Techniques of EMG signal analysis: Detection, processing, classification and applications

Md. Mamun Ibne Reaz, M. S. Hussain, F. Mohd-Yasin

Research output: Contribution to journalArticle

613 Citations (Scopus)

Abstract

Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. The purpose of this paper is to illustrate the various methodologies and algorithms for EMC signal analysis to provide efficient and effective ways of understanding the signal and its nature. We further point up some of the hardware implementations using EMG focusing on applications related to prosthetic hand control, grasp recognition, and human computer interaction. A comparison study is also given to show performance of various EMG signal analysis methods. This paper provides researchers a good understanding of EMG signal and its analysis procedures. This knowledge will help them develop more powerful, flexible, and efficient applications.

Original languageEnglish
Pages (from-to)11-35
Number of pages25
JournalBiological Procedures Online
Volume8
Issue number1
DOIs
Publication statusPublished - 23 Mar 2006
Externally publishedYes

Fingerprint

Electromyography
Signal analysis
Processing
Human computer interaction
Electromagnetic compatibility
Human Development
Hand Strength
Prosthetics
Computer hardware
Muscle
Hand
Research Personnel
Psychological Signal Detection
Decomposition
Hardware
Muscles

Keywords

  • Electromyography
  • Fourier analysis
  • Muscles
  • Nervous system

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)

Cite this

Techniques of EMG signal analysis : Detection, processing, classification and applications. / Ibne Reaz, Md. Mamun; Hussain, M. S.; Mohd-Yasin, F.

In: Biological Procedures Online, Vol. 8, No. 1, 23.03.2006, p. 11-35.

Research output: Contribution to journalArticle

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