Managing electromyogram contamination in scalp recordings: An approach identifying reliable beta and gamma EEG features of psychoses or other disorders

Kenneth J. Pope, Trent W. Lewis, Sean P. Fitzgibbon, Azin S. Janani, Tyler S. Grummett, Patricia A.H. Williams, Malcolm Battersby, Tarun Bastiampillai, Emma M. Whitham, John O. Willoughby

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
57 Downloads (Pure)

Abstract

Objective: In publications on the electroencephalographic (EEG) features of psychoses and other disorders, various methods are utilized to diminish electromyogram (EMG) contamination. The extent of residual EMG contamination using these methods has not been recognized. Here, we seek to emphasize the extent of residual EMG contamination of EEG. 

Methods: We compared scalp electrical recordings after applying different EMG-pruning methods with recordings of EMG-free data from 6 fully paralyzed healthy subjects. We calculated the ratio of the power of pruned, normal scalp electrical recordings in the six subjects, to the power of unpruned recordings in the same subjects when paralyzed. We produced “contamination graphs” for different pruning methods. 

Results: EMG contamination exceeds EEG signals progressively more as frequencies exceed 25 Hz and with distance from the vertex. In contrast, Laplacian signals are spared in central scalp areas, even to 100 Hz. 

Conclusion: Given probable EMG contamination of EEG in psychiatric and other studies, few findings on beta- or gamma-frequency power can be relied upon. Based on the effectiveness of current methods of EEG de-contamination, investigators should be able to reanalyze recorded data, reevaluate conclusions from high-frequency EEG data, and be aware of limitations of the methods.

Original languageEnglish
Article numbere2721
Number of pages11
JournalBrain and Behavior
Volume12
Issue number9
Early online date2 Aug 2022
DOIs
Publication statusPublished - Sept 2022

Keywords

  • EEG
  • EMG contamination
  • psychoses
  • spectral analysis

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