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Abstract:
Abstract: Multichannel event-related EEG data, viewed as
linear sums of macropotentials generated in functionally
independent and spatially fixed brain (or extra-brain) regions, are
well-suited for blind decomposition by Independent Component
Analysis (ICA). ICA produces signals reflecting the separate
activities of the independent signal sources, plus maps giving
their respective projections to the scalp surface. A stability
analysis of ICA decomposition of event-related 31-channel EEG data
collected during a visual selective attention task (Makeig et al.,
J. Neurosci, 1999), plus trial analyses of higher-density EEG data,
suggests that high density arrays may be most suitable for
EEG-based functional imaging using ICA, but only if high quality
data are recorded at a sufficient number of channels. In general,
the functional independence of ICA-decomposed sources must be
tested for (a) physiologically plausibility and (b) distinct and
precise relationships to behavior or other variables. Decomposition
of single-trial 31-channel EEG data from a visual selective
attention experiment produced components meeting both criteria.
Results suggest a wealth of new information about event-related
brain dynamics, including: (1) A new view of the interaction
between human attention and fast motor responding. (2) New
hypotheses about the further decomposition of the late positive
complex (or P300) into EEG-based subcomponents. (3) New information
about the relation of early (P1/N1) visual response components to
ongoing EEG processes.
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