Cortico-muscular coherence on artifact corrected EEG-EMG data recorded with a MRI scanner

  • Simultaneous recording of electroencephalogram (EEG) and electromyogram (EMG) with magnetic resonance imaging (MRI) provides great potential for studying human brain activity with high temporal and spatial resolution. But, due to the MRI, the recorded signals are contaminated with artifacts. The correction of these artifacts is important to use these signals for further spectral analysis. The coherence can reveal the cortical representation of peripheral muscle signal in particular motor tasks, e.g. finger movements. The artifact correction of these signals was done by two different algorithms the Brain vision analyzer (BVA) and the Matlab FMRIB plug-in for EEGLAB. The Welch periodogram method was used for estimating the cortico-muscular coherence. Our analysis revealed coherence with a frequency of 5Hz in the contralateral side of the brain. The entropy is estimated for the calculated coherence to get the distribution of coherence in the scalp. The significance of the paper is toSimultaneous recording of electroencephalogram (EEG) and electromyogram (EMG) with magnetic resonance imaging (MRI) provides great potential for studying human brain activity with high temporal and spatial resolution. But, due to the MRI, the recorded signals are contaminated with artifacts. The correction of these artifacts is important to use these signals for further spectral analysis. The coherence can reveal the cortical representation of peripheral muscle signal in particular motor tasks, e.g. finger movements. The artifact correction of these signals was done by two different algorithms the Brain vision analyzer (BVA) and the Matlab FMRIB plug-in for EEGLAB. The Welch periodogram method was used for estimating the cortico-muscular coherence. Our analysis revealed coherence with a frequency of 5Hz in the contralateral side of the brain. The entropy is estimated for the calculated coherence to get the distribution of coherence in the scalp. The significance of the paper is to identify the optimal algorithm to rectify the MR artifacts and as a first step to use both these signals EEG and EMG in conjunction with MRI for further studies.show moreshow less

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Metadaten
Author:Muthuraman MuthuramanORCiDGND, Andreas Galka, V. N. Hong, U. Heute, Günther Deuschl, Jan Raethjen
URN:urn:nbn:de:bvb:384-opus4-1103373
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/110337
ISBN:978-1-4577-0216-7OPAC
Parent Title (English):2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 3-7 July 2013, Osaka, Japan
Publisher:IEEE
Place of publication:Piscataway, NJ
Editor:Yoshitaka Hirooka, Tomomi Ide, Takuya Kishi, Masaru Sugimachi
Type:Conference Proceeding
Language:English
Year of first Publication:2013
Publishing Institution:Universität Augsburg
Release Date:2023/12/21
First Page:4811
Last Page:4814
DOI:https://doi.org/10.1109/embc.2013.6610624
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Informatik / Professur für Informatik in der Medizintechnik
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):Deutsches Urheberrecht