High-density surface EMG decomposition based on a convolutive blind source separation approach.

Xiangjun Zhu, Yingchun Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

A novel automatic approach is developed in the present study to decompose high density surface electromyography (EMG) signals into motor unit (MU) firing patterns. The observed surface EMG signals are first modeled as a convolutive mixture of active MU sources. Contrast function maximization is employed to extract the first source, and separation of other sources is then carried out by an iterative deflation approach. Each extracted source is further processed and verified with the characteristics of motor unit action potential and firing patterns. The performance of the proposed automatic approach is evaluated in well-designed computer simulation. Results show that 4.7±0.5 and 7.1±0.6 MUs were correctly identified in the case of 5 and 10 active MUs respectively.

Original languageEnglish (US)
Title of host publicationProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Pages609-612
Number of pages4
Volume2012
DOIs
StatePublished - 2012
Event34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012 - San Diego, CA, United States
Duration: Aug 28 2012Sep 1 2012

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Other

Other34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012
CountryUnited States
CitySan Diego, CA
Period8/28/129/1/12

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics

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