A novel tool for the removal of muscle artefacts from EEG: Improving data quality in the gamma frequency range.
Gamma activity
High-frequency neuronal oscillations
Muscle artefacts
Journal
Journal of neuroscience methods
ISSN: 1872-678X
Titre abrégé: J Neurosci Methods
Pays: Netherlands
ID NLM: 7905558
Informations de publication
Date de publication:
01 07 2021
01 07 2021
Historique:
received:
25
11
2020
revised:
07
04
2021
accepted:
02
05
2021
pubmed:
9
5
2021
medline:
1
7
2021
entrez:
8
5
2021
Statut:
ppublish
Résumé
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 cortical gamma. 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.
Sections du résumé
BACKGROUND
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.
NEW METHOD
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.
RESULTS
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.
CONCLUSIONS
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 cortical gamma. 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.
Identifiants
pubmed: 33964345
pii: S0165-0270(21)00152-7
doi: 10.1016/j.jneumeth.2021.109217
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
109217Informations de copyright
Copyright © 2021 Elsevier B.V. All rights reserved.