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Validation of regression‐based myogenic correction techniques for scalp and source‐localized EEG
Author(s) -
McMenamin Brenton W.,
Shackman Alexander J.,
Maxwell Jeffrey S.,
Greischar Lawrence L.,
Davidson Richard J.
Publication year - 2009
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.1469-8986.2009.00787.x
Subject(s) - scalp , electroencephalography , regression , psychology , pattern recognition (psychology) , audiology , regression analysis , speech recognition , artificial intelligence , computer science , statistics , mathematics , cognitive psychology , neuroscience , anatomy , medicine
EEG and EEG source‐estimation are susceptible to electromyographic artifacts (EMG) generated by the cranial muscles. EMG can mask genuine effects or masquerade as a legitimate effect—even in low frequencies, such as alpha (8–13 Hz). Although regression‐based correction has been used previously, only cursory attempts at validation exist, and the utility for source‐localized data is unknown. To address this, EEG was recorded from 17 participants while neurogenic and myogenic activity were factorially varied. We assessed the sensitivity and specificity of four regression‐based techniques: between‐subjects, between‐subjects using difference‐scores, within‐subjects condition‐wise, and within‐subject epoch‐wise on the scalp and in data modeled using the LORETA algorithm. Although within‐subject epoch‐wise showed superior performance on the scalp, no technique succeeded in the source‐space. Aside from validating the novel epoch‐wise methods on the scalp, we highlight methods requiring further development.