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. 2006:8:11-35.
doi: 10.1251/bpo115. Epub 2006 Mar 23.

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

M B I Raez  1 , M S Hussain, F Mohd-Yasin
Affiliations

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

M B I Raez et al. Biol Proced Online. 2006.

Erratum in

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 EMG 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.

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Figures

Fig. 1
Fig. 1. EMG signal and decomposition of MUAPs.
Fig. 2
Fig. 2. Comparison between Mexican hat wavelet and typical unipolar MUAP shape.
Fig. 3
Fig. 3. Block diagram of the experiment procedure.
Fig. 4
Fig. 4. A model of the LTI system.
Fig. 5
Fig. 5. Sample EMG signal and its bispectrum curve.
Fig. 6
Fig. 6. EMG classification strategy using ANN approach.
Fig. 7
Fig. 7. The digital gate based architecture of the prosthetic hand controller.
Fig. 8
Fig. 8. Schematics of the core processing unil implemented on FPGA.
Fig. 9
Fig. 9. System block diagram of "Muscleman."
Fig. 10
Fig. 10. Normalized Force / EMG signal relationship for three different muscles.
The data have been greatly smoothed, with a window width of 2 s. Note the difference in the linearity of the relationship among the muscles (78).
Fig. 11
Fig. 11. A diagrammatic explanation of the spectral modification which occurs in the EMG signal during sustained contractions.
The muscle fatigue index is represented by the median frequency of the spectrum (78).
See this image and copyright information in PMC

References

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