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Low-amplitude craniofacial EMG power spectral density and 3D muscle reconstruction from MRI

Lukas Wiedemann, Jana Chaberova, Kyle Edmunds, Guðrún Einarsdóttir, Ceon Ramon, Paolo Gargiulo
  • Lukas Wiedemann
    Institute for Biomedical and Neural Engineering, Háskólinn í Reykjavík, Iceland; University of Applied Sciences, Höchstädtplatz 6, 1200 Wien, Austria
  • Jana Chaberova
    Institute for Biomedical and Neural Engineering, Háskólinn í Reykjavík, Iceland; Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic
  • Kyle Edmunds
    Institute for Biomedical and Neural Engineering, Háskólinn í Reykjavík, Iceland
  • Guðrún Einarsdóttir
    Institute for Biomedical and Neural Engineering, Háskólinn í Reykjavík, Iceland
  • Ceon Ramon
    University of Washington, Seattle, United States
  • Paolo Gargiulo
    Institute for Biomedical and Neural Engineering, Háskólinn í Reykjavík; Landspítali, Norðurmýri, Reykjavík, Iceland | paologar@landspitali.is

Abstract

Improving EEG signal interpretation, specificity, and sensitivity is a primary focus of many current investigations, and the successful application of EEG signal processing methods requires a detailed knowledge of both the topography and frequency spectra of low-amplitude, high-frequency craniofacial EMG. This information remains limited in clinical research, and as such, there is no known reliable technique for the removal of these artifacts from EEG data. The results presented herein outline a preliminary investigation of craniofacial EMG high-frequency spectra and 3D MRI segmentation that offers insight into the development of an anatomically-realistic model for characterizing these effects. The data presented highlights the potential for confounding signal contribution from around 60 to 200 Hz, when observed in frequency space, from both low and high-amplitude EMG signals. This range directly overlaps that of both low γ (30-50 Hz) and high γ (50-80 Hz) waves, as defined traditionally in standatrd EEG measurements, and mainly with waves presented in dense-array EEG recordings. Likewise, average EMG amplitude comparisons from each condition highlights the similarities in signal contribution of low-activity muscular movements and resting, control conditions. In addition to the FFT analysis performed, 3D segmentation and reconstruction of the craniofacial muscles whose EMG signals were measured was successful. This recapitulation of the relevant EMG morphology is a crucial first step in developing an anatomical model for the isolation and removal of confounding low-amplitude craniofacial EMG signals from EEG data. Such a model may be eventually applied in a clinical setting to ultimately help to extend the use of EEG in various clinical roles.

Keywords

EEG, EMG, Signal Contamination, Anatomical Modeling

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Submitted: 2014-12-09 12:14:53
Published: 2015-03-11 00:00:00
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Copyright (c) 2015 Lukas Wiedemann, Jana Chaberova, Kyle Edmunds, Guðrún Einarsdóttir, Ceon Ramon, Paolo Gargiulo

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