MOVING-WINDOW ICA DECOMPOSITION OF EEG DATA REVEALS EVENT-RELATED CHANGES IN OSCILLATORY BRAIN ACTIVITY
Decomposition of temporally overlapping sub- epochs from 3-s electroencephalographic (EEG) epochs time locked to the presentation of visual target stimuli in a selective attention task produced many more components with common scalp maps before stimulus delivery than after it. In particular, this was the case for components accounting for posterior alpha and central mu rhythms. Moving-window ICA decomposition thus appears to be a useful technique for evaluating changes in the independence of activity in different brain regions, i.e. event-related changes in brain dynamic modularity. However, common component clusters found by moving- window ICA decomposition strongly resembled those found by decomposition of the whole EEG epochs, suggesting that such whole epoch decomposition reveals stable independent components of EEG signals.