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Removal of the Ocular Artifact from the EEG: A Comparison of Time and Frequency Domain Methods with Simulated and Real Data
Author(s) -
Kenemans J. Leon,
Molenaar Peter C.M.,
Verbaten Marinus N.,
Slangen Jef L.
Publication year - 1991
Publication title -
psychophysiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.661
H-Index - 156
eISSN - 1469-8986
pISSN - 0048-5772
DOI - 10.1111/j.1469-8986.1991.tb03397.x
Subject(s) - frequency domain , regression analysis , electroencephalography , linear regression , statistics , regression , artifact (error) , time domain , lag , pattern recognition (psychology) , artificial intelligence , mathematics , computer science , psychology , mathematical analysis , computer network , psychiatry , computer vision
Frequency‐dependent transfer from EOG to EEG may be insufficiently accounted for by simple time domain regression methods (Gasser, Sroka, & Möcks, 1986; Woestenburg, Verbaten, & Slangen, 1983). In contrast, a multiple‐lag time domain regression analysis, using lagged regression of EEG on EOG, must theoretically account for both frequency dependence and independence. Two data sets were constructed, in which the transfer from EOG to EEG was either frequency‐independent (constant gain) or frequency‐dependent. Subsequently, three different correction methods were applied: 1) a simple regression analysis in the time domain; 2) a multiple‐lag regression analysis in the time domain; and 3) a regression analysis in the frequency domain. The major results were that, for data set 1, the three methods constructed the original EEG equally well. With data set 2, reconstruction of the original EEG was achieved reasonably well with the frequency domain method and the time domain multiple‐lag method, but not with simple time domain regression. These three correction procedures were also applied to real data, consisting of concomitantly recorded EEG and high‐variance EOG series. No appreciable differences in outcome of the three methods were observed, and estimated transfer parameters suggested that these data were marked by weak frequency dependence only, which can be accounted for by simple time domain regression (and also by the other two methods).