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bioRxiv · 10.1101/2020.11.23.393702

A Novel Tool for the Removal of Muscle Artefacts from EEG: Improving Data Quality in the Gamma Frequency Range

Liebisch, A. P.·Eggert, T.·Shindy, A.·Valentini, E.·Irving, S.·Stankewitz, A.·Schulz, E.
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Abstract

BackgroundThe past two decades have seen a particular focus towards high-frequency neural activity in the gamma band (>30Hz). However, gamma band activity shares frequency range with unwanted artefacts from muscular activity. New MethodWe developed a novel approach to remove muscle artefacts from neurophysiological data. We re-analysed existing EEG data that were decomposed by a blind source separation method (independent component analysis, ICA), which helped to better spatially and temporally separate single muscle spikes. We then applied an adapting algorithm that detects these singled-out muscle spikes. ResultsWe obtained data almost free from muscle artefacts; we needed to remove significantly fewer artefact components from the ICA and we included more trials for the statistical analysis compared to standard ICA artefact removal. All pain-related cortical effects in the gamma band have been preserved, which underlines the high efficacy and precision of this algorithm. ConclusionsOur results show a significant improvement of data quality by preserving task-relevant gamma oscillations of cortical origin. We were able to precisely detect, gauge, and carve out single muscle spikes from the time course of neurophysiological measures. We advocate the application of the tool for studies investigating gamma activity that contain a rather low number of trials, as well as for data that are highly contaminated with muscle artefacts. This validation of our tool allows for the application on event-free continuous EEG, for which the artefact removal is more challenging.

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Liebisch, A. P., Eggert, T., Shindy, A., Valentini, E., Irving, S., Stankewitz, A., Schulz, E.. 2020-11-23. A Novel Tool for the Removal of Muscle Artefacts from EEG: Improving Data Quality in the Gamma Frequency Range. https://doi.org/10.1101/2020.11.23.393702

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