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Low-amplitude craniofacial EMG power spectral density and 3D muscle reconstruction from MRI
Author(s) -
Lukas Wiedemann,
Jana Chaberova,
Kyle J. Edmunds,
Guðrún Einarsdóttir,
Ceon Ramon,
Paolo Gargiulo
Publication year - 2015
Publication title -
european journal of translational myology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.271
H-Index - 6
eISSN - 2037-7460
pISSN - 2037-7452
DOI - 10.4081/ejtm.2015.4886
Subject(s) - craniofacial , electroencephalography , signal (programming language) , computer science , amplitude , signal processing , artificial intelligence , segmentation , electromyography , low frequency , pattern recognition (psychology) , acoustics , computer vision , physics , psychology , neuroscience , telecommunications , radar , quantum mechanics , psychiatry , programming language
To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked Files.\udThis article is open access.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

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