ICA2000
Helsinki, Finland
August, 2000

(About ICA2001)


Moving-Window ICA Decomposition of EEG Data Reveals Event-Related Changes in Oscillatory Brain Activity


Scott Makeig*, Sigurd Enghoff (3,4). Tzyy-Ping Jung (3,5,6) and Terrence J. Sejnowski
The Second International Workshop on Independent Component Analysis and Signal Separation, 2000.

Abstract

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.

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Independent Component Analysis of Biomedical Signals


Jung T-P, Makeig S, Lee T-W, McKeown M.J., Brown G., Bell, A.J. and Sejnowski TJ,
The Second International Workshop on Independent Component Analysis and Signal Separation, 2000.

Abstract

Biomedical signals from many sources including hearts, brains and endocrine systems pose a challenge to researchers who may have to separate weak signals arriving from multiple sources contaminated with artifacts and noise. The analysis of these signals is important both for research and for medical diagnosis and treatment. The applications of Independent Component Analysis (ICA) to biomedical signals is a rapidly expanding area of research and many groups are now actively engaged in exploring the potential of blind signal separation and signal deconvolution for revealing new information about the brain and body. In this review, we survey some recent applications of ICA to a variety of electrical, magnetic and hemodynamic measurements, drawing primarily from our own research.

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