetSync = 0 + + Set StimDisp2_State = New SlideState + StimDisp2_State.Name = "Default" + + InitSlideStateDefaults StimDisp2_State + + StimDisp2_State.BackColor = CColor("black") + + StimDisp2.States.Add StimDisp2_State, "Default" + + + Set StimDisp2_SlideText = New SlideText + StimDisp2_SlideText.Name = "Text1" + + InitSlideTextDefaults StimDisp2_SlideText + + StimDisp2_SlideText.Text = "+" + StimDisp2_SlideText.Width = "10%" + StimDisp2_SlideText.Height = "10%" + StimDisp2_SlideText.ForeColor = CColor("silver") + StimDisp2_SlideText.BackStyle = "transparent" + StimDisp2.States.Item("Default").Objects.Add StimDisp2_SlideText, "Text1" + + Set StimDisp2_SlideImage = New SlideImage + StimDisp2_SlideImage.Name = "Image1" + + InitSlideImageDefaults StimDisp2_SlideImage + + StimDisp2_SlideImage.Stretch = CLogical("Yes") + StimDisp2_SlideImage.Width = "204" + StimDisp2_SlideImage.Height = "256" + StimDisp2.States.Item("Default").Objects.Add StimDisp2_SlideImage, "Image1" + StimDisp2.ActiveState = "Default" + + Set StimDisp3 = New Slide + StimDisp3.Name = "StimDisp3" + StimDisp3.Tag = "st04" + StimDisp3.TimingMode = ebTimingModeEvent + StimDisp3.PreRelease = Val("40") + + StimDisp3.OnsetSync = 1 + StimDisp3.OffsetSync = 0 + + Set StimDisp3_State = New SlideState + StimDisp3_State.Name = "Default" + + InitSlideStateDefaults StimDisp3_State + + StimDisp3_State.BackColor = CColor("black") + + StimDisp3.States.Add StimDisp3_State, "Default" + + + Set StimDisp3_SlideText = New SlideText + StimDisp3_SlideText.Name = "Text1" + + InitSlideTextDefaults StimDisp3_SlideText + + StimDisp3_SlideText.Text = "+" + StimDisp3_SlideText.Width = "10%" + StimDisp3_SlideText.Height = "10%" + StimDisp3_SlideText.ForeColor = CColor("silver") + StimDisp3_SlideText.BackStyle = "transparent" + StimDisp3.States.Item("Default").Objects.Add StimDisp3_SlideText, "Text1" + + Set StimDisp3_SlideImage = New SlideImage + StimDisp3_SlideImage.Name = "Image1" + + InitSlideImageDefaults StimDisp3_SlideImage + + StimDisp3_SlideImage.Stretch = CLogical("Yes") + StimDisp3_SlideImage.Width = "204" + StimDisp3_SlideImage.Height = "256" + StimDisp3.States.Item("Default").Objects.Add StimDisp3_SlideImage, "Image1" + StimDisp3.ActiveState = "Default" + + Set StimDisp4 = New Slide + StimDisp4.Name = "StimDisp4" + StimDisp4.Tag = "st05" + StimDisp4.TimingMode = ebTimingModeEvent + StimDisp4.PreRelease = Val("40") + + StimDisp4.OnsetSync = 1 + StimDisp4.OffsetSync = 0 + + Set StimDisp4_State = New SlideState + StimDisp4_State.Name = "Default" + + InitSlideStateDefaults StimDisp4_State + + StimDisp4_State.BackColor = CColor("black") + + StimDisp4.States.Add StimDisp4_State, "Default" + + + Set StimDisp4_SlideText = New SlideText + StimDisp4_SlideText.Name = "Text1" + + InitSlideTextDefaults StimDisp4_SlideText + + StimDisp4_SlideText.Text = "+" + StimDisp4_SlideText.Width = "10%" + StimDisp4_SlideText.Height = "10%" + StimDisp4_SlideText.ForeColor = CColor("silver") + StimDisp4_SlideText.BackStyle = "transparent" + StimDisp4.States.Item("Default").Objects.Add StimDisp4_SlideText, "Text1" + + Set StimDisp4_SlideImage = New SlideImage + StimDisp4_SlideImage.Name = "Image1" + + InitSlideImageDefaults StimDisp4_SlideImage + + StimDisp4_SlideImage.Stretch = CLogical("Yes") + StimDisp4_SlideImage.Width = "204" + StimDisp4_SlideImage.Height = "256" + StimDisp4.States.Item("Default").Objects.Add StimDisp4_SlideImage, "Image1" + StimDisp4.ActiveState = "Default" + + Set StimDisp5 = New Slide + StimDisp5.Name = "StimDisp5" + StimDisp5.Tag = "st06" + StimDisp5.TimingMode = ebTimingModeEvent + StimDisp5.PreRelease = Val("40") + + StimDisp5.OnsetSync = 1 + StimDisp5.OffsetSync = 0 + + Set StimDisp5_State = New SlideState + StimDisp5_State.Name = "Default" + + InitSlideStateDefaults StimDisp5_State + + StimDisp5_State.BackColor = CColor("black") + + StimDisp5.States.Add StimDisp5_State, "Default" + + + Set StimDisp5_SlideText = New SlideText + StimDisp5_SlideText.Name = "Text1" + + InitSlideTextDefaults StimDisp5_SlideText + + StimDisp5_SlideText.Text = "+" + StimDisp5_SlideText.Width = "10%" + StimDisp5_SlideText.Height = "10%" + StimDisp5_SlideText.ForeColor = CColor("silver") + StimDisp5_SlideText.BackStyle = "transparent" + StimDisp5.States.Item("Default").Objects.Add StimDisp5_SlideText, "Text1" + + Set StimDisp5_SlideImage = New SlideImage + StimDisp5_SlideImage.Name = "Image1" + + InitSlideImageDefaults StimDisp5_SlideImage + + StimDisp5_SlideImage.Stretch = CLogical("Yes") + StimDisp5_SlideImage.Width = "204" + StimDisp5_SlideImage.Height = "256" + StimDisp5.States.Item("Default").Objects.Add StimDisp5_SlideImage, "Image1" + StimDisp5.ActiveState = "Default" + + Set StimDisp6 = New Slide + StimDisp6.Name = "StimDisp6" + StimDisp6.Tag = "st07" + StimDisp6.TimingMode = ebTimingModeEvent + StimDisp6.PreRelease = Val("40") + + StimDisp6.OnsetSync = 1 + StimDisp6.OffsetSync = 0 + + Set StimDisp6_State = New SlideState + StimDisp6_State.Name = "Default" + + InitSlideStateDefaults StimDisp6_State + + StimDisp6_State.BackColor = CColor("black") + + StimDisp6.States.Add StimDisp6_State, "Default" + + + Set StimDisp6_SlideText = New SlideText + StimDisp6_SlideText.Name = "Text1" + + InitSlideTextDefaults StimDisp6_SlideText + + StimDisp6_SlideText.Text = "+" + StimDisp6_SlideText.Width = "10%" + StimDisp6_SlideText.Height = "10%" + StimDisp6_SlideText.ForeColor = CColor("silver") + StimDisp6_SlideText.BackStyle = "transparent" + StimDisp6.States.Item("Default").Objects.Add StimDisp6_SlideText, "Text1" + + Set StimDisp6_SlideImage = New SlideImage + StimDisp6_SlideImage.Name = "Image1" + + InitSlideImageDefaults StimDisp6_SlideImage + + StimDisp6_SlideImage.Stretch = CLogical("Yes") + StimDisp6_SlideImage.Width = "204" + StimDisp6_SlideImage.Height = "256" + StimDisp6.States.Item("Default").Objects.Add StimDisp6_SlideImage, "Image1" + StimDisp6.ActiveState = "Default" + + Set StimDisp7 = New Slide + StimDisp7.Name = "StimDisp7" + StimDisp7.Tag = "st14" + StimDisp7.TimingMode = ebTimingModeEvent + StimDisp7.PreRelease = Val("40") + + StimDisp7.OnsetSync = 1 + StimDisp7.OffsetSync = 0 + + Set StimDisp7_State = New SlideState + StimDisp7_State.Name = "Default" + + InitSlideStateDefaults StimDisp7_State + + StimDisp7_State.BackColor = CColor("black") + + StimDisp7.States.Add StimDisp7_State, "Default" + + + Set StimDisp7_SlideText = New SlideText + StimDisp7_SlideText.Name = "Text1" + + InitSlideTextDefaults StimDisp7_SlideText + + StimDisp7_SlideText.Text = "+" + StimDisp7_SlideText.Width = "10%" + StimDisp7_SlideText.Height = "10%" + StimDisp7_SlideText.ForeColor = CColor("silver") + StimDisp7_SlideText.BackStyle = "transparent" + StimDisp7.States.Item("Default").Objects.Add StimDisp7_SlideText, "Text1" + + Set StimDisp7_SlideImage = New SlideImage + StimDisp7_SlideImage.Name = "Image1" + + InitSlideImageDefaults StimDisp7_SlideImage + + StimDisp7_SlideImage.Stretch = CLogical("Yes") + StimDisp7_SlideImage.Width = "204" + StimDisp7_SlideImage.Height = "256" + StimDisp7.States.Item("Default").Objects.Add StimDisp7_SlideImage, "Image1" + StimDisp7.ActiveState = "Default" + + Set StimDisp8 = New Slide + StimDisp8.Name = "StimDisp8" + StimDisp8.Tag = "st15" + StimDisp8.TimingMode = ebTimingModeEvent + StimDisp8.PreRelease = Val("40") + + StimDisp8.OnsetSync = 1 + StimDisp8.OffsetSync = 0 + + Set StimDisp8_State = New SlideState + StimDisp8_State.Name = "Default" + + InitSlideStateDefaults StimDisp8_State + + StimDisp8_State.BackColor = CColor("black") + + StimDisp8.States.Add StimDisp8_State, "Default" + + + Set StimDisp8_SlideText = New SlideText + StimDisp8_SlideText.Name = "Text1" + + InitSlideTextDefaults StimDisp8_SlideText + + StimDisp8_SlideText.Text = "+" + StimDisp8_SlideText.Width = "10%" + StimDisp8_SlideText.Height = "10%" + StimDisp8_SlideText.ForeColor = CColor("silver") + StimDisp8_SlideText.BackStyle = "transparent" + StimDisp8.States.Item("Default").Objects.Add StimDisp8_SlideText, "Text1" + + Set StimDisp8_SlideImage = New SlideImage + StimDisp8_SlideImage.Name = "Image1" + + InitSlideImageDefaults StimDisp8_SlideImage + + StimDisp8_SlideImage.Stretch = CLogical("Yes") + StimDisp8_SlideImage.Width = "204" + StimDisp8_SlideImage.Height = "256" + StimDisp8.States.Item("Default").Objects.Add StimDisp8_SlideImage, "Image1" + StimDisp8.ActiveState = "Default" + + Set StimDisp9 = New Slide + StimDisp9.Name = "StimDisp9" + StimDisp9.Tag = "st16" + StimDisp9.TimingMode = ebTimingModeEvent + StimDisp9.PreRelease = Val("40") + + StimDisp9.OnsetSync = 1 + StimDisp9.OffsetSync = 0 + + Set StimDisp9_State = New SlideState + StimDisp9_State.Name = "Default" + + InitSlideStateDefaults StimDisp9_State + + StimDisp9_State.BackColor = CColor("black") + + StimDisp9.States.Add StimDisp9_State, "Default" + + + Set StimDisp9_SlideText = New SlideText + StimDisp9_SlideText.Name = "Text1" + + InitSlideTextDefaults StimDisp9_SlideText + + StimDisp9_SlideText.Text = "+" + StimDisp9_SlideText.Width = "10%" + StimDisp9_SlideText.Height = "10%" + StimDisp9_SlideText.ForeColor = CColor("silver") + StimDisp9_SlideText.BackStyle = "transparent" + StimDisp9.States.Item("Default").Objects.Add StimDisp9_SlideText, "Text1" + + Set StimDisp9_SlideImage = New SlideImage + StimDisp9_SlideImage.Name = "Image1" + + InitSlideImageDefaults StimDisp9_SlideImage + + StimDisp9_SlideImage.Stretch = CLogical("Yes") + StimDisp9_SlideImage.Width = "204" + StimDisp9_SlideImage.Height = "256" + StimDisp9.States.Item("Default").Objects.Add StimDisp9_SlideImage, "Image1" + StimDisp9.ActiveState = "Default" + + Set InitProc = New Procedure + InitProc.Name = "InitProc" + InitProc.Tag = "" + InitProc.Subroutine = "InitProc_Run" + + Set StimDisp10 = New Slide + StimDisp10.Name = "StimDisp10" + StimDisp10.Tag = "st17" + StimDisp10.TimingMode = ebTimingModeEvent + StimDisp10.PreRelease = Val("40") + + StimDisp10.OnsetSync = 1 + StimDisp10.OffsetSync = 0 + + Set StimDisp10_State = New SlideState + StimDisp10_State.Name = "Default" + + InitSlideStateDefaults StimDisp10_State + + StimDisp10_State.BackColor = CColor("black") + + StimDisp10.States.Add StimDisp10_State, "Default" + + + Set StimDisp10_SlideText = New SlideText + StimDisp10_SlideText.Name = "Text1" + + InitSlideTextDefaults StimDisp10_SlideText + + StimDisp10_SlideText.Text = "+" + StimDisp10_SlideText.Width = "10%" + StimDisp10_SlideText.Height = "10%" + StimDisp10_SlideText.ForeColor = CColor("silver") + StimDisp10_SlideText.BackStyle = "transparent" + StimDisp10.States.Item("Default").Objects.Add StimDisp10_SlideText, "Text1" + + Set StimDisp10_SlideImage = New SlideImage + StimDisp10_SlideImage.Name = "Image1" + + InitSlideImageDefaults StimDisp10_SlideImage + + StimDisp10_SlideImage.Stretch = CLogical("Yes") + StimDisp10_SlideImage.Width = "204" + StimDisp10_SlideImage.Height = "256" + StimDisp10.States.Item("Default").Objects.Add StimDisp10_SlideImage, "Image1" + StimDisp10.ActiveState = "Default" + + Set StimDisp11 = New Slide + StimDisp11.Name = "StimDisp11" + StimDisp11.Tag = "st18" + StimDisp11.TimingMode = ebTimingModeEvent + StimDisp11.PreRelease = Val("40") + + StimDisp11.OnsetSync = 1 + StimDisp11.OffsetSync = 0 + + Set StimDisp11_State = New SlideState + StimDisp11_State.Name = "Default" + + InitSlideStateDefaults StimDisp11_State + + StimDisp11_State.BackColor = CColor("black") + + StimDisp11.States.Add StimDisp11_State, "Default" + + + Set StimDisp11_SlideText = New SlideText + StimDisp11_SlideText.Name = "Text1" + + InitSlideTextDefaults StimDisp11_SlideText + + StimDisp11_SlideText.Text = "+" + StimDisp11_SlideText.Width = "10%" + StimDisp11_SlideText.Height = "10%" + StimDisp11_SlideText.ForeColor = CColor("silver") + StimDisp11_SlideText.BackStyle = "transparent" + StimDisp11.States.Item("Default").Objects.Add StimDisp11_SlideText, "Text1" + + Set StimDisp11_SlideImage = New SlideImage + StimDisp11_SlideImage.Name = "Image1" + + InitSlideImageDefaults StimDisp11_SlideImage + + StimDisp11_SlideImage.Stretch = CLogical("Yes") + StimDisp11_SlideImage.Width = "204" + StimDisp11_SlideImage.Height = "256" + StimDisp11.States.Item("Default").Objects.Add StimDisp11_SlideImage, "Image1" + StimDisp11.ActiveState = "Default" + + Set StimDisp12 = New Slide + StimDisp12.Name = "StimDisp12" + StimDisp12.Tag = "st19" + StimDisp12.TimingMode = ebTimingModeEvent + StimDisp12.PreRelease = Val("40") + + StimDisp12.OnsetSync = 1 + StimDisp12.OffsetSync = 0 + + Set StimDisp12_State = New SlideState + StimDisp12_State.Name = "Default" + + InitSlideStateDefaults StimDisp12_State + + StimDisp12_State.BackColor = CColor("black") + + StimDisp12.States.Add StimDisp12_State, "Default" + + + Set StimDisp12_SlideText = New SlideText + StimDisp12_SlideText.Name = "Text1" + + InitSlideTextDefaults StimDisp12_SlideText + + StimDisp12_SlideText.Text = "+" + StimDisp12_SlideText.Width = "10%" + StimDisp12_SlideText.Height = "10%" + StimDisp12_SlideText.ForeColor = CColor("silver") + StimDisp12_SlideText.BackStyle = "transparent" + StimDisp12.States.Item("Default").Objects.Add StimDisp12_SlideText, "Text1" + + Set StimDisp12_SlideImage = New SlideImage + StimDisp12_SlideImage.Name = "Image1" + + InitSlideImageDefaults StimDisp12_SlideImage + + StimDisp12_SlideImage.Stretch = CLogical("Yes") + StimDisp12_SlideImage.Width = "204" + StimDisp12_SlideImage.Height = "256" + StimDisp12.States.Item("Default").Objects.Add StimDisp12_SlideImage, "Image1" + StimDisp12.ActiveState = "Default" + + Set CellList = New List + CellList.Name = "CellList" + CellList.Tag = "" + + 'Initialization for CellList + + Set CellList.Order = New SequentialOrder + Set CellList.Deletion = NoDeletion + CellList.ResetEveryRun = False + + ' Create the column headings + CellList.AddAttrib "CellNumber" + CellList.AddAttrib "CellLabel" + CellList.Filename = "" + CellList.LoadMethod = ebLoadMethodEmbedded + + CellList.Load + + Set CellList.TerminateCondition = Cycles(1) + Set CellList.ResetCondition = Samples(5) + CellList.Reset + + Set StartProc = New Procedure + StartProc.Name = "StartProc" + StartProc.Tag = "" + StartProc.Subroutine = "StartProc_Run" + + Set EndProc = New Procedure + EndProc.Name = "EndProc" + EndProc.Tag = "" + EndProc.Subroutine = "EndProc_Run" + + Set StartText = New TextDisplay + StartText.Name = "StartText" + StartText.Tag = "" + + Set StartTextEchoClients = New EchoClientCollection + + InitTextDisplayDefaults StartText + + StartText.Text = "The task will begin shortly." + StartText.BackColor = CColor("black") + StartText.ForeColor = CColor("silver") + StartText.Duration = CLng("-1") + StartText.TimingMode = ebTimingModeEvent + StartText.PreRelease = Val("0") + + StartText.OnsetSync = 1 + StartText.OffsetSync = 0 + + Set EndText = New TextDisplay + EndText.Name = "EndText" + EndText.Tag = "" + + Set EndTextEchoClients = New EchoClientCollection + + InitTextDisplayDefaults EndText + + EndText.Text = "Block complete." + EndText.BackColor = CColor("black") + EndText.ForeColor = CColor("silver") + EndText.Duration = CLng("-1") + EndText.TimingMode = ebTimingModeEvent + EndText.PreRelease = Val("0") + + EndText.OnsetSync = 1 + EndText.OffsetSync = 0 + + Set StartSlide = New Slide + StartSlide.Name = "StartSlide" + StartSlide.Tag = "st01" + StartSlide.Duration = CLng("5000") + StartSlide.TimingMode = ebTimingModeEvent + StartSlide.PreRelease = Val("40") + + StartSlide.OnsetSync = 1 + StartSlide.OffsetSync = 0 + + Set StartSlide_State = New SlideState + StartSlide_State.Name = "Default" + + InitSlideStateDefaults StartSlide_State + + StartSlide_State.BackColor = CColor("black") + + StartSlide.States.Add StartSlide_State, "Default" + + + Set StartSlide_SlideText = New SlideText + StartSlide_SlideText.Name = "Text1" + + InitSlideTextDefaults StartSlide_SlideText + + StartSlide_SlideText.Text = "+" + StartSlide_SlideText.Width = "10%" + StartSlide_SlideText.Height = "10%" + StartSlide_SlideText.ForeColor = CColor("silver") + StartSlide_SlideText.BackColor = CColor("silver") + StartSlide_SlideText.BackStyle = "transparent" + StartSlide.States.Item("Default").Objects.Add StartSlide_SlideText, "Text1" + + Set StartSlide_SlideImage = New SlideImage + StartSlide_SlideImage.Name = "Image1" + StartSlide_SlideImage.Filename = "Stim\\C1O1_F8.bmp" + StartSlide_SlideImage.Load + + InitSlideImageDefaults StartSlide_SlideImage + + StartSlide_SlideImage.Stretch = CLogical("Yes") + StartSlide_SlideImage.Width = "204" + StartSlide_SlideImage.Height = "256" + StartSlide.States.Item("Default").Objects.Add StartSlide_SlideImage, "Image1" + StartSlide.ActiveState = "Default" + + Set EndSlide = New Slide + EndSlide.Name = "EndSlide" + EndSlide.Tag = "st01" + EndSlide.Duration = CLng("5000") + EndSlide.TimingMode = ebTimingModeEvent + EndSlide.PreRelease = Val("40") + + EndSlide.OnsetSync = 1 + EndSlide.OffsetSync = 0 + + Set EndSlide_State = New SlideState + EndSlide_State.Name = "Default" + + InitSlideStateDefaults EndSlide_State + + EndSlide_State.BackColor = CColor("black") + + EndSlide.States.Add EndSlide_State, "Default" + + + Set EndSlide_SlideText = New SlideText + EndSlide_SlideText.Name = "Text1" + + InitSlideTextDefaults EndSlide_SlideText + + EndSlide_SlideText.Text = "+" + EndSlide_SlideText.Width = "10%" + EndSlide_SlideText.Height = "10%" + EndSlide_SlideText.ForeColor = CColor("silver") + EndSlide_SlideText.BackColor = CColor("silver") + EndSlide_SlideText.BackStyle = "transparent" + EndSlide.States.Item("Default").Objects.Add EndSlide_SlideText, "Text1" + + Set EndSlide_SlideImage = New SlideImage + EndSlide_SlideImage.Name = "Image1" + EndSlide_SlideImage.Filename = "Stim\\C1O1_F8.bmp" + EndSlide_SlideImage.Load + + InitSlideImageDefaults EndSlide_SlideImage + + EndSlide_SlideImage.Stretch = CLogical("Yes") + EndSlide_SlideImage.Width = "204" + EndSlide_SlideImage.Height = "256" + EndSlide.States.Item("Default").Objects.Add EndSlide_SlideImage, "Image1" + EndSlide.ActiveState = "Default" + + +End Sub + + +'-------------------------------------------------------------------------- +' InitPackages +' +'-------------------------------------------------------------------------- +Sub InitPackages(c As Context) + +End Sub + + + +'-------------------------------------------------------------------------- +'-------------------------------------------------------------------------- +' UnInitDevices +'-------------------------------------------------------------------------- +Sub UnInitDevices() + Display.Close + Set Display = Nothing + + Keyboard.Close + Set Keyboard = Nothing + + Mouse.Close + Set Mouse = Nothing + + + SRBOX.Close + Set SRBOX = Nothing + +End Sub + + +'-------------------------------------------------------------------------- +'-------------------------------------------------------------------------- +' UnInitPackages +'-------------------------------------------------------------------------- +Sub UnInitPackages() +End Sub + + +'-------------------------------------------------------------------------- +' UnInitObjects +' +'-------------------------------------------------------------------------- +Sub UnInitObjects() + + Set SessionProc = Nothing + + Set MainList = Nothing + + Set TestList = Nothing + + Set TrialProc = Nothing + + Set StimDisp1 = Nothing + + Set StimDisp2 = Nothing + + Set StimDisp3 = Nothing + + Set StimDisp4 = Nothing + + Set StimDisp5 = Nothing + + Set StimDisp6 = Nothing + + Set StimDisp7 = Nothing + + Set StimDisp8 = Nothing + + Set StimDisp9 = Nothing + + Set InitProc = Nothing + + Set StimDisp10 = Nothing + + Set StimDisp11 = Nothing + + Set StimDisp12 = Nothing + + Set CellList = Nothing + + Set StartProc = Nothing + + Set EndProc = Nothing + + Set StartText = Nothing + + Set StartTextEchoClients = Nothing + + Set EndText = Nothing + + Set EndTextEchoClients = Nothing + + Set StartSlide = Nothing + + Set EndSlide = Nothing + + +End Sub + + +'-------------------------------------------------------------------------- +' Main +' +'-------------------------------------------------------------------------- +Sub Main() + + CreateDefaultPort + + ' Create and initialize the default context, data file, + ' and provide global access to the context. + Dim c As Context + Set c = New Context + Set c.DataFile = New DataFile + c.PushNewFrame + Set ebContext = c + + ' Set the log level names + c.SetLogLevelName 1, "Session" + c.SetLogLevelName 2, "Block" + c.SetLogLevelName 3, "Trial" + c.SetLogLevelName 4, "SubTrial" + c.SetLogLevelName 5, "LogLevel5" + c.SetLogLevelName 6, "LogLevel6" + c.SetLogLevelName 7, "LogLevel7" + c.SetLogLevelName 8, "LogLevel8" + c.SetLogLevelName 9, "LogLevel9" + c.SetLogLevelName 10, "LogLevel10" + + ' Set standard logging items + ebContext.SetAttrib "Experiment", "ICTrn_FHC_SpFq_250Blk" + ebContext.SetAttrib "SessionDate", Date$ + ebContext.SetAttrib "SessionTime", Time$ + ebContext.SetAttrib "RandomSeed", PRNG.GetSeed() + + If Basic.OS = ebWin32 Then + WinActivate "E-Run Experiment Window" + End If + + ' Get the StartupInfo + ' Set default for GroupNumber + c.SetAttrib "Group", "1" + + + ' Set the defaults for all of the StartupInfo + c.SetAttrib "Subject", "1" + c.SetAttrib "Session", "1" + c.SetAttrib "Group", "1" + c.SetAttrib "Name", "" + c.SetAttrib "Age", "0" + c.SetAttrib "Sex", "male" + c.SetAttrib "Handedness", "left" + c.SetAttrib "ResearcherID", "1" + + Dim vAnswer As Variant +StartupInfo_Begin: + +StartupInfoPrompt_Subject: + vAnswer = AskBox("Please enter the Subject Number (1-32767, 0=No Data Logging):", c.GetAttrib("Subject")) + If Not IsEmpty(vAnswer) then + If Not IsNumeric(vAnswer) then + MsgBox "Please enter an integer value" + GoTo StartupInfoPrompt_Subject + ElseIf CLng(vAnswer) < 0 Then + MsgBox "The value for Subject must not be less than 0" + GoTo StartupInfoPrompt_Subject + ElseIf CLng(vAnswer) > 32767 Then + MsgBox "The value for Subject must be not be greater than 32767" + GoTo StartupInfoPrompt_Subject + End If + Else + GoTo ExperimentAbort + End if + + c.SetAttrib "Subject", CStr(vAnswer) + +StartupInfoPrompt_Session: + vAnswer = AskBox("Please enter the Session Number (1-32767):", c.GetAttrib("Session")) + If Not IsEmpty(vAnswer) then + If Not IsNumeric(vAnswer) then + MsgBox "Please enter an integer value" + GoTo StartupInfoPrompt_Session + ElseIf CLng(vAnswer) < 1 Then + MsgBox "The value for Session must not be less than 1" + GoTo StartupInfoPrompt_Session + ElseIf CLng(vAnswer) > 32767 Then + MsgBox "The value for Session must be not be greater than 32767" + GoTo StartupInfoPrompt_Session + End If + Else + GoTo ExperimentAbort + End if + + c.SetAttrib "Session", CStr(vAnswer) + +StartupInfoPrompt_Group: + vAnswer = AskBox("Please enter Subject's Group (0-32767):", c.GetAttrib("Group")) + If Not IsEmpty(vAnswer) then + If Not IsNumeric(vAnswer) then + MsgBox "Please enter an integer value" + GoTo StartupInfoPrompt_Group + ElseIf CLng(vAnswer) < 0 Then + MsgBox "The value for Group must not be less than 0" + GoTo StartupInfoPrompt_Group + ElseIf CLng(vAnswer) > 32767 Then + MsgBox "The value for Group must be not be greater than 32767" + GoTo StartupInfoPrompt_Group + End If + Else + GoTo ExperimentAbort + End if + + c.SetAttrib "Group", CStr(vAnswer) + +StartupInfoPrompt_Name: + vAnswer = AskBox("Please enter Subject's Name:", c.GetAttrib("Name")) + If Not IsEmpty(vAnswer) then + If Len(CStr(vAnswer)) > 255 Then + MsgBox "The length for Name must be not be greater than 255" + GoTo StartupInfoPrompt_Name + End If + Else + GoTo ExperimentAbort + End if + + c.SetAttrib "Name", CStr(vAnswer) + +StartupInfoPrompt_Age: + vAnswer = AskBox("Please enter Subject's Age (0-150):", c.GetAttrib("Age")) + If Not IsEmpty(vAnswer) then + If Not IsNumeric(vAnswer) then + MsgBox "Please enter an integer value" + GoTo StartupInfoPrompt_Age + ElseIf CLng(vAnswer) < 0 Then + MsgBox "The value for Age must not be less than 0" + GoTo StartupInfoPrompt_Age + ElseIf CLng(vAnswer) > 150 Then + MsgBox "The value for Age must be not be greater than 150" + GoTo StartupInfoPrompt_Age + End If + Else + GoTo ExperimentAbort + End if + + c.SetAttrib "Age", CStr(vAnswer) + +StartupInfoPrompt_Sex: +Dim arrayChoicesSex(2) + arrayChoicesSex(0) = "male" + arrayChoicesSex(1) = "female" + vAnswer = SelectBox(, "Please enter Subject's Sex:", arrayChoicesSex) + If vAnswer >= 0 then + c.SetAttrib "Sex", arrayChoicesSex(vAnswer) + Else + GoTo ExperimentAbort + End If + +StartupInfoPrompt_Handedness: +Dim arrayChoicesHandedness(2) + arrayChoicesHandedness(0) = "left" + arrayChoicesHandedness(1) = "right" + vAnswer = SelectBox(, "Enter Subject's Handedness:", arrayChoicesHandedness) + If vAnswer >= 0 then + c.SetAttrib "Handedness", arrayChoicesHandedness(vAnswer) + Else + GoTo ExperimentAbort + End If + +StartupInfoPrompt_ResearcherID: + vAnswer = AskBox("Please enter Researcher's ID:", c.GetAttrib("ResearcherID")) + If Not IsEmpty(vAnswer) then + If Not IsNumeric(vAnswer) then + MsgBox "Please enter an integer value" + GoTo StartupInfoPrompt_ResearcherID + ElseIf CLng(vAnswer) < 0 Then + MsgBox "The value for ResearcherID must not be less than 0" + GoTo StartupInfoPrompt_ResearcherID + ElseIf CLng(vAnswer) > 32767 Then + MsgBox "The value for ResearcherID must be not be greater than 32767" + GoTo StartupInfoPrompt_ResearcherID + End If + Else + GoTo ExperimentAbort + End if + + c.SetAttrib "ResearcherID", CStr(vAnswer) + + ' Display the summary + Dim strSummary As String + strSummary = "Subject: " & c.GetAttrib("Subject") & "\n" + strSummary = strSummary & "Session: " & c.GetAttrib("Session") & "\n" + strSummary = strSummary & "Group: " & c.GetAttrib("Group") & "\n" + strSummary = strSummary & "Name: " & c.GetAttrib("Name") & "\n" + strSummary = strSummary & "Age: " & c.GetAttrib("Age") & "\n" + strSummary = strSummary & "Sex: " & c.GetAttrib("Sex") & "\n" + strSummary = strSummary & "Handedness: " & c.GetAttrib("Handedness") & "\n" + strSummary = strSummary & "ResearcherID: " & c.GetAttrib("ResearcherID") & "\n" + strSummary = strSummary & "\nContinue with the above startup info?" + + Dim nSummaryAnswer As Integer + nSummaryAnswer = MsgBox(strSummary, ebYesNoCancel + ebQuestion, "Summary of Startup Info") + If nSummaryAnswer = ebNo Then + GoTo StartupInfo_Begin + ElseIf nSummaryAnswer = ebCancel Then + GoTo ExperimentAbort + End If + + + ' Set the default Data Filename + c.DataFile.Filename = CStr(c.GetAttrib("Experiment")) & "-" & CStr(c.GetAttrib("Subject")) & "-" & CStr(c.GetAttrib("Session")) & ".txt" + + ' If we are logging data, then prompt to overwrite the data file if it exists + If CLng(c.GetAttrib("Subject")) <> 0 Then + Dim strFilenameEDAT As String + strFilenameEDAT = CStr(c.GetAttrib("Experiment")) & "-" & CStr(c.GetAttrib("Subject")) & "-" & CStr(c.GetAttrib("Session")) & ".edat" + If FileExists(c.DataFile.Filename) Or FileExists(strFilenameEDAT) Then + If ebYes <> MsgBox("WARNING: The data file and/or recovery file already exists:\nFILE: " & c.DataFile.Filename & "\n\nDo you want to overwrite?", ebYesNo + ebQuestion) Then + GoTo ExperimentAbort + End If + End If + End If + + ' Initialize all system devices, packages, and objects + InitDevices c + InitPackages c + InitObjects c + + ' If we are logging data, then open the datafile + If CLng(c.GetAttrib("Subject")) <> 0 Then + c.DataFile.Open + c.LogHeader + End If + + + +#If RUNTIME_VERSION_MAJOR > 1 Or (RUNTIME_VERSION_MAJOR = 1 And RUNTIME_VERSION_MINOR >= 2) Then + ' Log clock timing information + c.SetAttrib "Clock.Information", Clock.Information +#End If + + ' Start the running of the Experiment + SessionProc.Run c + + ' Clean up the context and close the datafile + If CLng(c.GetAttrib("Subject")) <> 0 Then + c.DataFile.Close + ' Attempt to convert the recovery file into a data file + Dim nConvert As Long + nConvert = c.DataFile.Convert(ebProgressSimple) + If nConvert = 0 Then + ' Settings in E-Studio are set to not remove E-Recovery file + Else + ' The datafile failed to convert! + MsgBox "ERROR: The datafile did not convert!\nFILE: " & c.DataFile.Filename & "\n\nIt is recommended that you recover your data with the E-Recovery utility" + MsgBox c.DataFile.GetLastErrorMessage() + End If + End If +ExperimentFinish: + + UnInitObjects + + UnInitPackages + UnInitDevices + +ExperimentAbort: + + ' Clean up the context + c.PopFrame + Set c = Nothing + Set ebContext = Nothing + + DestroyDefaultPort + +End Sub + + +[DataSection_MainList(1)] +Weight Nested Procedure +1 InitProc +1 StartProc +250 TestList TrialProc +1 EndProc\0 + +[DataSection_TestList(1)] +Weight Nested Procedure CellNumber +1 1 +1 2 +1 3 +1 4 +1 5\0 + +[DataSection_CellList(1)] +Weight Nested Procedure CellNumber CellLabel +1 1 Face_up +1 2 Face_down +1 3 House_up +1 4 House_down +1 5 Check_up\0 \ No newline at end of file diff --git a/eeg_face13/sourcedata/tasks/ICTrn_FHC_SpFq_250Blk.es b/eeg_face13/sourcedata/tasks/ICTrn_FHC_SpFq_250Blk.es new file mode 100644 index 000000000..29382d29e --- /dev/null +++ b/eeg_face13/sourcedata/tasks/ICTrn_FHC_SpFq_250Blk.es @@ -0,0 +1,2147 @@ +[WARNING] +WARNING: DO NOT EDIT THIS FILE DIRECTLY! +Use only E-Studio to edit this file. +Editing of this file from any other means is not supported +and may corrupt the experiment design specification. +Technical support will not be able to address any issue in +regards to this file format. + +[Experiment] +VersionMajor=1 +VersionMinor=2 +VersionInternal=1 +VersionBuild=33 +_VersionPersist=6 +Root="SessionProc" +Author=" (Brock University)" +Abstract="" +Notes="" +Creation=1251382517 + +[Device0] +_VersionPersist=1 +OpenDevice=1 +Width=1024 +Height=768 +Bpp=16 +Name="Display" +Class="Display" + +[Device1] +_VersionPersist=1 +OpenDevice=0 +Channels=2 +Samples=22050 +Bps=16 +Name="Sound" +Class="Sound" + +[Device2] +_VersionPersist=1 +OpenDevice=1 +CollectionMode=1 +EmulateDeviceName="" +CapsLock=0 +NumLock=1 +Name="Keyboard" +Class="Keyboard" + +[Device3] +_VersionPersist=1 +OpenDevice=1 +CollectionMode=1 +EmulateDeviceName="" +OpenMode=0 +ShowCursor=0 +Name="Mouse" +Class="Mouse" + +[Device4] +_VersionPersist=1 +OpenDevice=1 +CollectionMode=1 +EmulateDeviceName="" +Port=1 +BaudRate=19200 +Cps=800 +Configuration=0 +Name="SRBOX" +Class="SRBOX" + +[DataFile] +WarnBeforeOverwrite=1 +WarnIfConvertFailed=1 +RemoveERecoveryFile=0 +ReceiveFeedbackDuringDataFileConversion=1 +LogLevel(2).Name="Block" +LogLevel(3).Name="Trial" +LogLevel(4).Name="SubTrial" +LogLevel(5).Name="LogLevel5" +LogLevel(6).Name="LogLevel6" +LogLevel(7).Name="LogLevel7" +LogLevel(8).Name="LogLevel8" +LogLevel(9).Name="LogLevel9" +LogLevel(10).Name="LogLevel10" + +[StartupInfo] +DisplaySummary=1 +UseDefaults=0 + +[StartupInfo1] +Name="Subject" +Prompt="Please enter the Subject Number (1-32767, 0=No Data Logging):" +DataType=0 +Enabled=1 +PromptEnabled=1 +Default="1" +Min=0 +Max=32767 +PrivateFlags=268435477 +PrivateInfo=1 + +[StartupInfo2] +Name="Session" +Prompt="Please enter the Session Number (1-32767):" +DataType=0 +Enabled=1 +PromptEnabled=1 +Default="1" +Min=1 +Max=32767 +PrivateFlags=268435477 +PrivateInfo=1 + +[StartupInfo3] +Name="Group" +Prompt="Please enter Subject's Group (0-32767):" +DataType=0 +Enabled=1 +PromptEnabled=1 +Default="1" +Min=0 +Max=32767 + +[StartupInfo4] +Name="Name" +Prompt="Please enter Subject's Name:" +DataType=1 +Enabled=1 +PromptEnabled=1 +Default="" +MaxLength=255 + +[StartupInfo5] +Name="Age" +Prompt="Please enter Subject's Age (0-150):" +DataType=0 +Enabled=1 +PromptEnabled=1 +Default="0" +Min=0 +Max=150 + +[StartupInfo6] +Name="Sex" +Prompt="Please enter Subject's Sex:" +DataType=2 +Enabled=1 +PromptEnabled=1 +Default="male" +Choice(1).Value="male" +Choice(2).Value="female" + +[StartupInfo7] +Name="Handedness" +Prompt="Enter Subject's Handedness:" +DataType=2 +Enabled=1 +PromptEnabled=1 +Default="left" +Choice(1).Value="left" +Choice(2).Value="right" + +[StartupInfo8] +Name="ResearcherID" +Prompt="Please enter Researcher's ID:" +DataType=0 +Enabled=1 +PromptEnabled=1 +Default="1" +Min=0 +Max=32767 + +[Object0] +_Version=65536 +_ExtentX=2646 +_ExtentY=1323 +_StockProps=0 +VersionMajor=1 +VersionMinor=2 +VersionInternal=1 +VersionBuild=21 +_ItemList="MainList" +_VersionPersist=1 +FlowLines(0).Count=1 +FlowLines(0).FlowItem(0).Name="MainList" +LogData=1 +Name="SessionProc" +TypeName="Procedure" +Tag="" +Notes="" + +[Object1] +_Version=65536 +_ExtentX=2646 +_ExtentY=1323 +_StockProps=0 +VersionMajor=1 +VersionMinor=2 +VersionInternal=1 +VersionBuild=21 +_ItemList="TestList;InitProc;StartProc;TrialProc;EndProc" +_VersionPersist=1 +VersionMajor=1 +VersionMinor=2 +VersionInternal=1 +VersionBuild=21 +Order=0 +OrderBy=0 +ResetEveryRun=0 +HideLevelsWithZeroWeight=0 +CycleDef=0 +CycleValueSamples=0 +CycleValueCondition="" +ExitDef=0 +ExitValueCycles=1 +ExitValueSamples=1 +ExitValueSeconds=0 +ExitValueCondition="" +LoadMethod=0 +Filename="" +UserAttributes=0 +Attributes("Weight").DefaultValue="1" +Attributes("Weight").Visible=1 +Attributes("Weight").Width=75 +Attributes("Weight").OrderIndex=0 +Attributes("Nested").DefaultValue="" +Attributes("Nested").Visible=1 +Attributes("Nested").Width=75 +Attributes("Nested").OrderIndex=1 +Attributes("Procedure").DefaultValue="trn_N170_Proc" +Attributes("Procedure").Visible=1 +Attributes("Procedure").Width=161 +Attributes("Procedure").OrderIndex=2 +Levels=4 +Levels(1).ValueString="1\t\tInitProc\t" +Levels(2).ValueString="1\t\tStartProc\t" +Levels(3).ValueString="250\tTestList\tTrialProc\t" +Levels(4).ValueString="1\t\tEndProc\t" +Name="MainList" +TypeName="List" +Tag="" +Notes="" + +[Object2] +_Version=65536 +_ExtentX=2646 +_ExtentY=1323 +_StockProps=0 +VersionMajor=1 +VersionMinor=2 +VersionInternal=1 +VersionBuild=21 +Logging("CellNumber").Log=-1 +_VersionPersist=1 +VersionMajor=1 +VersionMinor=2 +VersionInternal=1 +VersionBuild=21 +Order=1 +OrderBy=0 +ResetEveryRun=0 +HideLevelsWithZeroWeight=0 +CycleDef=0 +CycleValueSamples=0 +CycleValueCondition="" +ExitDef=0 +ExitValueCycles=1 +ExitValueSamples=1 +ExitValueSeconds=0 +ExitValueCondition="" +LoadMethod=0 +Filename="" +UserAttributes=1 +Attributes("Weight").DefaultValue="1" +Attributes("Weight").Visible=1 +Attributes("Weight").Width=75 +Attributes("Weight").OrderIndex=0 +Attributes("Nested").DefaultValue="" +Attributes("Nested").Visible=1 +Attributes("Nested").Width=75 +Attributes("Nested").OrderIndex=1 +Attributes("Procedure").DefaultValue="TrnN170TrialProc" +Attributes("Procedure").Visible=1 +Attributes("Procedure").Width=75 +Attributes("Procedure").OrderIndex=2 +Attributes(1).Name="CellNumber" +Attributes(1).DefaultValue="1" +Attributes(1).VarType=8 +Attributes(1).VarTypeInternal=8 +Attributes(1).Visible=1 +Attributes(1).Width=75 +Levels=5 +Levels(1).ValueString="1\t\t\t1\t" +Levels(2).ValueString="1\t\t\t2\t" +Levels(3).ValueString="1\t\t\t3\t" +Levels(4).ValueString="1\t\t\t4\t" +Levels(5).ValueString="1\t\t\t5\t" +Name="TestList" +TypeName="List" +Tag="" +Notes="" + +[Object3] +_Version=65536 +_ExtentX=2646 +_ExtentY=1323 +_StockProps=0 +VersionMajor=1 +VersionMinor=2 +VersionInternal=1 +VersionBuild=21 +_ItemList="TrnN170Init;StimDisp1;StimDisp2;StimDisp3;StimDisp4;StimDisp5;StimDisp6;StimDisp7;StimDisp8;StimDisp9;StimDisp10;StimDisp11;StimDisp12" +_VersionPersist=1 +FlowLines(0).Count=13 +FlowLines(0).FlowItem(0).Name="TrnN170Init" +FlowLines(0).FlowItem(1).Name="StimDisp1" +FlowLines(0).FlowItem(2).Name="StimDisp2" +FlowLines(0).FlowItem(3).Name="StimDisp3" +FlowLines(0).FlowItem(4).Name="StimDisp4" +FlowLines(0).FlowItem(5).Name="StimDisp5" +FlowLines(0).FlowItem(6).Name="StimDisp6" +FlowLines(0).FlowItem(7).Name="StimDisp7" +FlowLines(0).FlowItem(8).Name="StimDisp8" +FlowLines(0).FlowItem(9).Name="StimDisp9" +FlowLines(0).FlowItem(10).Name="StimDisp10" +FlowLines(0).FlowItem(11).Name="StimDisp11" +FlowLines(0).FlowItem(12).Name="StimDisp12" +LogData=1 +Name="TrialProc" +TypeName="Procedure" +Tag="" +Notes="" + +[Object4] +_Version=131072 +_ExtentX=2646 +_ExtentY=1323 +_StockProps=0 +VersionMajor=1 +VersionMinor=2 +VersionInternal=1 +VersionBuild=21 +_VersionPersist=1 +Code="c.SetAttrib \"StimSlide_Dur\", random(50,80)\nCellN=c.GetAttrib(\"CellNumber\")\n\n'define category variable\nif CellN=1 or CellN=2 then\n\tCatStr=\"H\"\nend if\nif CellN=3 or CellN=4 then\n\tCatStr=\"F\"\nend if\nif CellN=5 or CellN=6 then\n\tCatStr=\"C\"\nend if\n\n\n'Define ID variable.\nIDVal=random(1,5)\nif CellN>4 then\n\tIDVal=1\nend if\n\n'Define emotion variable.\nEmotVal=random(1,2)\nif EmotVal = 1 then\n\tEmotStr = \"F\"\nelse\n\tEmotStr = \"A\"\nend if\nif CellN<3 or CellN>4 then\n\tEmotStr=\"\"\nend if\n\n'Define inversion variable\nInvVal=1\nif CellN=2 or CellN=4 or CellN=6 then\n\tInvVal=2\nend if\n\n\nc.SetAttrib \"TrnN170FName1\", \"Stim\\\\\" & CatStr & CStr(IDVal) & EmotStr & \"O\" & CStr(InvVal) & \"_F\" & \"8.bmp\"\nc.SetAttrib \"TrnN170FName2\", \"Stim\\\\\" & CatStr & CStr(IDVal) & EmotStr & \"O\" & CStr(InvVal) & \"_F\" & \"7.bmp\"\nc.SetAttrib \"TrnN170FName3\", \"Stim\\\\\" & CatStr & CStr(IDVal) & EmotStr & \"O\" & CStr(InvVal) & \"_F\" & \"6.bmp\"\nc.SetAttrib \"TrnN170FName4\", \"Stim\\\\\" & CatStr & CStr(IDVal) & EmotStr & \"O\" & CStr(InvVal) & \"_F\" & \"5.bmp\"\nc.SetAttrib \"TrnN170FName5\", \"Stim\\\\\" & CatStr & CStr(IDVal) & EmotStr & \"O\" & CStr(InvVal) & \"_F\" & \"4.bmp\"\nc.SetAttrib \"TrnN170FName6\", \"Stim\\\\\" & CatStr & CStr(IDVal) & EmotStr & \"O\" & CStr(InvVal) & \"_F\" & \"3.bmp\"\nc.SetAttrib \"TrnN170FName7\", \"Stim\\\\\" & CatStr & CStr(IDVal) & EmotStr & \"O\" & CStr(InvVal) & \"_F\" & \"2.bmp\"\nc.SetAttrib \"TrnN170FName8\", \"Stim\\\\\" & CatStr & CStr(IDVal) & EmotStr & \"O\" & CStr(InvVal) & \"_F\" & \"1.bmp\"\n\nStimDisp1.OnsetSignalData = CellN*10 + 1\nStimDisp2.OnsetSignalData = CellN*10 + 2\nStimDisp3.OnsetSignalData = CellN*10 + 3\nStimDisp4.OnsetSignalData = CellN*10 + 4\nStimDisp5.OnsetSignalData = CellN*10 + 5\nStimDisp6.OnsetSignalData = CellN*10 + 6\n" +Name="TrnN170Init" +TypeName="InLine" +Tag="" +Notes="" + +[Object5] +_Version=65536 +_ExtentX=2646 +_ExtentY=1323 +_StockProps=0 +VersionMajor=1 +VersionMinor=2 +VersionInternal=1 +VersionBuild=21 +Logging("OnsetDelay").Log=0 +Logging("OnsetTime").Log=0 +Logging("DurationError").Log=0 +Logging("PreRelease").Log=0 +Logging("Duration").Log=0 +Logging("StartTime").Log=0 +Logging("OffsetTime").Log=0 +Logging("FinishTime").Log=0 +Logging("TimingMode").Log=0 +Logging("CustomOnsetTime").Log=0 +Logging("CustomOffsetTime").Log=0 +Logging("ActionDelay").Log=0 +Logging("ActionTime").Log=0 +Logging("TargetOffsetTime").Log=0 +Logging("TargetOnsetTime").Log=0 +Logging("OffsetDelay").Log=0 +Logging("RTTime").Log=0 +Logging("ACC").Log=0 +Logging("RT").Log=0 +Logging("RESP").Log=0 +Logging("CRESP").Log=0 +Logging("Tag").Log=0 +_VersionPersist=2 +ActiveState="Default" +States.Count=1 +States(1).Name="Default" +States("Default").X="center" +States("Default").Y="center" +States("Default").Width="100%" +States("Default").Height="100%" +States("Default").XAlign="center" +States("Default").YAlign="center" +States("Default").BackColor="black" +States("Default").BackStyle="opaque" +States("Default").BorderColor="black" +States("Default").BorderWidth="0" +States("Default").ClearAfter="No" +States("Default").Enabled="Yes" +States("Default").ChildCount=2 +States("Default").Objects(1).Type="Image" +States("Default").Objects(1).Filename="[TrnN170FName2]" +States("Default").Objects(1).Stretch="Yes" +States("Default").Objects(1).MirrorLeftRight="No" +States("Default").Objects(1).MirrorUpDown="No" +States("Default").Objects(1).Width="204" +States("Default").Objects(1).Height="256" +States("Default").Objects(1).X="center" +States("Default").Objects(1).Y="center" +States("Default").Objects(1).AlignHorizontal="center" +States("Default").Objects(1).AlignVertical="center" +States("Default").Objects(1).ForeColor="black" +States("Default").Objects(1).BackColor="white" +States("Default").Objects(1).BackStyle="opaque" +States("Default").Objects(1).BorderWidth="0" +States("Default").Objects(1).BorderColor="black" +States("Default").Objects(1).XAlign="center" +States("Default").Objects(1).YAlign="center" +States("Default").Objects(1).SourceColorKey="black" +States("Default").Objects(1).UseSourceColorKey="No" +States("Default").Objects(1).Name="Image1" +States("Default").Objects(2).Type="Text" +States("Default").Objects(2).Text="+" +States("Default").Objects(2).Width="10%" +States("Default").Objects(2).Height="10%" +States("Default").Objects(2).X="center" +States("Default").Objects(2).Y="center" +States("Default").Objects(2).AlignHorizontal="center" +States("Default").Objects(2).AlignVertical="center" +States("Default").Objects(2).FontName="Courier New" +States("Default").Objects(2).FontSize="18" +States("Default").Objects(2).FontBold="Yes" +States("Default").Objects(2).FontUnderline="No" +States("Default").Objects(2).FontStrikeout="No" +States("Default").Objects(2).FontItalic="No" +States("Default").Objects(2).ForeColor="silver" +States("Default").Objects(2).BackColor="white" +States("Default").Objects(2).BackStyle="transparent" +States("Default").Objects(2).BorderWidth="0" +States("Default").Objects(2).BorderColor="black" +States("Default").Objects(2).XAlign="center" +States("Default").Objects(2).YAlign="center" +States("Default").Objects(2).WordWrap=1 +States("Default").Objects(2).Name="Text1" +Duration="[StimSlide_Dur]" +JumpLabel="" +TimingMode=0 +PreRelease="40" +OnsetSync=1 +OffsetSync=0 +Name="StimDisp1" +TypeName="Slide" +Tag="at02" +Notes="" + +[Object6] +_Version=65536 +_ExtentX=2646 +_ExtentY=1323 +_StockProps=0 +VersionMajor=1 +VersionMinor=2 +VersionInternal=1 +VersionBuild=21 +Logging("OnsetDelay").Log=0 +Logging("OnsetTime").Log=0 +Logging("DurationError").Log=0 +Logging("PreRelease").Log=0 +Logging("Duration").Log=0 +Logging("StartTime").Log=0 +Logging("OffsetTime").Log=0 +Logging("FinishTime").Log=0 +Logging("TimingMode").Log=0 +Logging("CustomOnsetTime").Log=0 +Logging("CustomOffsetTime").Log=0 +Logging("ActionDelay").Log=0 +Logging("ActionTime").Log=0 +Logging("TargetOffsetTime").Log=0 +Logging("TargetOnsetTime").Log=0 +Logging("OffsetDelay").Log=0 +Logging("RTTime").Log=0 +Logging("ACC").Log=0 +Logging("RT").Log=0 +Logging("RESP").Log=0 +Logging("CRESP").Log=0 +Logging("Tag").Log=0 +_VersionPersist=2 +ActiveState="Default" +States.Count=1 +States(1).Name="Default" +States("Default").X="center" +States("Default").Y="center" +States("Default").Width="100%" +States("Default").Height="100%" +States("Default").XAlign="center" +States("Default").YAlign="center" +States("Default").BackColor="black" +States("Default").BackStyle="opaque" +States("Default").BorderColor="black" +States("Default").BorderWidth="0" +States("Default").ClearAfter="No" +States("Default").Enabled="Yes" +States("Default").ChildCount=2 +States("Default").Objects(1).Type="Image" +States("Default").Objects(1).Filename="[TrnN170FName3]" +States("Default").Objects(1).Stretch="Yes" +States("Default").Objects(1).MirrorLeftRight="No" +States("Default").Objects(1).MirrorUpDown="No" +States("Default").Objects(1).Width="204" +States("Default").Objects(1).Height="256" +States("Default").Objects(1).X="center" +States("Default").Objects(1).Y="center" +States("Default").Objects(1).AlignHorizontal="center" +States("Default").Objects(1).AlignVertical="center" +States("Default").Objects(1).ForeColor="black" 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+States("Default").Objects(1).BackStyle="opaque" +States("Default").Objects(1).BorderWidth="0" +States("Default").Objects(1).BorderColor="black" +States("Default").Objects(1).XAlign="center" +States("Default").Objects(1).YAlign="center" +States("Default").Objects(1).SourceColorKey="black" +States("Default").Objects(1).UseSourceColorKey="No" +States("Default").Objects(1).Name="Image1" +States("Default").Objects(2).Type="Text" +States("Default").Objects(2).Text="+" +States("Default").Objects(2).Width="10%" +States("Default").Objects(2).Height="10%" +States("Default").Objects(2).X="center" +States("Default").Objects(2).Y="center" +States("Default").Objects(2).AlignHorizontal="center" +States("Default").Objects(2).AlignVertical="center" +States("Default").Objects(2).FontName="Courier New" +States("Default").Objects(2).FontSize="18" +States("Default").Objects(2).FontBold="Yes" +States("Default").Objects(2).FontUnderline="No" +States("Default").Objects(2).FontStrikeout="No" +States("Default").Objects(2).FontItalic="No" +States("Default").Objects(2).ForeColor="silver" +States("Default").Objects(2).BackColor="silver" +States("Default").Objects(2).BackStyle="transparent" +States("Default").Objects(2).BorderWidth="0" +States("Default").Objects(2).BorderColor="black" +States("Default").Objects(2).XAlign="center" +States("Default").Objects(2).YAlign="center" +States("Default").Objects(2).WordWrap=1 +States("Default").Objects(2).Name="Text1" +Duration="5000" +JumpLabel="" +TimingMode=0 +PreRelease="40" +OnsetSync=1 +OffsetSync=0 +Name="StartSlide" +TypeName="Slide" +Tag="st01" +Notes="" + +[Object25] +_Version=65536 +_ExtentX=2646 +_ExtentY=1323 +_StockProps=0 +VersionMajor=1 +VersionMinor=2 +VersionInternal=1 +VersionBuild=21 +Logging("OnsetDelay").Log=0 +Logging("OnsetTime").Log=0 +Logging("DurationError").Log=0 +Logging("PreRelease").Log=0 +Logging("Duration").Log=0 +Logging("StartTime").Log=0 +Logging("OffsetTime").Log=0 +Logging("FinishTime").Log=0 +Logging("TimingMode").Log=0 +Logging("CustomOnsetTime").Log=0 +Logging("CustomOffsetTime").Log=0 +Logging("ActionDelay").Log=0 +Logging("ActionTime").Log=0 +Logging("TargetOffsetTime").Log=0 +Logging("TargetOnsetTime").Log=0 +Logging("OffsetDelay").Log=0 +Logging("RTTime").Log=0 +Logging("ACC").Log=0 +Logging("RT").Log=0 +Logging("RESP").Log=0 +Logging("CRESP").Log=0 +Logging("Tag").Log=0 +_VersionPersist=2 +ActiveState="Default" +States.Count=1 +States(1).Name="Default" +States("Default").X="center" +States("Default").Y="center" +States("Default").Width="100%" +States("Default").Height="100%" +States("Default").XAlign="center" +States("Default").YAlign="center" +States("Default").BackColor="black" +States("Default").BackStyle="opaque" +States("Default").BorderColor="black" +States("Default").BorderWidth="0" +States("Default").ClearAfter="No" +States("Default").Enabled="Yes" +States("Default").ChildCount=2 +States("Default").Objects(1).Type="Image" +States("Default").Objects(1).Filename="Stim\\C1O1_F8.bmp" +States("Default").Objects(1).Stretch="Yes" +States("Default").Objects(1).MirrorLeftRight="No" +States("Default").Objects(1).MirrorUpDown="No" +States("Default").Objects(1).Width="204" +States("Default").Objects(1).Height="256" +States("Default").Objects(1).X="center" +States("Default").Objects(1).Y="center" +States("Default").Objects(1).AlignHorizontal="center" +States("Default").Objects(1).AlignVertical="center" +States("Default").Objects(1).ForeColor="black" +States("Default").Objects(1).BackColor="white" +States("Default").Objects(1).BackStyle="opaque" +States("Default").Objects(1).BorderWidth="0" +States("Default").Objects(1).BorderColor="black" +States("Default").Objects(1).XAlign="center" +States("Default").Objects(1).YAlign="center" +States("Default").Objects(1).SourceColorKey="black" +States("Default").Objects(1).UseSourceColorKey="No" +States("Default").Objects(1).Name="Image1" +States("Default").Objects(2).Type="Text" +States("Default").Objects(2).Text="+" +States("Default").Objects(2).Width="10%" +States("Default").Objects(2).Height="10%" +States("Default").Objects(2).X="center" +States("Default").Objects(2).Y="center" +States("Default").Objects(2).AlignHorizontal="center" +States("Default").Objects(2).AlignVertical="center" +States("Default").Objects(2).FontName="Courier New" +States("Default").Objects(2).FontSize="18" +States("Default").Objects(2).FontBold="Yes" +States("Default").Objects(2).FontUnderline="No" +States("Default").Objects(2).FontStrikeout="No" +States("Default").Objects(2).FontItalic="No" +States("Default").Objects(2).ForeColor="silver" +States("Default").Objects(2).BackColor="silver" +States("Default").Objects(2).BackStyle="transparent" +States("Default").Objects(2).BorderWidth="0" +States("Default").Objects(2).BorderColor="black" +States("Default").Objects(2).XAlign="center" +States("Default").Objects(2).YAlign="center" +States("Default").Objects(2).WordWrap=1 +States("Default").Objects(2).Name="Text1" +Duration="5000" +JumpLabel="" +TimingMode=0 +PreRelease="40" +OnsetSync=1 +OffsetSync=0 +Name="EndSlide" +TypeName="Slide" +Tag="st01" +Notes="" + +[Object26] +_Version=131072 +_ExtentX=2117 +_ExtentY=1058 +_StockProps=0 +VersionMajor=1 +VersionMinor=2 +VersionInternal=1 +VersionBuild=21 +_VersionPersist=1 +Code="'WritePort &H378, 254" +Name="StartRec" +TypeName="InLine" +Tag="" +Notes="" + +[Object27] +_Version=131072 +_ExtentX=2117 +_ExtentY=1058 +_StockProps=0 +VersionMajor=1 +VersionMinor=2 +VersionInternal=1 +VersionBuild=21 +_VersionPersist=1 +Code="'WritePort &H378, 255" +Name="StopRec" +TypeName="InLine" +Tag="" +Notes="" + +[UserScript] +Dim CellN as integer +Dim CatVal as integer +Dim CatStr as string +Dim IDVal as integer +Dim EmotVal as integer +Dim EmotStr as string +Dim InvVal as integer + diff --git a/eeg_face13/sourcedata/tasks/Stim/Black.bmp b/eeg_face13/sourcedata/tasks/Stim/Black.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/C1.bmp b/eeg_face13/sourcedata/tasks/Stim/C1.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/C1O1_F0.bmp b/eeg_face13/sourcedata/tasks/Stim/C1O1_F0.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/C1O1_F0_L.bmp b/eeg_face13/sourcedata/tasks/Stim/C1O1_F0_L.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/C1O1_F0_R.bmp b/eeg_face13/sourcedata/tasks/Stim/C1O1_F0_R.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/C1O1_F1.bmp b/eeg_face13/sourcedata/tasks/Stim/C1O1_F1.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/C1O1_F2.bmp b/eeg_face13/sourcedata/tasks/Stim/C1O1_F2.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/C1O1_F3.bmp b/eeg_face13/sourcedata/tasks/Stim/C1O1_F3.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/C1O1_F4.bmp b/eeg_face13/sourcedata/tasks/Stim/C1O1_F4.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/C1O1_F5.bmp b/eeg_face13/sourcedata/tasks/Stim/C1O1_F5.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/C1O1_F6.bmp b/eeg_face13/sourcedata/tasks/Stim/C1O1_F6.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/C1O1_F7.bmp b/eeg_face13/sourcedata/tasks/Stim/C1O1_F7.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/C1O1_F8.bmp b/eeg_face13/sourcedata/tasks/Stim/C1O1_F8.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/C1O2_F0.bmp b/eeg_face13/sourcedata/tasks/Stim/C1O2_F0.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/C1O2_F1.bmp b/eeg_face13/sourcedata/tasks/Stim/C1O2_F1.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/C1O2_F2.bmp b/eeg_face13/sourcedata/tasks/Stim/C1O2_F2.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/C1O2_F3.bmp b/eeg_face13/sourcedata/tasks/Stim/C1O2_F3.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/C1O2_F4.bmp b/eeg_face13/sourcedata/tasks/Stim/C1O2_F4.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/C1O2_F5.bmp b/eeg_face13/sourcedata/tasks/Stim/C1O2_F5.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/C1O2_F6.bmp b/eeg_face13/sourcedata/tasks/Stim/C1O2_F6.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/C1O2_F7.bmp b/eeg_face13/sourcedata/tasks/Stim/C1O2_F7.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/C1O2_F8.bmp b/eeg_face13/sourcedata/tasks/Stim/C1O2_F8.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F1A.bmp b/eeg_face13/sourcedata/tasks/Stim/F1A.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F1AO1_F0.bmp b/eeg_face13/sourcedata/tasks/Stim/F1AO1_F0.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F1AO1_F1.bmp b/eeg_face13/sourcedata/tasks/Stim/F1AO1_F1.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F1AO1_F10.bmp b/eeg_face13/sourcedata/tasks/Stim/F1AO1_F10.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F1AO1_F2.bmp b/eeg_face13/sourcedata/tasks/Stim/F1AO1_F2.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F1AO1_F3.bmp b/eeg_face13/sourcedata/tasks/Stim/F1AO1_F3.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F1AO1_F4.bmp b/eeg_face13/sourcedata/tasks/Stim/F1AO1_F4.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F1AO1_F5.bmp b/eeg_face13/sourcedata/tasks/Stim/F1AO1_F5.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F1AO1_F6.bmp b/eeg_face13/sourcedata/tasks/Stim/F1AO1_F6.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F1AO1_F7.bmp b/eeg_face13/sourcedata/tasks/Stim/F1AO1_F7.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F1AO1_F8.bmp b/eeg_face13/sourcedata/tasks/Stim/F1AO1_F8.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F1AO1_F9.bmp b/eeg_face13/sourcedata/tasks/Stim/F1AO1_F9.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F1AO1_SF_L10.bmp b/eeg_face13/sourcedata/tasks/Stim/F1AO1_SF_L10.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F1AO2_F0.bmp b/eeg_face13/sourcedata/tasks/Stim/F1AO2_F0.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F1AO2_F1.bmp b/eeg_face13/sourcedata/tasks/Stim/F1AO2_F1.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F1AO2_F10.bmp b/eeg_face13/sourcedata/tasks/Stim/F1AO2_F10.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F1AO2_F2.bmp b/eeg_face13/sourcedata/tasks/Stim/F1AO2_F2.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F1AO2_F3.bmp b/eeg_face13/sourcedata/tasks/Stim/F1AO2_F3.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F1AO2_F4.bmp b/eeg_face13/sourcedata/tasks/Stim/F1AO2_F4.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F1AO2_F5.bmp b/eeg_face13/sourcedata/tasks/Stim/F1AO2_F5.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F1AO2_F6.bmp b/eeg_face13/sourcedata/tasks/Stim/F1AO2_F6.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F1AO2_F7.bmp b/eeg_face13/sourcedata/tasks/Stim/F1AO2_F7.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F1AO2_F8.bmp b/eeg_face13/sourcedata/tasks/Stim/F1AO2_F8.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F1AO2_F9.bmp b/eeg_face13/sourcedata/tasks/Stim/F1AO2_F9.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F1AO2_SF_L10.bmp b/eeg_face13/sourcedata/tasks/Stim/F1AO2_SF_L10.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F1F.bmp b/eeg_face13/sourcedata/tasks/Stim/F1F.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F1FO1_F0.bmp b/eeg_face13/sourcedata/tasks/Stim/F1FO1_F0.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F1FO1_F1.bmp b/eeg_face13/sourcedata/tasks/Stim/F1FO1_F1.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F1FO1_F10.bmp b/eeg_face13/sourcedata/tasks/Stim/F1FO1_F10.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F1FO1_F2.bmp b/eeg_face13/sourcedata/tasks/Stim/F1FO1_F2.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F1FO1_F3.bmp b/eeg_face13/sourcedata/tasks/Stim/F1FO1_F3.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F1FO1_F4.bmp b/eeg_face13/sourcedata/tasks/Stim/F1FO1_F4.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F1FO1_F5.bmp b/eeg_face13/sourcedata/tasks/Stim/F1FO1_F5.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F1FO1_F6.bmp b/eeg_face13/sourcedata/tasks/Stim/F1FO1_F6.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F1FO1_F7.bmp b/eeg_face13/sourcedata/tasks/Stim/F1FO1_F7.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F1FO1_F8.bmp b/eeg_face13/sourcedata/tasks/Stim/F1FO1_F8.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F1FO1_F9.bmp b/eeg_face13/sourcedata/tasks/Stim/F1FO1_F9.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F1FO1_SF_L10.bmp b/eeg_face13/sourcedata/tasks/Stim/F1FO1_SF_L10.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F1FO2_F0.bmp b/eeg_face13/sourcedata/tasks/Stim/F1FO2_F0.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F1FO2_F1.bmp b/eeg_face13/sourcedata/tasks/Stim/F1FO2_F1.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F1FO2_F10.bmp b/eeg_face13/sourcedata/tasks/Stim/F1FO2_F10.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F1FO2_F2.bmp b/eeg_face13/sourcedata/tasks/Stim/F1FO2_F2.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F1FO2_F3.bmp 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a/eeg_face13/sourcedata/tasks/Stim/F6FO1_F9.bmp b/eeg_face13/sourcedata/tasks/Stim/F6FO1_F9.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F6FO1_SF_L10.bmp b/eeg_face13/sourcedata/tasks/Stim/F6FO1_SF_L10.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F6FO2_F0.bmp b/eeg_face13/sourcedata/tasks/Stim/F6FO2_F0.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F6FO2_F1.bmp b/eeg_face13/sourcedata/tasks/Stim/F6FO2_F1.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F6FO2_F10.bmp b/eeg_face13/sourcedata/tasks/Stim/F6FO2_F10.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F6FO2_F2.bmp b/eeg_face13/sourcedata/tasks/Stim/F6FO2_F2.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/F6FO2_F3.bmp 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index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/FaceMod.asv b/eeg_face13/sourcedata/tasks/Stim/FaceMod.asv new file mode 100644 index 000000000..ebfc7fe3f --- /dev/null +++ b/eeg_face13/sourcedata/tasks/Stim/FaceMod.asv @@ -0,0 +1,29 @@ +function FaceMod(FNameOut) + +[FName, FPath]=uigetfile('*.bmp','Choose a file to modify:'); + +img=imread(FName); +Msk=imread('MaskF.bmp')/255; + +imgMsk=img.*Msk; + +Norm=100/mean(mean(imgMsk)); +imgNorm=(imgMsk+Norm).*Msk; +disp(mean(mean(imgNorm))); +imwrite(imgNorm(:,:),sprintf('%s%s%s%s%s%s',FNameOut,'O','1','_F','0','.bmp'),'BMP'); +imwrite(flipdim(imgNorm(:,:),1),sprintf('%s%s%s%s%s%s',FNameOut,'O','2','_F','0','.bmp'),'BMP'); + +for i=1:10; + + h = fspecial('average', [i*5,i*5]); + imgFiltMsk=imfilter(imgNorm,h).*Msk; + Norm=100-mean(mean(imgFiltMsk)); + imgFiltNorm=(imgFiltMsk+Norm).*Msk; + disp(mean(mean(imgFiltNorm))); + + imwrite(imgFiltNorm,sprintf('%s%s%s%s%s%s',FNameOut,'O','1','_F',num2str(i),'.bmp'),'BMP'); + imwrite(flipdim(imgFiltNorm,1),sprintf('%s%s%s%s%s%s',FNameOut,'O','2','_F',num2str(i),'.bmp'),'BMP'); +end + + + diff --git a/eeg_face13/sourcedata/tasks/Stim/FaceMod.m b/eeg_face13/sourcedata/tasks/Stim/FaceMod.m new file mode 100644 index 000000000..20c2c148e --- /dev/null +++ b/eeg_face13/sourcedata/tasks/Stim/FaceMod.m @@ -0,0 +1,32 @@ +function FaceMod(FNameOut) + +[FName, FPath]=uigetfile('*.bmp','Choose a file to modify:'); + +img=imread(FName); +Msk=imread('MaskF.bmp')/255; +MskI=imread('MaskFi.bmp')/255; + +imgMsk=img.*Msk; +imgMsk=img.*MskI; + +Norm=100/mean(mean(imgMsk)); +imgNorm=imgMsk*Norm; +disp(mean(mean(imgNorm))); +imwrite(imgNorm(:,:),sprintf('%s%s%s%s%s%s',FNameOut,'O','1','_F','0','.bmp'),'BMP'); +imwrite(flipdim(imgNorm(:,:),1),sprintf('%s%s%s%s%s%s',FNameOut,'O','2','_F','0','.bmp'),'BMP'); + +FSize=[1 2 4 8 16 32 64 128]; +for i=1:8; + + h = fspecial('average', [FSize(i),FSize(i)]); + imgFiltMsk=imfilter(imgNorm,h).*Msk; + Norm=100/mean(mean(imgFiltMsk)); + imgFiltNorm=imgFiltMsk*Norm; + disp(mean(mean(imgFiltNorm))); + + imwrite(imgFiltNorm,sprintf('%s%s%s%s%s%s',FNameOut,'O','1','_F',num2str(i),'.bmp'),'BMP'); + imwrite(flipdim(imgFiltNorm,1),sprintf('%s%s%s%s%s%s',FNameOut,'O','2','_F',num2str(i),'.bmp'),'BMP'); +end + + + diff --git a/eeg_face13/sourcedata/tasks/Stim/H1.bmp b/eeg_face13/sourcedata/tasks/Stim/H1.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/H1O1_F0.bmp b/eeg_face13/sourcedata/tasks/Stim/H1O1_F0.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/H1O1_F1.bmp b/eeg_face13/sourcedata/tasks/Stim/H1O1_F1.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/H1O1_F10.bmp b/eeg_face13/sourcedata/tasks/Stim/H1O1_F10.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/H1O1_F2.bmp b/eeg_face13/sourcedata/tasks/Stim/H1O1_F2.bmp new file mode 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b/eeg_face13/sourcedata/tasks/Stim/H5O2_F3.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/H5O2_F4.bmp b/eeg_face13/sourcedata/tasks/Stim/H5O2_F4.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/H5O2_F5.bmp b/eeg_face13/sourcedata/tasks/Stim/H5O2_F5.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/H5O2_F6.bmp b/eeg_face13/sourcedata/tasks/Stim/H5O2_F6.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/H5O2_F7.bmp b/eeg_face13/sourcedata/tasks/Stim/H5O2_F7.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/H5O2_F8.bmp b/eeg_face13/sourcedata/tasks/Stim/H5O2_F8.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/H5O2_F9.bmp b/eeg_face13/sourcedata/tasks/Stim/H5O2_F9.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/MaskBMP.asv b/eeg_face13/sourcedata/tasks/Stim/MaskBMP.asv new file mode 100644 index 000000000..a61b23fd0 --- /dev/null +++ b/eeg_face13/sourcedata/tasks/Stim/MaskBMP.asv @@ -0,0 +1,10 @@ +[FName,FPath]=uigetfile('*.bmp','target bm files','multiselect','on'); + +Mask=imread('MaskFi.bmp')/255; + +for i=1:length(FName); + img=imread(FName{i}); + imgMask=img.*Mask; + imwrite(imgMask,FName{i},'BMP'); +end + \ No newline at end of file diff --git a/eeg_face13/sourcedata/tasks/Stim/MaskBMP.m b/eeg_face13/sourcedata/tasks/Stim/MaskBMP.m new file mode 100644 index 000000000..5be4ca838 --- /dev/null +++ b/eeg_face13/sourcedata/tasks/Stim/MaskBMP.m @@ -0,0 +1,10 @@ +[FName,FPath]=uigetfile('*.bmp','target bmp files','multiselect','on'); + +Mask=imread('MaskFi.bmp')/255; + +for i=1:length(FName); + img=imread(FName{i}); + imgMask=img.*Mask; + imwrite(imgMask,FName{i},'BMP'); +end + \ No newline at end of file diff --git a/eeg_face13/sourcedata/tasks/Stim/MaskF.bmp b/eeg_face13/sourcedata/tasks/Stim/MaskF.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/MaskFi.bmp b/eeg_face13/sourcedata/tasks/Stim/MaskFi.bmp new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sourcedata/tasks/Stim/Thumbs.db b/eeg_face13/sourcedata/tasks/Stim/Thumbs.db new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sub-001/eeg/sub-001_task-faceFO_channels.tsv b/eeg_face13/sub-001/eeg/sub-001_task-faceFO_channels.tsv new file mode 100644 index 000000000..b257ed7ba --- /dev/null +++ b/eeg_face13/sub-001/eeg/sub-001_task-faceFO_channels.tsv @@ -0,0 +1,129 @@ +name type units +A1 eeg microV +A2 eeg microV +A3 eeg microV +A4 eeg microV +A5 eeg microV +A6 eeg microV +A7 eeg microV +A8 eeg microV +A9 eeg microV +A10 eeg microV +A11 eeg microV +A12 eeg microV +A13 eeg microV +A14 eeg microV +A15 eeg microV +A16 eeg microV +A17 eeg microV +A18 eeg microV +A19 eeg microV +A20 eeg microV +A21 eeg microV +A22 eeg microV +A23 eeg microV +A24 eeg microV +A25 eeg microV +A26 eeg microV +A27 eeg microV +A28 eeg microV +A29 eeg microV +A30 eeg microV +A31 eeg microV +A32 eeg microV +B1 eeg microV +B2 eeg microV +B3 eeg microV +B4 eeg microV +B5 eeg microV +B6 eeg microV +B7 eeg microV +B8 eeg microV +B9 eeg microV +B10 eeg microV +B11 eeg microV +B12 eeg microV +B13 eeg microV +B14 eeg microV +B15 eeg microV +B16 eeg microV +B17 eeg microV +B18 eeg microV +B19 eeg microV +B20 eeg microV +B21 eeg microV +B22 eeg microV +B23 eeg microV +B24 eeg microV +B25 eeg microV +B26 eeg microV +B27 eeg microV +B28 eeg microV +B29 eeg microV +B30 eeg microV +B31 eeg microV +B32 eeg microV +C1 eeg microV +C2 eeg microV +C3 eeg microV +C4 eeg microV +C5 eeg microV +C6 eeg microV +C7 eeg microV +C8 eeg microV +C9 eeg microV +C10 eeg microV +C11 eeg microV +C12 eeg microV +C13 eeg microV +C14 eeg microV +C15 eeg microV +C16 eeg microV +C17 eeg microV 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b/eeg_face13/sub-001/eeg/sub-001_task-faceFO_coordsystem.json @@ -0,0 +1 @@ +{"EEGCoordinateSystem":"Other","EEGCoordinateUnits":"metres"} \ No newline at end of file diff --git a/eeg_face13/sub-001/eeg/sub-001_task-faceFO_eeg.edf b/eeg_face13/sub-001/eeg/sub-001_task-faceFO_eeg.edf new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sub-001/eeg/sub-001_task-faceFO_eeg.json b/eeg_face13/sub-001/eeg/sub-001_task-faceFO_eeg.json new file mode 100644 index 000000000..4661817f3 --- /dev/null +++ b/eeg_face13/sub-001/eeg/sub-001_task-faceFO_eeg.json @@ -0,0 +1,25 @@ +{ + "TaskName": "faceFO", + "TaskDescription": "Visual presentation of oval cropped face and house images both upright and inverted. Rare left or right half oval checkerboards were presetned as targets for keypress response.", + "InstitutionName": "Brock University", + "InstitutionAddress": "500 Glenridge Ave, St.Catharines, Ontario", + "EOGChannelCount": 7, + "EMGChannelCount": 0, + "MiscChannelCount": 0, + "TriggerChannelCount": 0, + "PowerLineFrequency": 60, + "EEGPlacementScheme": "Custom equidistant 128 channel BioSemi montage established in coordination with Judith Schedden McMaster Univertisy", + "Manufacturer": "BioSemi", + "ManufacturersModelName": "ActiveTwo", + "HardwareFilters": "n/a", + "SoftwareFilters": "n/a", + "SoftwareVersions": "NI ActiView 532-Lores", + "CapManufacturer": "ElectroCap International", + "CapManufacturersModelName": "10032", + "EEGChannelCount": 128, + "EEGReference": "common", + "RecordingType": "continuous", + "RecordingDuration": 2045, + "SamplingFrequency": 512, + "ECGChannelCount": 0 +} diff --git a/eeg_face13/sub-001/eeg/sub-001_task-faceFO_electrodes.tsv 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n/a 1114142.0000000000 face-inverted diff --git a/eeg_face13/sub-003/eeg/sub-003_task-faceFO_channels.tsv b/eeg_face13/sub-003/eeg/sub-003_task-faceFO_channels.tsv new file mode 100644 index 000000000..9d1164054 --- /dev/null +++ b/eeg_face13/sub-003/eeg/sub-003_task-faceFO_channels.tsv @@ -0,0 +1,129 @@ +name type units +A1 n/a n/a +A2 n/a n/a +A3 n/a n/a +A4 n/a n/a +A5 n/a n/a +A6 n/a n/a +A7 n/a n/a +A8 n/a n/a +A9 n/a n/a +A10 n/a n/a +A11 n/a n/a +A12 n/a n/a +A13 n/a n/a +A14 n/a n/a +A15 n/a n/a +A16 n/a n/a +A17 n/a n/a +A18 n/a n/a +A19 n/a n/a +A20 n/a n/a +A21 n/a n/a +A22 n/a n/a +A23 n/a n/a +A24 n/a n/a +A25 n/a n/a +A26 n/a n/a +A27 n/a n/a +A28 n/a n/a +A29 n/a n/a +A30 n/a n/a +A31 n/a n/a +A32 n/a n/a +B1 n/a n/a +B2 n/a n/a +B3 n/a n/a +B4 n/a n/a +B5 n/a n/a +B6 n/a n/a +B7 n/a n/a +B8 n/a n/a +B9 n/a n/a +B10 n/a n/a +B11 n/a n/a +B12 n/a n/a +B13 n/a n/a +B14 n/a n/a +B15 n/a n/a +B16 n/a n/a +B17 n/a n/a +B18 n/a n/a +B19 n/a n/a +B20 n/a n/a +B21 n/a n/a +B22 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a/eeg_face13/sub-003/eeg/sub-003_task-faceFO_coordsystem.json b/eeg_face13/sub-003/eeg/sub-003_task-faceFO_coordsystem.json new file mode 100644 index 000000000..dcdec75d7 --- /dev/null +++ b/eeg_face13/sub-003/eeg/sub-003_task-faceFO_coordsystem.json @@ -0,0 +1 @@ +{"EEGCoordinateSystem":"Other","EEGCoordinateUnits":"metres"} \ No newline at end of file diff --git a/eeg_face13/sub-003/eeg/sub-003_task-faceFO_eeg.edf b/eeg_face13/sub-003/eeg/sub-003_task-faceFO_eeg.edf new file mode 100644 index 000000000..e69de29bb diff --git a/eeg_face13/sub-003/eeg/sub-003_task-faceFO_eeg.json b/eeg_face13/sub-003/eeg/sub-003_task-faceFO_eeg.json new file mode 100644 index 000000000..d80f77a2e --- /dev/null +++ b/eeg_face13/sub-003/eeg/sub-003_task-faceFO_eeg.json @@ -0,0 +1,25 @@ +{ + "TaskName": "faceFO", + "TaskDescription": "Visual presentation of oval cropped face and house images both upright and inverted. Rare left or right half oval checkerboards were presetned as targets for keypress response.", + "InstitutionName": "Brock University", + "InstitutionAddress": "500 Glenridge Ave, St.Catharines, Ontario", + "EOGChannelCount": 7, + "EMGChannelCount": 0, + "MiscChannelCount": 128, + "TriggerChannelCount": 0, + "PowerLineFrequency": 60, + "EEGPlacementScheme": "Custom equidistant 128 channel BioSemi montage established in coordination with Judith Schedden McMaster Univertisy", + "Manufacturer": "BioSemi", + "ManufacturersModelName": "ActiveTwo", + "HardwareFilters": "n/a", + "SoftwareFilters": "n/a", + "SoftwareVersions": "NI ActiView 532-Lores", + "CapManufacturer": "ElectroCap International", + "CapManufacturersModelName": "10032", + "EEGChannelCount": 0, + "EEGReference": "common", + "RecordingType": "continuous", + "RecordingDuration": 2307, + "SamplingFrequency": 512, + "ECGChannelCount": 0 +} diff --git a/eeg_face13/sub-003/eeg/sub-003_task-faceFO_electrodes.tsv 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