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Low-Amplitude Craniofacial EMG Power Spectral Density and 3D Muscle Reconstruction from MRI

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 craniofacia...

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Autores principales: Wiedemann, Lukas, Chaberova, Jana, Edmunds, Kyle, Einarsdóttir, Guðrún, Ramon, Ceon, Gargiulo, Paolo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PAGEPress Publications, Pavia, Italy 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4749011/
https://www.ncbi.nlm.nih.gov/pubmed/26913150
http://dx.doi.org/10.4081/ejtm.2015.4886
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author Wiedemann, Lukas
Chaberova, Jana
Edmunds, Kyle
Einarsdóttir, Guðrún
Ramon, Ceon
Gargiulo, Paolo
author_facet Wiedemann, Lukas
Chaberova, Jana
Edmunds, Kyle
Einarsdóttir, Guðrún
Ramon, Ceon
Gargiulo, Paolo
author_sort Wiedemann, Lukas
collection PubMed
description 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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spelling pubmed-47490112016-02-24 Low-Amplitude Craniofacial EMG Power Spectral Density and 3D Muscle Reconstruction from MRI Wiedemann, Lukas Chaberova, Jana Edmunds, Kyle Einarsdóttir, Guðrún Ramon, Ceon Gargiulo, Paolo Eur J Transl Myol Article 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. PAGEPress Publications, Pavia, Italy 2015-03-11 /pmc/articles/PMC4749011/ /pubmed/26913150 http://dx.doi.org/10.4081/ejtm.2015.4886 Text en http://creativecommons.org/licenses/by-nc/3.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Article
Wiedemann, Lukas
Chaberova, Jana
Edmunds, Kyle
Einarsdóttir, Guðrún
Ramon, Ceon
Gargiulo, Paolo
Low-Amplitude Craniofacial EMG Power Spectral Density and 3D Muscle Reconstruction from MRI
title Low-Amplitude Craniofacial EMG Power Spectral Density and 3D Muscle Reconstruction from MRI
title_full Low-Amplitude Craniofacial EMG Power Spectral Density and 3D Muscle Reconstruction from MRI
title_fullStr Low-Amplitude Craniofacial EMG Power Spectral Density and 3D Muscle Reconstruction from MRI
title_full_unstemmed Low-Amplitude Craniofacial EMG Power Spectral Density and 3D Muscle Reconstruction from MRI
title_short Low-Amplitude Craniofacial EMG Power Spectral Density and 3D Muscle Reconstruction from MRI
title_sort low-amplitude craniofacial emg power spectral density and 3d muscle reconstruction from mri
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4749011/
https://www.ncbi.nlm.nih.gov/pubmed/26913150
http://dx.doi.org/10.4081/ejtm.2015.4886
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