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Removing electroencephalographic artifacts by blind source separation

Published online by Cambridge University Press:  01 March 2000

TZYY-PING JUNG
Affiliation:
Howard Hughes Medical Institute and Computational Neurobiology Laboratory, The Salk Institute, San Diego, California, USA University of California San Diego, La Jolla, USA
SCOTT MAKEIG
Affiliation:
University of California San Diego, La Jolla, USA Naval Health Research Center, San Diego, California, USA
COLIN HUMPHRIES
Affiliation:
Howard Hughes Medical Institute and Computational Neurobiology Laboratory, The Salk Institute, San Diego, California, USA
TE-WON LEE
Affiliation:
Howard Hughes Medical Institute and Computational Neurobiology Laboratory, The Salk Institute, San Diego, California, USA University of California San Diego, La Jolla, USA
MARTIN J. McKEOWN
Affiliation:
Howard Hughes Medical Institute and Computational Neurobiology Laboratory, The Salk Institute, San Diego, California, USA
VICENTE IRAGUI
Affiliation:
University of California San Diego, La Jolla, USA
TERRENCE J. SEJNOWSKI
Affiliation:
Howard Hughes Medical Institute and Computational Neurobiology Laboratory, The Salk Institute, San Diego, California, USA University of California San Diego, La Jolla, USA
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Abstract

Eye movements, eye blinks, cardiac signals, muscle noise, and line noise present serious problems for electroencephalographic (EEG) interpretation and analysis when rejecting contaminated EEG segments results in an unacceptable data loss. Many methods have been proposed to remove artifacts from EEG recordings, especially those arising from eye movements and blinks. Often regression in the time or frequency domain is performed on parallel EEG and electrooculographic (EOG) recordings to derive parameters characterizing the appearance and spread of EOG artifacts in the EEG channels. Because EEG and ocular activity mix bidirectionally, regressing out eye artifacts inevitably involves subtracting relevant EEG signals from each record as well. Regression methods become even more problematic when a good regressing channel is not available for each artifact source, as in the case of muscle artifacts. Use of principal component analysis (PCA) has been proposed to remove eye artifacts from multichannel EEG. However, PCA cannot completely separate eye artifacts from brain signals, especially when they have comparable amplitudes. Here, we propose a new and generally applicable method for removing a wide variety of artifacts from EEG records based on blind source separation by independent component analysis (ICA). Our results on EEG data collected from normal and autistic subjects show that ICA can effectively detect, separate, and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably with those obtained using regression and PCA methods. ICA can also be used to analyze blink-related brain activity.

Type
Research Article
Copyright
© 2000 Society for Psychophysiological Research

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