A novel tool for the removal of muscle artefacts from EEG: Improving data quality in the gamma frequency range.

Journal of Neuroscience Methods
A. P. LiebischEnrico Schulz

Abstract

The past two decades have seen a particular focus towards high-frequency neural activity in the gamma band (>30 Hz). However, gamma band activity shares frequency range with unwanted artefacts from muscular activity. We 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. We 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. Our results show a significant improvement of data quality by preserving task-relevant gamma oscillations of presumed cortical origin. We were able to precisely detect, gauge, and carve out single muscle spikes from the time course of neurophysiological measures without perturbing cor...Continue Reading

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