Accounting for microsaccadic artifacts in the EEG using independent component analysis and beamforming

Matt Craddock, Jasna Martinovic, Matthias M. Müller

Research output: Contribution to journalArticle

12 Citations (Scopus)
11 Downloads (Pure)

Abstract

Neuronal activity in the gamma-band range was long considered a marker of object representation. However, scalp-recorded EEG activity in this range is contaminated by a miniature saccade-related muscle artifact. Independent component analysis (ICA) has been proposed as a method of removal of such artifacts. Alternatively, beamforming, a source analysis method in which potential sources of activity across the whole brain are scanned independently through the use of adaptive spatial filters, offers a promising method of accounting for the artifact without relying on its explicit removal. We present here the application of ICA-based correction to a previously published dataset. Then, using beamforming, we examine the effect of ICA correction on the scalp-recorded EEG signal and the extent to which genuine activity is recoverable before and after ICA correction. We find that beamforming attributes much of the scalp-recorded gamma-band signal before correction to deep frontal sources, likely the eye muscles, which generate the artifact related to each miniature saccade. Beamforming confirms that what is removed by ICA is predominantly this artifactual signal, and that what remains after correction plausibly originates in the visual cortex. Thus, beamforming allows researchers to confirm whether their removal procedures successfully removed the artifact. Our results demonstrate that ICA-based correction brings about general improvements in signal-to-noise ratio suggesting it should be used along with, rather than be replaced by, beamforming.

Original languageEnglish
Pages (from-to)553-565
Number of pages13
JournalPsychophysiology
Volume53
Issue number4
Early online date4 Dec 2015
DOIs
Publication statusPublished - Apr 2016

Keywords

  • EEG
  • oscillation/time frequency analyses
  • visual processes

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