General Linear Model (GLM)
One tool for correcting EMG artifacts involves using variants of the GLM (e.g., regression, ANCOVA) to remove variance in a neurogenic band of interest (e.g., alpha) that is predicted by activity in an EMG (e.g., 70–80Hz) band. The advantage of this technique is that it is automatic and, by performing separate corrections at each site, can accommodate anatomical variation. Also, it does not require dedicated EMG channels. While useful for ongoing and induced EEG investigations (Lutz, Greischar, Rawlings, Ricard, and Davidson 2004
), it does not allow time-series reconstruction, limiting its usefulness for event-related spectral perturbation (ERSP) analyses.
We recently tested this technique’s validity using scripted data (McMenamin et al. in press
). High-density EEG data (125-channel; n
=17) were acquired while neurogenic and myogenic activation was independently varied by crossing an alpha-blocking manipulation (i.e., eyes opened/closed) with low-intensity muscle activation (i.e., tensing/quiescence). Gross artifacts were rejected before correction, yielding a more realistic degree of contamination (). Inspection of the uncorrected data revealed that the peak and topography of the alpha-blocking contrast was markedly disturbed when changes in EMG and EEG covaried ().
We then examined the sensitivity and specificity of inter
- and intra
-individual variants of the GLM technique for correcting alpha-band neurogenic activity. Sensitivity was quantified by comparing “corrected” EMG-contaminated data to uncorrected EMG-free data in an anterior, myogenic region of interest (ROI). Equivalence tests (Seaman and Serlin 1998
) were used as a follow-up. Specificity was similarly quantified using a posterior, neurogenic (i.e., alpha-blocking) ROI.
Results showed that only intra-individual correction, which models correlations between EEG and EMG bands across 1.024-s segments separately for each participant (dfObserved = number of segments - 2), showed adequate performance (). Unfortunately, parallel analyses on source-localized data indicated that none of the GLM-based techniques, when applied in a voxelwise manner, adequately corrected the data.
Independent Component Analysis (ICA)
A second tool for correcting EMG artifacts exploits ICA (Delorme et al. 2007
) to blindly separate each individual’s EEG into temporally independent components. Using manual or algorithmic classification (ibid; Fitzgibbon et al. 2007
; Mammone and Morabito in press
), some components are classified as EMG and discarded prior to reconstruction of the corrected time-series. Typically, this requires inspection of as many components as Channels × Participants, although principal components analysis or other means (Li, Adal, and Calhoun 2008
) can be used to reduce dimensionality prior to ICA (Hu et al. 2005
). To date, ICA has been subjected to only modest attempts, using simulated or ad hoc
data, at validation. Moreover, few investigators provide detailed descriptions of their IC classification protocol or estimates of inter-rater reliability (Fatourechi, Bashashati, Ward, and Birch 2007
Superficially, the question of classifying an IC as EMG appears trivial—it is whatever “looks like EMG” in the temporal (“fuzzy”-looking traces), anatomical (broad fringe/rim distribution; isolated channel, suggesting superficial source), and spectral (broad, high-frequency peak; accelerating/flat spectrum) domains. But in our own experience, this question is of the utmost importance (cf. Fitzgibbon et al. 2007
To illustrate this problem, we describe an ad hoc validation test conducted on unpublished 105-channel ERSP data time-locked to 100ms face presentations. The first 40 independent components (92% variance) were manually classified as (i) EEG, (ii) “pure” EMG, (iii) a mixture, or (iv) non-EMG artifacts (). Correction was performed twice: first removing only non-EMG artifacts, and then removing both non-EMG and pure EMG artifacts (i.e., mixed EMG/EEG components were retained).
Because EMG was not explicitly manipulated in this experiment, validation required another means of identifying data contaminated by greater or lesser amounts of myogenic activity. Accordingly, we exploited individual differences in EMG to create two groups of participants. The high-EMG group (n=22) displayed two or more pure EMG components, whereas the low-EMG group (n=24) displayed one or none. Consistent with expectation, confirmatory analyses indicated that the high-EMG group showed more EMG contamination before ICA correction. Specifically, the cumulative variance predicted by the pure EMG components was greater for the high-EMG (Ms=0.48%, 2%) than the low-EMG group (Ms=0.19%, 0.19%), ps<.001. This effect was specific to the pure EMG components; the variance predicted by components classified as non-EMG artifact (p=.99) and mixed EMG/EEG (p=.12) did not differ. Not surprisingly, the Group × Component-Type (Pure EMG, Mixed EMG/EEG) contrast was also significant, p<.001. As a coarse test of ICA’s sensitivity, we then performed ERSP analyses comparing activity in the gamma band (25–50Hz) across groups. Notably, this revealed that the high-EMG group displayed greater power () over lateral-frontal sites (~300–400ms) before and after ICA-based EMG correction.
Collectively, our results suggest that the group difference in gamma band ERSP activity following correction reflects residual EMG artifact. Two mechanisms, both entailing inadequate separation of EMG from EEG, might account for this. It could be that undercorrection reflects the decision to retain mixed EMG/EEG components, which non-significantly accounted for one-third more variance in the high-EMG (M=2.16%) than low-EMG group (M=1.63%). Alternatively, our putatively pure EEG components may have contained subtle EMG.
These observations raise several important questions. In particular, the optimal number of components to extract and classify remains unknown. This decision is likely to have a marked impact on the quality of source separation. Likewise, the appropriate classification of components containing varying ratios of neurogenic and myogenic activity, and the impact of removing such mixed-source components on sensitivity and specificity is unresolved. We (McMenamin, Shackman, Maxwell, Bachhuber, Koppenhaver, Greischar and Davidson, in preparation
) are currently pursuing the answers to these questions by pairing the data and validation methods of McMenamin et al. (in press)
with ICA-based source separation.
For now, our preliminary findings and other investigators’ work (Fitzgibbon et al. 2007
) demonstrate that ICA does not necessarily provide adequate protection against EMG artifact. They also highlight the substantial impact that the choice of component classification and rejection protocol can have on inferential validity.