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Neuroimage. 2014 Feb 15;87:297-310. doi: 10.1016/j.neuroimage.2013.09.045. Epub 2013 Oct 8.

Cortical surface alignment in multi-subject spatiotemporal independent EEG source imaging.

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Institute of Statistical Science, Academia Sinica, Taiwan. Electronic address:
Swartz Center for Computational Neuroscience, University of California San Diego, La Jolla, CA, USA; Center for Advanced Neurological Engineering, University of California San Diego, La Jolla, CA, USA.
Institute of Statistical Science, Academia Sinica, Taiwan.
State Research Institute of Physiology, SB RAMS, Novosibirsk, Russia.
Swartz Center for Computational Neuroscience, University of California San Diego, La Jolla, CA, USA.


Brain responses to stimulus presentations may vary widely across subjects in both time course and spatial origins. Multi-subject EEG source imaging studies that apply Independent Component Analysis (ICA) to data concatenated across subjects have overlooked the fact that projections to the scalp sensors from functionally equivalent cortical sources vary from subject to subject. This study demonstrates an approach to spatiotemporal independent component decomposition and alignment that spatially co-registers the MR-derived cortical topographies of individual subjects to a well-defined, shared spherical topology (Fischl et al., 1999). Its efficacy for identifying functionally equivalent EEG sources in multi-subject analysis is demonstrated by analyzing EEG and behavioral data from a stop-signal paradigm using two source-imaging approaches, both based on individual subject independent source decompositions. The first, two-stage approach uses temporal infomax ICA to separate each subject's data into temporally independent components (ICs), then estimates the source density distribution of each IC process from its scalp map and clusters similar sources across subjects (Makeig et al., 2002). The second approach, Electromagnetic Spatiotemporal Independent Component Analysis (EMSICA), combines ICA decomposition and source current density estimation of the artifact-rejected data into a single spatiotemporal ICA decomposition for each subject (Tsai et al., 2006), concurrently identifying both the spatial source distribution of each cortical source and its event-related dynamics. Applied to the stop-signal task data, both approaches gave IC clusters that separately accounted for EEG processes expected in stop-signal tasks, including pre/postcentral mu rhythms, anterior-cingulate theta rhythm, and right-inferior frontal responses, the EMSICA clusters exhibiting more tightly correlated source areas and time-frequency features.


Cortically surface-based alignment; EMSICA; ERSP warping; ICA

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