A fully automated correction method of EOG artifacts in EEG recordings

Abstract Objective A fully automated method for reducing EOG artifacts is presented and validated. Methods The correction method is based on regression analysis and was applied to 18 recordings with 22 channels and approx. 6 min each. Two independent experts scored the original and corrected EEG in...

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Bibliographic Details
Published in:Clinical neurophysiology 2007-01, Vol.118 (1), p.98-104
Main Authors: Schlögl, A, Keinrath, C, Zimmermann, D, Scherer, R, Leeb, R, Pfurtscheller, G
Format: Article
Language:English
Subjects:
Adolescent
Adult
Artifact processing
Biological and medical sciences
Brain - physiology
Brain Mapping
Central nervous system
Electrodiagnosis. Electric activity recording
Electroencephalogram
Electroencephalography
Electrooculogram
Electrooculography
Electrophysiology
Female
Fundamental and applied biological sciences. Psychology
Humans
Investigative techniques, diagnostic techniques (general aspects)
Male
Medical sciences
Missing values
Nervous system
Neurology
Regression analysis
Reproducibility of Results
Signal Processing, Computer-Assisted
Vertebrates: nervous system and sense organs
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Summary:Abstract Objective A fully automated method for reducing EOG artifacts is presented and validated. Methods The correction method is based on regression analysis and was applied to 18 recordings with 22 channels and approx. 6 min each. Two independent experts scored the original and corrected EEG in a blinded evaluation. Results The expert scorers identified in 5.9% of the raw data some EOG artifacts; 4.7% were corrected. After applying the EOG correction, the expert scorers identified in another 1.9% of the data some EOG artifacts, which were not recognized in the uncorrected data. Conclusions The advantage of a fully automated reduction of EOG artifacts justifies the small additional effort of the proposed method and is a viable option for reducing EOG artifacts. The method has been implemented for offline and online analysis and is available through BioSig, an open source software library for biomedical signal processing. Significance Visual identification and rejection of EOG-contaminated EEG segments can miss many EOG artifacts, and is therefore not sufficient for removing EOG artifacts. The proposed method was able to reduce EOG artifacts by 80%.
ISSN:1388-2457
1872-8952
DOI:10.1016/j.clinph.2006.09.003