Literature DB >> 16904342

Using two-dimensional spatial information in decomposition of surface EMG signals.

Bert U Kleine1, Johannes P van Dijk, Bernd G Lapatki, Machiel J Zwarts, Dick F Stegeman.   

Abstract

Recently, high-density surface EMG electrode grids and multi-channel amplifiers became available for non-invasive recording of human motor units (MUs). We present a way to decompose surface EMG signals into MU firing patterns, whereby we concentrate on the importance of two-dimensional spatial differences between the MU action potentials (MUAPs). Our method is exemplified with high-density EMG data from the vastus lateralis muscle of a single subject. Bipolar and Laplacian spatial filtering was applied to the monopolar raw signals. From the single recording in this subject six different simultaneously active MUs could be distinguished using the spatial differences between MUAPs in the direction perpendicular to the muscle fiber direction. After spike-triggered averaging, 125-channel two-dimensional MUAP templates were obtained. Template-matching allowed tracking of all MU firings. The impact of spatial information was measured by using subsets of the MUAP templates, either in parallel or perpendicular to the muscle fiber direction. The use of one-dimensional spatial information perpendicular to the muscle fiber direction was superior to the use of a linear array electrode in the longitudinal direction. However, to detect the firing events of the MUs with a high accuracy, as needed for instance for estimation of firing synchrony, two-dimensional information from the complete grid electrode appears essential.

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Year:  2006        PMID: 16904342     DOI: 10.1016/j.jelekin.2006.05.003

Source DB:  PubMed          Journal:  J Electromyogr Kinesiol        ISSN: 1050-6411            Impact factor:   2.368


  29 in total

1.  Rigorous a posteriori assessment of accuracy in EMG decomposition.

Authors:  Kevin C McGill; Hamid R Marateb
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2010-07-15       Impact factor: 3.802

2.  Spatial variability in cortex-muscle coherence investigated with magnetoencephalography and high-density surface electromyography.

Authors:  Harri Piitulainen; Alberto Botter; Mathieu Bourguignon; Veikko Jousmäki; Riitta Hari
Journal:  J Neurophysiol       Date:  2015-09-09       Impact factor: 2.714

3.  Detecting the unique representation of motor-unit action potentials in the surface electromyogram.

Authors:  Dario Farina; Francesco Negro; Marco Gazzoni; Roger M Enoka
Journal:  J Neurophysiol       Date:  2008-05-21       Impact factor: 2.714

4.  Estimating reflex responses in large populations of motor units by decomposition of the high-density surface electromyogram.

Authors:  Utku Ş Yavuz; Francesco Negro; Oğuz Sebik; Aleŝ Holobar; Cornelius Frömmel; Kemal S Türker; Dario Farina
Journal:  J Physiol       Date:  2015-08-02       Impact factor: 5.182

5.  A new and fast approach towards sEMG decomposition.

Authors:  Ivan Gligorijević; Johannes P van Dijk; Bogdan Mijović; Sabine Van Huffel; Joleen H Blok; Maarten De Vos
Journal:  Med Biol Eng Comput       Date:  2013-01-18       Impact factor: 2.602

Review 6.  The extraction of neural strategies from the surface EMG: an update.

Authors:  Dario Farina; Roberto Merletti; Roger M Enoka
Journal:  J Appl Physiol (1985)       Date:  2014-10-02

7.  Denoising of HD-sEMG signals using canonical correlation analysis.

Authors:  M Al Harrach; S Boudaoud; M Hassan; F S Ayachi; D Gamet; J F Grosset; F Marin
Journal:  Med Biol Eng Comput       Date:  2016-05-25       Impact factor: 2.602

8.  Electrophysiological method to examine muscle fiber architecture in the upper lip in cleft-lip patients.

Authors:  Johanna Radeke; Johannes Peter van Dijk; Ales Holobar; Bernd Georg Lapatki
Journal:  J Orofac Orthop       Date:  2014-01-19       Impact factor: 1.938

9.  Surface EMG decomposition based on K-means clustering and convolution kernel compensation.

Authors:  Yong Ning; Xiangjun Zhu; Shanan Zhu; Yingchun Zhang
Journal:  IEEE J Biomed Health Inform       Date:  2014-06-02       Impact factor: 5.772

10.  Automatic classification of motor unit potentials in surface EMG recorded from thenar muscles paralyzed by spinal cord injury.

Authors:  Jeffrey Winslow; Marine Dididze; Christine K Thomas
Journal:  J Neurosci Methods       Date:  2009-09-15       Impact factor: 2.390

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