Open AccessManaging electromyogram contamination in scalp recordings: An approach identifying reliable beta and gamma EEG features of psychoses or other disorders
Author(s) -
Pope Kenneth J.,
Lewis Trent W.,
Fitzgibbon Sean P.,
Janani Azin S.,
Grummett Tyler S.,
Williams Patricia A. H.,
Battersby Malcolm,
Bastiampillai Tarun,
Whitham Emma M.,
Willoughby John O.
Publication year - 2022
Publication title -
brain and behavior
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.915
H-Index - 41
ISSN - 2162-3279
DOI - 10.1002/brb3.2721
Subject(s) - electroencephalography , scalp , contamination , audiology , electromyography , physical medicine and rehabilitation , computer science , medicine , neuroscience , psychology , surgery , biology , ecology
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.