Literature DB >> 21566274

Surface electromyogram analysis of the direction of isometric torque generation by the first dorsal interosseous muscle.

Ping Zhou1, Nina L Suresh, William Zev Rymer.   

Abstract

The objective of this study was to determine whether a novel technique using high density surface electromyogram (EMG) recordings can be used to detect the directional dependence of muscle activity in a multifunctional muscle, the first dorsal interosseous (FDI). We used surface EMG recordings with a two-dimensional electrode array to search for inhomogeneous FDI activation patterns with changing torque direction at the metacarpophalangeal joint, the locus of action of the FDI muscle. The interference EMG distribution across the whole FDI muscle was recorded during isometric contraction at the same force magnitude in five different directions in the index finger abduction-flexion plane. The electrode array EMG activity was characterized by contour plots, interpolating the EMG amplitude between electrode sites. Across all subjects the amplitude of the flexion EMG was consistently lower than that of the abduction EMG at the given force. Pattern recognition methods were used to discriminate the isometric muscle contraction tasks with a linear discriminant analysis classifier, based on the extraction of two different feature sets of the surface EMG signal: the time domain (TD) feature set and a combination of autoregressive coefficients and the root mean square amplitude (AR+RMS) as a feature set. We found that high accuracies were obtained in the classification of different directions of the FDI muscle isometric contraction. With a monopolar electrode configuration, the average overall classification accuracy from nine subjects was 94.1 ± 2.3% for the TD feature set and 95.8 ± 1.5% for the AR+RMS feature set. Spatial filtering of the signal with bipolar electrode configuration improved the average overall classification accuracy to 96.7 ± 2.7% for the TD feature set and 98.1 ± 1.6% for the AR+RMS feature set. The distinct EMG contour plots and the high classification accuracies obtained from this study confirm distinct interference EMG pattern distributions as a function of task direction, suggesting that high density surface EMG is a useful tool for understanding the activation of multifunctional muscles.

Mesh:

Year:  2011        PMID: 21566274     DOI: 10.1088/1741-2560/8/3/036028

Source DB:  PubMed          Journal:  J Neural Eng        ISSN: 1741-2552            Impact factor:   5.379


  9 in total

1.  Uneven spatial distribution of surface EMG: what does it mean?

Authors:  Alessio Gallina; Roberto Merletti; Marco Gazzoni
Journal:  Eur J Appl Physiol       Date:  2012-09-23       Impact factor: 3.078

2.  Computing motor unit number index of the first dorsal interosseous muscle with two different contraction tasks.

Authors:  Ping Zhou; Xiaoyan Li; William Zev Rymer
Journal:  Med Eng Phys       Date:  2012-07-18       Impact factor: 2.242

3.  Task-related variations in the surface EMG of the human first dorsal interosseous muscle.

Authors:  Maureen Whitford; Carl G Kukulka
Journal:  Exp Brain Res       Date:  2011-10-01       Impact factor: 1.972

4.  A novel myoelectric pattern recognition strategy for hand function restoration after incomplete cervical spinal cord injury.

Authors:  Jie Liu; Ping Zhou
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2012-09-27       Impact factor: 3.802

5.  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

6.  Alterations in multidimensional motor unit number index of hand muscles after incomplete cervical spinal cord injury.

Authors:  Le Li; Xiaoyan Li; Jie Liu; Ping Zhou
Journal:  Front Hum Neurosci       Date:  2015-05-08       Impact factor: 3.169

7.  Multichannel Surface EMG Decomposition Based on Measurement Correlation and LMMSE.

Authors:  Yong Ning; Yuming Zhao; Akbarjon Juraboev; Ping Tan; Jin Ding; Jinbao He
Journal:  J Healthc Eng       Date:  2018-06-28       Impact factor: 2.682

8.  Evaluating Muscle Synergies With EMG Data and Physics Simulation in the Neurorobotics Platform.

Authors:  Benedikt Feldotto; Cristian Soare; Alois Knoll; Piyanee Sriya; Sarah Astill; Marc de Kamps; Samit Chakrabarty
Journal:  Front Neurorobot       Date:  2022-07-12       Impact factor: 3.493

9.  Duration of observation required in detecting fasciculation potentials in amyotrophic lateral sclerosis using high-density surface EMG.

Authors:  Ping Zhou; Xiaoyan Li; Faezeh Jahanmiri-Nezhad; William Zev Rymer; Paul E Barkhaus
Journal:  J Neuroeng Rehabil       Date:  2012-10-10       Impact factor: 4.262

  9 in total

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