Analyzing and Visualizing Single-Trial Event-Related Potentials

Abstract

Event-related potentials (ERPs), are portions of electroencephalo(cid:173) graphic (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 activ(cid:173) ity, 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 Anal(cid:173) ysis (ICA) [1], 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 on normal and autistic subjects show that ICA can sep(cid:173) arate artifactual, stimulus-locked, response-locked, and. non-event related background EEG activities into separate components, al(cid:173) lowing ( 1) removal of pervasive artifacts of all types from single-trial EEG records, and (2) identification of both stimulus- and response(cid:173) locked EEG components. Second, this study proposes a new visual(cid:173) ization tool, the 'ERP image', for investigating variability in laten(cid:173) cies and amplitudes of event-evoked responses in spontaneous EEG or MEG records. We show that sorting single-trial ERP epochs in order of reaction time and plotting the potentials in 2-D clearly reveals underlying patterns of response variability linked to per(cid:173) formance. These analysis and visualization tools appear broadly applicable to electrophyiological research on both normal and clin(cid:173) ical populations.