title: Analysis and visualization of single-trial event-related potentials.
abstract: In this study, a linear decomposition technique, independent component analysis (ICA), is applied to single-trial multichannel EEG data from event-related potential (ERP) experiments. Spatial filters derived by ICA blindly separate the input data into a sum of temporally independent and spatially fixed components arising from distinct or overlapping brain or extra-brain sources. Both the data and their decomposition are displayed using a new visualization tool, the "ERP image," that can clearly characterize single-trial variations in the amplitudes and latencies of evoked responses, particularly when sorted by a relevant behavioral or physiological variable. These tools 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, and non-event-related background EEG activities into separate components, a taxonomy not obtained from conventional signal averaging approaches. This method allows: (1) removal of pervasive artifacts of all types from single-trial EEG records, (2) identification and segregation of stimulus- and response-locked EEG components, (3) examination of differences in single-trial responses, and (4) separation of temporally distinct but spatially overlapping EEG oscillatory activities with distinct relationships to task events. The 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 components with similar scalp maps and activation power spectra. Components accounting for blinks, eye movements, temporal muscle activity, event-related potentials, and event-modulated alpha activities were largely replicated across subjects. Applying ICA and ERP image visualization to the analysis of sets of single trials from event-related EEG (or MEG) experiments can increase the information available from ERP (or ERF) data.
authors.affiliation: Institute for Neural Computation, University of California San Diego, La Jolla, California 92093-0523, USA. jung@salk.edu
authors.forename: T P
authors.initials: TP
authors.lastname: Jung
authors.forename: S
authors.initials: S
authors.lastname: Makeig
authors.forename: M
authors.initials: M
authors.lastname: Westerfield
authors.forename: J
authors.initials: J
authors.lastname: Townsend
authors.forename: E
authors.initials: E
authors.lastname: Courchesne
authors.forename: T J
authors.initials: TJ
authors.lastname: Sejnowski
mesh_terms: D000513:Alpha Rhythm; D016477:Artifacts; D001683:Biological Clocks / Q000502:physiology; D001767:Blinking / Q000502:physiology; D001931:Brain Mapping / Q000379:methods; D002540:Cerebral Cortex / Q000502:physiology*; D004569:Electroencephalography / Q000379:methods*; D018913:Event-Related Potentials, P300 / Q000502:physiology*; D005133:Eye Movements / Q000502:physiology; D007839:Functional Laterality / Q000502:physiology; D014644:Genetic Variation / Q000502:physiology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D011930:Reaction Time / Q000502:physiology; D012815:Signal Processing, Computer-Assisted