A framework for evaluating ICA methods of artifact removal from multichannel EEG

Kevin A. Glass, Gwen A. Frishkoff, Robert M. Frank, Colin Davey, Joseph Dien, Allen D. Malony, Don M. Tucker

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

We present a method for evaluating ICA separation of artifacts from EEG (electroencephalographic) data. Two algorithms, Infomax and FastICA, were applied to "synthetic data," created by superimposing simulated blinks on a blink-free EEG. To examine sensitivity to different data characteristics, multiple datasets were constructed by varying properties of the simulated blinks. ICA was used to decompose the data, and each source was cross-correlated with a blink template. Different thresholds for correlation were used to assess stability of the algorithms. When a match between the blink-template and a component was obtained, the contribution of the source was subtracted from the EEG. Since the original data were known a priori to be blink-free, it was possible to compute the correlation between these "baseline" data and the results of different decompositions. By averaging the filtered data, time-locked to the simulated blinks, we illustrate effects of different outcomes for EEG waveform and topographic analysis.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsCarlos G. Puntonet, Alberto Prieto
PublisherSpringer Verlag
Pages1033-1040
Number of pages8
ISBN (Electronic)3540230564, 9783540230564
DOIs
StatePublished - 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3195
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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    Glass, K. A., Frishkoff, G. A., Frank, R. M., Davey, C., Dien, J., Malony, A. D., & Tucker, D. M. (2004). A framework for evaluating ICA methods of artifact removal from multichannel EEG. In C. G. Puntonet, & A. Prieto (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 1033-1040). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3195). Springer Verlag. https://doi.org/10.1007/978-3-540-30110-3_130