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Signed-off-by: Bruna Junqueira Lopes <99977808+brunaafl@users.noreply.github.com>
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,34 @@ | ||
| import glob | ||
| from argparse import ArgumentParser | ||
| from pathlib import Path | ||
|
|
||
| import pandas as pd | ||
|
|
||
|
|
||
| def prepare_table(df: pd.DataFrame): | ||
| no_pwc = df["PapersWithCode leaderboard"].isna() | ||
| df.loc[no_pwc, "PapersWithCode leaderboard"] = "No" | ||
| df.loc[~no_pwc, "PapersWithCode leaderboard"] = df.loc[ | ||
| ~no_pwc, "PapersWithCode leaderboard" | ||
| ].apply(lambda x: f"`Yes <{x}>`_") | ||
| df["Dataset"] = df["Dataset"].apply(lambda x: f":class:`{x}`") | ||
|
|
||
|
|
||
| def main(source_dir: str, target_dir: str): | ||
| target_dir = Path(target_dir) | ||
| target_dir.mkdir(parents=True, exist_ok=True) | ||
| files = glob.glob(str(Path(source_dir) / "*.csv")) | ||
| for f in files: | ||
| target_file = target_dir / Path(f).name | ||
| print(f"Processing {f} -> {target_file}") | ||
| df = pd.read_csv(f, index_col=False, header=0, skipinitialspace=True) | ||
| prepare_table(df) | ||
| df.to_csv(target_file, index=False) | ||
|
|
||
|
|
||
| if __name__ == "__main__": | ||
| parser = ArgumentParser() | ||
| parser.add_argument("source_dir", type=str) | ||
| parser.add_argument("target_dir", type=str) | ||
| args = parser.parse_args() | ||
| main(args.source_dir, args.target_dir) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
|
|
@@ -31,7 +31,7 @@ Motor Imagery Datasets | |
| Weibo2014 | ||
| Zhou2016 | ||
| Stieger2021 | ||
|
|
||
| Liu2024 | ||
|
|
||
| ------------ | ||
| ERP Datasets | ||
|
|
||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,5 +1,5 @@ | ||
| # flake8: noqa | ||
| __version__ = "1.1.0" | ||
| __version__ = "1.1.1" | ||
|
|
||
| from .benchmark import benchmark | ||
| from .utils import make_process_pipelines, set_download_dir, set_log_level, setup_seed |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,340 @@ | ||
| """Liu2024 Motor imagery dataset.""" | ||
|
|
||
| import os | ||
| import shutil | ||
| import warnings | ||
| import zipfile as z | ||
| from pathlib import Path | ||
| from typing import Any, Dict, Tuple | ||
|
|
||
| import mne | ||
| import numpy as np | ||
| import pandas as pd | ||
| from mne.channels import read_custom_montage | ||
|
|
||
| from moabb.datasets import download as dl | ||
| from moabb.datasets.base import BaseDataset | ||
|
|
||
|
|
||
| # Link to the raw data | ||
| LIU2024_URL = "https://figshare.com/ndownloader/files/38516654" | ||
|
|
||
| # Links to the electrodes and events information files | ||
| LIU2024_ELECTRODES = "https://figshare.com/ndownloader/files/38516078" | ||
| LIU2024_EVENTS = "https://figshare.com/ndownloader/files/38516084" | ||
|
|
||
|
|
||
| class Liu2024(BaseDataset): | ||
| """ | ||
| Dataset [1]_ from the study on motor imagery [2]_. | ||
| **Dataset description** | ||
| This dataset includes data from 50 acute stroke patients (the time after stroke ranges from 1 day to 30 days) | ||
| admitted to the stroke unit of Xuanwu Hospital of Capital Medical University. The patients included 39 males (78%) | ||
| and 11 females (22%), aged between 31 and 77 years, with an average age of 56.70 years (SD = 10.57) | ||
| Before the start of the experiment, the subject sat in a chair in a position as comfortable as possible with an | ||
| EEG cap placed on their head; subjects were positioned approximately 80 cm away from a computer screen in front of them. | ||
| The computer played audio instructions to the patient about the procedure. Each experiment lasted approximately 20 minutes, | ||
| including preparation time and approximately 10 minutes of signal recording. Before the start of the MI experiment, | ||
| the patients opened their eyes and closed their eyes for 1 minute each. The MI experiment was divided into 40 trials, and | ||
| each trial took 8 seconds, which consisted of three stages (instruction, MI and break). In the instruction stage, patients | ||
| were prompted to imagine grasping a spherical object with the left- or right-hand. In the MI stage, participants imagined | ||
| performing this action, a video of gripping motion is played on the computer, which leads the patient imagine grabbing the | ||
| ball. This video stays playing for 4 s. Patients only imagine one hand movement.In the break stage, participants were allowed | ||
| to relax and rest. The MI experiments alternated between the left- and right-hand, and the patients moved onto the next stage | ||
| of the experiment according to the instructions. | ||
| The EEG data were collected through a wireless multichannel EEG acquisition system (ZhenTec NT1, Xi’an ZhenTec Intelligence | ||
| Technology Co., Ltd., China). The system includes an EEG cap, an EEG acquisition amplifier, a data receiver and host computer | ||
| software. The EEG cap had electrodes placed according to the international 10-10 system, including 29 EEG recording electrodes | ||
| and 2 electrooculography (EOG) electrodes. The reference electrode located at CPz position and the grounding electrode located | ||
| at FPz position. All the EEG electrodes and grounding electrode are Ag/AgCl semi-dry EEG electrodes based on highly absorbable | ||
| porous sponges that are dampened with 3% NaCl solution. The EOG electrodes are composed by Ag/AgCl electrodes and conductive | ||
| adhesive hydrogel. The common-mode rejection ratio was 120 dB, the input impedance was 1 GΩ, the input noise was less than | ||
| 0.4 μVrms, and the resolution was 24 bits. The acquisition impedance was less than or equal to 20 kΩ. The sampling frequency | ||
| was 500 Hz. | ||
| References | ||
| ---------- | ||
| .. [1] Liu, Haijie; Lv, Xiaodong (2022). EEG datasets of stroke patients. | ||
| figshare. Dataset. DOI: https://doi.org/10.6084/m9.figshare.21679035.v5 | ||
| .. [2] Liu, Haijie, Wei, P., Wang, H. et al. An EEG motor imagery dataset | ||
| for brain computer interface in acute stroke patients. Sci Data 11, 131 | ||
| (2024). DOI: https://doi.org/10.1038/s41597-023-02787-8 | ||
| Notes | ||
| ----- | ||
| To add the break and instruction events, set the `break_events` and | ||
| `instr_events` parameters to True while instantiating the class. | ||
| .. versionadded:: 1.1.1 | ||
| """ | ||
|
|
||
| def __init__(self, break_events=False, instr_events=False): | ||
| self.break_events = break_events | ||
| self.instr_events = instr_events | ||
| events = {"left_hand": 1, "right_hand": 2} | ||
| if break_events: | ||
| events["instr"] = 3 | ||
| if instr_events: | ||
| events["break"] = 4 | ||
| super().__init__( | ||
| subjects=list(range(1, 50 + 1)), | ||
| sessions_per_subject=1, | ||
| events=events, | ||
| code="Liu2024", | ||
| interval=(2, 6), | ||
| paradigm="imagery", | ||
| doi="10.1038/s41597-023-02787-8", | ||
| ) | ||
|
|
||
| def data_path( | ||
| self, subject, path=None, force_update=False, update_path=None, verbose=None | ||
| ): | ||
| """Return the data paths of a single subject. | ||
| Parameters | ||
| ---------- | ||
| subject : int | ||
| The subject number to fetch data for. | ||
| path : None | str | ||
| Location of where to look for the data storing location. If None, | ||
| the environment variable or config parameter MNE_(dataset) is used. | ||
| If it doesn’t exist, the “~/mne_data” directory is used. If the | ||
| dataset is not found under the given path, the data | ||
| will be automatically downloaded to the specified folder. | ||
| force_update : bool | ||
| Force update of the dataset even if a local copy exists. | ||
| update_path : bool | None | ||
| If True, set the MNE_DATASETS_(dataset)_PATH in mne-python config | ||
| to the given path. | ||
| If None, the user is prompted. | ||
| verbose : bool, str, int, or None | ||
| If not None, override default verbose level (see mne.verbose()). | ||
| Returns | ||
| ------- | ||
| list | ||
| A list containing the path to the subject's data file. | ||
| """ | ||
| if subject not in self.subject_list: | ||
| raise ValueError("Invalid subject number") | ||
|
|
||
| # Download the zip file containing the data | ||
| path_zip = dl.data_dl(LIU2024_URL, self.code) | ||
| path_zip = Path(path_zip) | ||
| path_folder = path_zip.parent | ||
|
|
||
| # Extract the zip file if it hasn't been extracted yet | ||
| if not (path_folder / "edffile").is_dir(): | ||
| zip_ref = z.ZipFile(path_zip, "r") | ||
| zip_ref.extractall(path_folder) | ||
|
|
||
| subject_paths = [] | ||
| sub = f"sub-{subject:02d}" | ||
|
|
||
| # Construct the path to the subject's data file | ||
| subject_path = ( | ||
| path_folder / "edffile" / sub / "eeg" / f"{sub}_task-motor-imagery_eeg.edf" | ||
| ) | ||
| subject_paths.append(str(subject_path)) | ||
|
|
||
| return subject_paths | ||
|
|
||
| def encoding(self, events_df: pd.DataFrame) -> Tuple[np.array, Dict[int, str]]: | ||
| """Encode the columns 'value' and 'trial_type' into a single event type. | ||
| Parameters | ||
| ---------- | ||
| events_df : pd.DataFrame | ||
| DataFrame containing the events information. | ||
| Returns | ||
| ------- | ||
| np.ndarray | ||
| Array of encoded event types. | ||
| Notes | ||
| ----- | ||
| The 'trial_type' variable can take the following values: | ||
| - 1 : Left hand | ||
| - 2 : Right hand | ||
| The 'value' variable can take the following values: | ||
| - 1 : instructions | ||
| - 2 : MI | ||
| - 3 : break | ||
| """ | ||
| # Define the mapping dictionary | ||
| encoding_mapping = { | ||
| (2, 2): 1, # Left hand, MI | ||
| (1, 2): 2, # Right hand, MI | ||
| } | ||
|
|
||
| mapping = { | ||
| 1: "left_hand", | ||
| 2: "right_hand", | ||
| } | ||
|
|
||
| if self.instr_events: | ||
| encoding_mapping.update( | ||
| { | ||
| (1, 1): 3, # Right hand, instructions | ||
| (2, 1): 3, # Left hand, instructions | ||
| } | ||
| ) | ||
| mapping[3] = "instr" | ||
|
|
||
| if self.break_events: | ||
| encoding_mapping.update( | ||
| { | ||
| (1, 3): 4, # Right hand, break | ||
| (2, 3): 4, # Left hand, break | ||
| } | ||
| ) | ||
| mapping[4] = "break" | ||
|
|
||
| # Filter out rows that won't be encoded | ||
| valid_tuples = encoding_mapping.keys() | ||
| events_df = events_df[ | ||
| events_df.apply( | ||
| lambda row: (row["trial_type"], row["value"]) in valid_tuples, axis=1 | ||
| ) | ||
| ] | ||
|
|
||
| # Apply the mapping to the DataFrame | ||
| event_category = events_df.apply( | ||
| lambda row: encoding_mapping[(row["trial_type"], row["value"])], axis=1 | ||
| ) | ||
|
|
||
| return event_category, mapping | ||
|
|
||
| def _get_single_subject_data(self, subject): | ||
| """Return the data of a single subject. | ||
| Parameters | ||
| ---------- | ||
| subject : int | ||
| The subject number to fetch data for. | ||
| Returns | ||
| ------- | ||
| dict | ||
| A dictionary containing the raw data for the subject. | ||
| """ | ||
|
|
||
| file_path_list = self.data_path(subject)[0] | ||
| path_electrodes, path_events = self.data_infos() | ||
|
|
||
| with warnings.catch_warnings(): | ||
| warnings.simplefilter("ignore") | ||
| # Read the subject's raw data | ||
| raw = mne.io.read_raw_edf( | ||
| file_path_list, verbose=False, infer_types=True, stim_channel="" | ||
| ) | ||
|
|
||
| # Dropping reference channels with constant values | ||
| raw = raw.drop_channels(["CPz"]) | ||
|
|
||
| # Renaming channels accurately | ||
| raw.rename_channels({"HEOR": "VEOR", "": "STI"}) | ||
|
|
||
| # Create a dictionary with the channel names and their new types | ||
| mapping = {"STI": "stim", "VEOR": "eog", "HEOL": "eog"} | ||
|
|
||
| # Set the new channel types | ||
| raw.set_channel_types(mapping) | ||
|
|
||
| # Normalize and Read the montage | ||
| path_electrodes = self._normalize_extension(path_electrodes) | ||
| # Read and set the montage | ||
| montage = read_custom_montage(path_electrodes) | ||
|
|
||
| events_df = pd.read_csv(path_events, sep="\t") | ||
|
|
||
| # Encode the events | ||
| event_category, mapping = self.encoding(events_df=events_df) | ||
|
|
||
| events = self.create_event_array(raw=raw, event_category=event_category) | ||
|
|
||
| # Creating and setting annotations from the events | ||
| annotations = mne.annotations_from_events( | ||
| events, sfreq=raw.info["sfreq"], event_desc=mapping | ||
| ) | ||
|
|
||
| raw = raw.set_annotations(annotations) | ||
| with warnings.catch_warnings(): | ||
| warnings.simplefilter("ignore") | ||
| # Removing the stimulus channels | ||
| raw = raw.pick(["eeg", "eog"]) | ||
| # Setting the montage | ||
| raw = raw.set_montage(montage, verbose=False) | ||
| # Loading dataset | ||
| raw = raw.load_data(verbose=False) | ||
| # There is only one session | ||
| sessions = {"0": {"0": raw}} | ||
|
|
||
| return sessions | ||
|
|
||
| def data_infos(self): | ||
| """Returns the data paths of the electrodes and events information | ||
| This function downloads the necessary data files for electrodes | ||
| and events from their respective URLs and returns their local file paths. | ||
| Returns | ||
| ------- | ||
| tuple | ||
| A tuple containing the local file paths to the channels, electrodes, | ||
| and events information files. | ||
| """ | ||
|
|
||
| path_electrodes = dl.data_dl(LIU2024_ELECTRODES, self.code) | ||
|
|
||
| path_events = dl.data_dl(LIU2024_EVENTS, self.code) | ||
|
|
||
| return path_electrodes, path_events | ||
|
|
||
| @staticmethod | ||
| def _normalize_extension(file_name: str) -> str: | ||
| # Renaming the .tsv file to make sure it's recognized as .tsv | ||
| # Check if the file already has the ".tsv" extension | ||
|
|
||
| file_electrodes_tsv = file_name + ".tsv" | ||
|
|
||
| if not os.path.exists(file_electrodes_tsv): | ||
| # Perform the rename operation only if the target file | ||
| # doesn't exist | ||
| shutil.copy(file_name, file_electrodes_tsv) | ||
|
|
||
| return file_electrodes_tsv | ||
|
|
||
| @staticmethod | ||
| def create_event_array(raw: Any, event_category: np.ndarray) -> np.ndarray: | ||
| """ | ||
| This method creates an event array based on the stimulus channel. | ||
| Parameters | ||
| ---------- | ||
| raw : mne.io.Raw | ||
| The raw data. | ||
| event_category : np.ndarray | ||
| The event categories. | ||
| Returns | ||
| ------- | ||
| events : np.ndarray | ||
| The created events array. | ||
| """ | ||
| _, idx_trigger = np.nonzero(raw.copy().pick("STI").get_data()) | ||
| n_label_stim = len(event_category) | ||
| # Create the events array based on the stimulus channel | ||
| events = np.column_stack( | ||
| (idx_trigger[:n_label_stim], np.zeros_like(event_category), event_category) | ||
| ) | ||
| return events |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,7 @@ | ||
| Dataset, #Subj, #Sessions, Sampling rate, #Chan, Trials length, #Trial classes, #Trials / class, #Epochs classes, #Epochs / class, Codes, Presentation rate, PapersWithCode leaderboard | ||
| Thielen2015,12,1,2048Hz,64,4.2s,36,3,2,27216 NT / 27216 T,Gold codes,120Hz, | ||
| Thielen2021,30,1,512Hz,8,31.5s,20,5,2,18900 NT / 18900 T,Gold codes,60Hz, | ||
| CastillosCVEP100, 12,1,500Hz,32,2.2s,4,15/15/15/15,2,3525 NT / 3495 T,m-sequence,60Hz, | ||
| CastillosCVEP40, 12,1,500Hz,32,2.2s,4,15/15/15/15,2,3525 NT / 3495 T,m-sequence,60Hz, | ||
| CastillosBurstVEP40, 12,1,500Hz,32,2.2s,4,15/15/15/15,2,5820 NT / 1200 T,Burst-CVEP,60Hz, | ||
| CastillosBurstVEP100,12,1,500Hz,32,2.2s,4,15/15/15/15,2,5820 NT / 1200 T,Burst-CVEP,60Hz, |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,19 @@ | ||
| Dataset, #Subj, #Chan, #Classes, #Trials, Trial length, Freq, #Session, #Runs, Total_trials, PapersWithCode leaderboard | ||
| AlexMI,8,16,3,20,3s,512Hz,1,1,480,https://paperswithcode.com/dataset/alexandremotorimagery-moabb | ||
| BNCI2014_001,9,22,4,144,4s,250Hz,2,6,62208,https://paperswithcode.com/dataset/bnci2014-001-moabb-1 | ||
| BNCI2014_002,14,15,2,80,5s,512Hz,1,8,17920,https://paperswithcode.com/dataset/bnci2014-002-moabb-1 | ||
| BNCI2014_004,9,3,2,360,4.5s,250Hz,5,1,32400,https://paperswithcode.com/dataset/bnci2014-004-moabb-1 | ||
| BNCI2015_001,12,13,2,200,5s,512Hz,3,1,14400,https://paperswithcode.com/dataset/bnci2015-001-moabb-1 | ||
| BNCI2015_004,9,30,5,80,7s,256Hz,2,1,7200,https://paperswithcode.com/dataset/bnci2015-004-moabb-1 | ||
| Cho2017,52,64,2,100,3s,512Hz,1,1,9800,https://paperswithcode.com/dataset/cho2017-moabb | ||
| Lee2019_MI,54,62,2,100,4s,1000Hz,2,1,11000,https://paperswithcode.com/dataset/lee2019-mi-moabb-1 | ||
| GrosseWentrup2009,10,128,2,150,7s,500Hz,1,1,3000,https://paperswithcode.com/dataset/grossewentrup2009-moabb | ||
| Schirrmeister2017,14,128,4,120,4s,500Hz,1,2,13440,https://paperswithcode.com/dataset/schirrmeister2017-moabb | ||
| Ofner2017,15,61,7,60,3s,512Hz,1,10,63000, | ||
| PhysionetMI,109,64,4,23,3s,160Hz,1,1,69760,https://paperswithcode.com/dataset/physionetmotorimagery-moabb | ||
| Shin2017A,29,30,2,30,10s,200Hz,3,1,5220,https://paperswithcode.com/dataset/shin2017a-moabb | ||
| Shin2017B,29,30,2,30,10s,200Hz,3,1,5220, | ||
| Weibo2014,10,60,7,80,4s,200Hz,1,1,5600,https://paperswithcode.com/dataset/weibo2014-moabb | ||
| Zhou2016,4,14,3,160,5s,250Hz,3,2,11496,https://paperswithcode.com/dataset/zhou2016-moabb | ||
| Stieger2021,62,64,4,450,3s,1000Hz,7 or 11,1,250000, | ||
| Liu2024,50,29,2,20,4s,500Hz,1,1,2000, |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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| Dataset, #Subj, #Chan, #Trials / class, Trials length, Sampling rate, #Sessions, PapersWithCode leaderboard | ||
| BNCI2014_008, 8, 8, 3500 NT / 700 T, 1s, 256Hz, 1,https://paperswithcode.com/dataset/bnci2014-008-moabb-1 | ||
| BNCI2014_009, 10, 16, 1440 NT / 288 T, 0.8s, 256Hz, 3,https://paperswithcode.com/dataset/bnci2014-009-moabb-1 | ||
| BNCI2015_003, 10, 8, 1500 NT / 300 T, 0.8s, 256Hz, 1,https://paperswithcode.com/dataset/bnci2015-003-moabb-1 | ||
| BI2012, 25, 16, 640 NT / 128 T, 1s, 128Hz, 2,https://paperswithcode.com/dataset/braininvaders2012-moabb | ||
| BI2013a, 24, 16, 3200 NT / 640 T, 1s, 512Hz, 8 for subjects 1-7 else 1,https://paperswithcode.com/dataset/braininvaders2013a-moabb | ||
| BI2014a, 64, 16, 990 NT / 198 T, 1s, 512Hz, up to 3,https://paperswithcode.com/dataset/braininvaders2014a-moabb | ||
| BI2014b, 38, 32, 200 NT / 40 T, 1s, 512Hz, 3,https://paperswithcode.com/dataset/braininvaders2014b-moabb | ||
| BI2015a, 43, 32, 4131 NT / 825 T, 1s, 512Hz, 3,https://paperswithcode.com/dataset/braininvaders2015a-moabb | ||
| BI2015b, 44, 32, 2160 NT / 480 T, 1s, 512Hz, 1,https://paperswithcode.com/dataset/braininvaders2015b-moabb | ||
| Cattan2019_VR, 21, 16, 600 NT / 120 T, 1s, 512Hz, 2,https://paperswithcode.com/dataset/cattan2019-vr-moabb-1 | ||
| Huebner2017, 13, 31, 364 NT / 112 T, 0.9s, 1000Hz, 3,https://paperswithcode.com/dataset/huebner2017-moabb | ||
| Huebner2018, 12, 31, 364 NT / 112 T, 0.9s, 1000Hz, 3,https://paperswithcode.com/dataset/huebner2018-moabb | ||
| Sosulski2019, 13, 31, 7500 NT / 1500 T, 1.2s, 1000Hz, 1,https://paperswithcode.com/dataset/sosulski2019-moabb | ||
| EPFLP300, 8, 32, 2753 NT / 551 T, 1s, 2048Hz, 4,https://paperswithcode.com/dataset/epflp300-moabb | ||
| Lee2019_ERP, 54, 62, 6900 NT / 1380 T, 1s, 1000Hz, 2,https://paperswithcode.com/dataset/lee2019-erp-moabb-1 | ||
| DemonsP300, 60, 8, 935 NT / 50 T, 1s, 500Hz, 1, |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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| Dataset, #Subj, #Chan, #Classes, #Blocks / class, Trials length, Sampling rate, #Sessions, PapersWithCode leaderboard | ||
| Cattan2019_PHMD,12,16,2,10,60s,512Hz,1, | ||
| Hinss2021,15,62,4,1,2s,250Hz,1, | ||
| Rodrigues2017,20,16,2,5,10s,512Hz,1, |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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| Dataset, #Subj, #Chan, #Classes, #Trials / class, Trials length, Sampling rate, #Sessions, PapersWithCode leaderboard | ||
| Lee2019_SSVEP,54,62,4,50,4s,1000Hz,2,https://paperswithcode.com/dataset/lee2019-ssvep-moabb-1 | ||
| Kalunga2016,12,8,4,16,2s,256Hz,1,https://paperswithcode.com/dataset/kalunga2016-moabb | ||
| MAMEM1,10,256,5,12-15,3s,250Hz,1,https://paperswithcode.com/dataset/mamem1-moabb | ||
| MAMEM2,10,256,5,20-30,3s,250Hz,1,https://paperswithcode.com/dataset/mamem2-moabb | ||
| MAMEM3,10,14,4,20-30,3s,128Hz,1,https://paperswithcode.com/dataset/mamem3-moabb | ||
| Nakanishi2015,9,8,12,15,4.15s,256Hz,1,https://paperswithcode.com/dataset/nakanishi2015-moabb | ||
| Wang2016,34,62,40,6,5s,250Hz,1,https://paperswithcode.com/dataset/wang2016-moabb |
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