Skip to main content Accessibility help
×
Home

Article contents

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

Get access

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

Access options

Get access to the full version of this content by using one of the access options below.

Full text views

Full text views reflects PDF downloads, PDFs sent to Google Drive, Dropbox and Kindle and HTML full text views.

Total number of HTML views: 24
Total number of PDF views: 394 *
View data table for this chart

* Views captured on Cambridge Core between September 2016 - 3rd December 2020. This data will be updated every 24 hours.

Hostname: page-component-79f79cbf67-8q5vc Total loading time: 0.279 Render date: 2020-12-03T04:45:20.178Z Query parameters: { "hasAccess": "0", "openAccess": "0", "isLogged": "0", "lang": "en" } Feature Flags last update: Thu Dec 03 2020 04:07:24 GMT+0000 (Coordinated Universal Time) Feature Flags: { "metrics": true, "metricsAbstractViews": false, "peerReview": true, "crossMark": true, "comments": true, "relatedCommentaries": true, "subject": true, "clr": false, "languageSwitch": true }

Send article to Kindle

To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Removing electroencephalographic artifacts by blind source separation
Available formats
×

Send article to Dropbox

To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

Removing electroencephalographic artifacts by blind source separation
Available formats
×

Send article to Google Drive

To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

Removing electroencephalographic artifacts by blind source separation
Available formats
×
×

Reply to: Submit a response


Your details


Conflicting interests

Do you have any conflicting interests? *