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Abstract:
Event-related potentials (ERPs), are portions of
electroencephalographic (EEG) recordings that are both time- and
phase-locked to experimental events. ERPs are usually averaged to
increase their signal/noise ratio relative to non-phase locked EEG
activity, regardless of the fact that response activity in single
epochs may vary widely in time course and scalp distribution. This
study applies a linear decomposition tool, Independent Component
Analysis (ICA) (Lee et. al., in press), to multichannel
single-trial EEG records to derive spatial filters that decompose
single-trial EEG epochs into a sum of temporally independent and
spatially fixed components arising from distinct or overlapping
brain or extra-brain networks. Our results show that ICA can
separate artifactual, stimulus-locked, response-locked, and
non-event related background EEG activities into separate
components, allowing (1) removal of pervasive artifacts of all
types from single-trial EEG records, and (2) identification of both
stimulus- and response-locked EEG components. Second, this study
proposes a new visualization tool, the `ERP image', for
investigating variability in latencies and amplitudes of
event-evoked responses in spontaneous EEG or MEG records. We show
that sorting single-trial ERP epochs in order of a relevant
response measure (e.g. reaction time) and plotting the potentials
in 2-D clearly reveals underlying patterns of response variability
linked to performance. These analysis and visualization tools
appear broadly applicable to electrophyiological research on both
normal and clinical populations.
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