9
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

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

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          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.

          Related collections

          Most cited references22

          • Record: found
          • Abstract: found
          • Article: not found

          Removing electroencephalographic artifacts by blind source separation.

          Eye movements, eye blinks, cardiac signals, muscle noise, and line noise present serious problems for electroencephalographic (EEG) interpretation and analysis when rejecting contaminated EEG segments results in an unacceptable data loss. Many methods have been proposed to remove artifacts from EEG recordings, especially those arising from eye movements and blinks. Often regression in the time or frequency domain is performed on parallel EEG and electrooculographic (EOG) recordings to derive parameters characterizing the appearance and spread of EOG artifacts in the EEG channels. Because EEG and ocular activity mix bidirectionally, regressing out eye artifacts inevitably involves subtracting relevant EEG signals from each record as well. Regression methods become even more problematic when a good regressing channel is not available for each artifact source, as in the case of muscle artifacts. Use of principal component analysis (PCA) has been proposed to remove eye artifacts from multichannel EEG. However, PCA cannot completely separate eye artifacts from brain signals, especially when they have comparable amplitudes. Here, we propose a new and generally applicable method for removing a wide variety of artifacts from EEG records based on blind source separation by independent component analysis (ICA). Our results on EEG data collected from normal and autistic subjects show that ICA can effectively detect, separate, and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably with those obtained using regression and PCA methods. ICA can also be used to analyze blink-related brain activity.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            High-frequency brain activity and muscle artifacts in MEG/EEG: a review and recommendations

            In recent years high-frequency brain activity in the gamma-frequency band (30–80 Hz) and above has become the focus of a growing body of work in MEG/EEG research. Unfortunately, high-frequency neural activity overlaps entirely with the spectral bandwidth of muscle activity (~20–300 Hz). It is becoming appreciated that artifacts of muscle activity may contaminate a number of non-invasive reports of high-frequency activity. In this review, the spectral, spatial, and temporal characteristics of muscle artifacts are compared with those described (so far) for high-frequency neural activity. In addition, several of the techniques that are being developed to help suppress muscle artifacts in MEG/EEG are reviewed. Suggestions are made for the collection, analysis, and presentation of experimental data with the aim of reducing the number of publications in the future that may contain muscle artifacts.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Scalp electrical recording during paralysis: quantitative evidence that EEG frequencies above 20 Hz are contaminated by EMG.

              To identify the possible contribution of electromyogram (EMG) to scalp electroencephalogram (EEG) rhythms at rest and induced or evoked by cognitive tasks. Scalp EEG recordings were made on two subjects in presence and absence of complete neuromuscular blockade, sparing the dominant arm. The subjects undertook cognitive tasks in both states to allow direct comparison of electrical recordings. EEG rhythms in the paralysed state differed significantly compared with the unparalysed state, with 10- to 200-fold differences in the power of frequencies above 20 Hz during paralysis. Most of the scalp EEG recording above 20 Hz is of EMG origin. Previous studies measuring gamma EEG need to be re-evaluated. This has a significant impact on measurements of gamma rhythms from the scalp EEG in unparalysed humans. It is to be hoped that signal separation methods will be able to rectify this situation.
                Bookmark

                Author and article information

                Journal
                Eur J Transl Myol
                Eur J Transl Myol
                ejtm
                European Journal of Translational Myology
                PAGEPress Publications, Pavia, Italy
                2037-7452
                2037-7460
                11 March 2015
                11 March 2015
                : 25
                : 2
                : 4886
                Affiliations
                [1 ] Institute for Biomedical and Neural Engineering, Háskólinn í Reykjavík , Menntavegur 1, 101 Reykjavík, Iceland
                [2 ]University of Applied Sciences , Höchstädtplatz 6, 1200 Wien, Austria
                [3 ] Faculty of Electrical Engineering, Czech Technical University in Prague , Zikova 1903/4, 166 36 Praha 6, Czech Republic
                [4 ]University of Washington , 206M EEB, Seattle, WA 98195
                [5 ]Landspítali, Norðurmýri , 101 Reykjavík, Iceland
                Author notes
                Institute for Biomedical and Neural Engineering, Háskólinn í Reykjavík, Menntavegur 1-101 Reykjavík, Iceland Phone Office: 003545431533, Mobile: 003548245384 paologar@ 123456landspitali.is Lukas Wiedemann, lukas.wiedemann@ 123456hotmail.com Jana Chaberova, jana14@ 123456ru.is Kyle Edmunds, Kyle.Edmunds@ 123456Tufts.edu Guðrún Einarsdóttir, gudrun89@ 123456gmail.com Ceon Ramon, ceon@ 123456u.washington.edu
                Article
                10.4081/ejtm.2015.4886
                4749011
                26913150
                86a4d4f0-a588-444b-8975-c613c9ffb667
                Copyright @

                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.

                History
                : 09 December 2014
                : 07 January 2015
                Page count
                Figures: 4, Tables: 0, Equations: 0, References: 28, Pages: 7
                Categories
                Article

                eeg,emg,signal contamination,anatomical modeling
                eeg, emg, signal contamination, anatomical modeling

                Comments

                Comment on this article