Automatic removal of eye movement and blink artifacts from EEG data using blind component separation

Signals from eye movements and blinks can be orders of magnitude larger than brain‐generated electrical potentials and are one of the main sources of artifacts in electroencephalographic (EEG) data. Rejecting contaminated trials causes substantial data loss, and restricting eye movements/blinks limi...

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Bibliographic Details
Published in:Psychophysiology 2004-03, Vol.41 (2), p.313-325
Main Authors: Joyce, Carrie A., Gorodnitsky, Irina F., Kutas, Marta
Format: Article
Language:English
Subjects:
Algorithms
Automated
Biological and medical sciences
Blind source separation
Blinking - physiology
Computer processing
Data Interpretation, Statistical
Electrodes
Electroencephalogram
Electroencephalography
Electrooculogram
Electrooculography
Eye Movements - physiology
Fundamental and applied biological sciences. Psychology
Humans
Independent component analysis
Methodology. Experimentation
Psychology. Psychoanalysis. Psychiatry
Psychology. Psychophysiology
Psychometrics. Statistics. Methodology
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Summary:Signals from eye movements and blinks can be orders of magnitude larger than brain‐generated electrical potentials and are one of the main sources of artifacts in electroencephalographic (EEG) data. Rejecting contaminated trials causes substantial data loss, and restricting eye movements/blinks limits the experimental designs possible and may impact the cognitive processes under investigation. This article presents a method based on blind source separation (BSS) for automatic removal of electroocular artifacts from EEG data. BBS is a signal‐processing methodology that includes independent component analysis (ICA). In contrast to previously explored ICA‐based methods for artifact removal, this method is automated. Moreover, the BSS algorithm described herein can isolate correlated electroocular components with a high degree of accuracy. Although the focus is on eliminating ocular artifacts in EEG data, the approach can be extended to other sources of EEG contamination such as cardiac signals, environmental noise, and electrode drift, and adapted for use with magnetoencephalographic (MEG) data, a magnetic correlate of EEG.
ISSN:0048-5772
1469-8986
1540-5958
DOI:10.1111/j.1469-8986.2003.00141.x