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MoBILAB toolbox for MATLAB is an analysis and visualization platform for Mobile Brain/Body data (Makeig et al., 2009). MoBILAB uses object oriented programming to define classes that encapsulate the analysis and visualization of time-series, EEG (time-series + head model), motion capture data, sound, video, etc. The software is still a work in progress, functionalities like ICA, Kalman Filter motion tracking, and group analysis will be added shortly. It can be used as a standalone toolbox or as a plugin for EEGLAB.
Makeig S, Gramann K, Jung T-P, Sejnowski TJ, Poizner (2009). Linking brain, mind and behavior: The promise of mobile brain/body imaging (MoBI). International Journal of Psychophysiology 73:985-100.
===Screenshots===
Figure 1: MoBILAB's gui. Each step of processing is reflected on the tree structure embedded on the main gui. A pipeline can be automatically generated following any branch.
http://mobilab.googlecode.com/files/erp_target.png
Figure 2: Event-related potential associated to a reward auditory signal emitted after the participant found a hidden target. The figure on the top panel shows all the trials stacked, the figure at the bottom shows the ERP response after a wavelet-based denoising method (see erp_example on the Downloads section).
http://mobilab.googlecode.com/files/ersp_target.png
Figure 3: Event-related spectral perturbation of the data above.
http://mobilab.googlecode.com/files/itc_target.png
Figure 4: Inter-trial coherence of the data above.
http://mobilab.googlecode.com/files/head_model.png
Figure 5: Automatic estimation of individualized head models. No individual MRI is needed, the function warps a template in MNI space to make it match the manifold defined by the electrode positions of the subject, resulting in a new head model adjusted to the individual geometry. Once the head model is estimated, the realistic forward model can be computed using state of the art BEM solvers like OpenMEEG (http://www-sop.inria.fr/athena/software/OpenMEEG/) or EEGLAB/NFT (http://sccn.ucsd.edu/wiki/NFT).
http://mobilab.googlecode.com/files/forward_and_inverse_solutions.png
Figure 6: The panel on the left shows the Primary Current Density estimated by my implementation of sLORETA (http://www.uzh.ch/keyinst/loreta.htm). The panel on the right shows the correspondent scalp map estimated using ICA (http://sccn.ucsd.edu/wiki/Chapter_09:_Decomposing_Data_Using_ICA). The same method can be used to estimate inverse solutions of EEG topographies or tomographic locations of ICA scalp maps.