Analysis and visualization of single-trial event-related potentials

In this study, a linear decomposition technique, independent component anal ysis (ICA), is applied to single-trial multichannel EEG data from event-rel ated potential (ERP) experiments. Spatial filters derived by ICA blindly se parate the input data into a sum of temporally independent and spatially fi xed components arising from distinct or overlapping brain or extra-brain so urces. Both the data and their decomposition are displayed using a new visu alization tool, the "ERP image," that can clearly characterize single-trial variations in the amplitudes and latencies of evoked responses, particular ly when sorted by a relevant behavioral or physiological variable. These to ols were used to analyze data from a visual selective attention experiment on 28 control subjects plus 22 neurological patients whose EEG records were heavily contaminated with blink and other eye-movement artifacts. Results show that ICA can separate artifactual, stimulus-locked, response-locked, a nd non-event-related background EEG activities into separate components, a taxonomy not obtained from conventional signal averaging approaches. This m ethod allows: (1) removal of pervasive artifacts of all types from single-t rial EEG records, (2) identification and segregation of stimulus- and respo nse-locked EEG components, (3) examination of differences in single-trial r esponses, and (4) separation of temporally distinct but spatially overlappi ng EEG oscillatory activities with distinct relationships to task events. T he proposed methods also allow the interaction between ERPs and the ongoing EEG to be investigated directly. We studied the between-subject component stability of ICA decomposition of single-trial EEG epochs by clustering com ponents with similar scalp maps and activation power spectra. Components ac counting for blinks, eye movements, temporal muscle activity, event-related potentials, and event-modulated alpha activities were largely replicated a cross subjects. Applying ICA and ERP image visualization to the analysis of sets of single trials from event-related EEG (or MEG) experiments can incr ease the information available from ERP (or ERF) data. (C) 2001 Wiley-Liss, Inc.