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Titolo:
Analysis and visualization of single-trial event-related potentials
Autore:
Jung, TP; Makeig, S; Westerfield, M; Townsend, J; Courchesne, E; Sejnowski, TJ;
Indirizzi:
Univ Calif San Diego, Inst Neural Computat, Dept 0523, La Jolla, CA 92093 USA Univ Calif San Diego La Jolla CA USA 92093 t 0523, La Jolla, CA 92093 USA Salk Inst, Howard Hughes Med Inst, San Diego, CA USA Salk Inst San Diego CA USA st, Howard Hughes Med Inst, San Diego, CA USA Salk Inst, Computat Neurobiol Lab, San Diego, CA USA Salk Inst San Diego CA USA st, Computat Neurobiol Lab, San Diego, CA USA USN, Hlth Res Ctr, San Diego, CA 92152 USA USN San Diego CA USA 92152USN, Hlth Res Ctr, San Diego, CA 92152 USA Childrens Hosp, Res Ctr, La Jolla, CA USA Childrens Hosp La Jolla CA USAChildrens Hosp, Res Ctr, La Jolla, CA USA
Titolo Testata:
HUMAN BRAIN MAPPING
fascicolo: 3, volume: 14, anno: 2001,
pagine: 166 - 185
SICI:
1065-9471(200111)14:3<166:AAVOSE>2.0.ZU;2-T
Fonte:
ISI
Lingua:
ENG
Soggetto:
FUNCTIONALLY INDEPENDENT COMPONENTS; VISUAL-SPATIAL ATTENTION; BLIND SEPARATION; EEG; ARTIFACTS; P300; PHASE; CLASSIFICATION; INFORMATION; ALGORITHM;
Keywords:
blind source separation; ICA; ERP; single-trial ERP; artifact removal; P300; alpha;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Life Sciences
Citazioni:
57
Recensione:
Indirizzi per estratti:
Indirizzo: Jung, TP Univ Calif San Diego, Inst Neural Computat, Dept 0523, 9500 Gilman Dr, La Jolla, CA 92093 USA Univ Calif San Diego 9500 Gilman Dr La Jolla CA USA 92093 093 USA
Citazione:
T.P. Jung et al., "Analysis and visualization of single-trial event-related potentials", HUM BRAIN M, 14(3), 2001, pp. 166-185

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 experimenton 28 control subjects plus 22 neurological patients whose EEG records were heavily contaminated with blink and other eye-movement artifacts. Resultsshow that ICA can separate artifactual, stimulus-locked, response-locked, and non-event-related background EEG activities into separate components, ataxonomy 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 componentstability 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. (C) 2001 Wiley-Liss, Inc.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 27/01/20 alle ore 02:17:11