Surface Electromyogram Signal Classification Using Higher Order Statistics

Nazarpour, Kianoush; Sharafat, Ahmad Reza; Firouzabadi, Seyed Mohammad

doi:10.22041/ijbme.2007.13498

Document Type : Full Research Paper

Authors

¹ Assistant Professor, Brain Behavioral Science Group, University of Birmingham

² Professor, School of Electrical and Computer Engineering Tarbiat Modares University

³ Associate Professor, Biophysics Group, School of Medicine, Tarbiat Modares University

10.22041/ijbme.2007.13498

Abstract

A novel approach to surface electromyogram (sEMG) signal classification using its higher order statistics (HOS) is presented in this study. As the probability density function of the sEMG during isometric contraction in some cases is very close to the Gaussian distribution, it is frequently assumed to be Gaussian. As this assumption is not valid when the force is small, in this paper, we consider the non-Gaussian characteristics of the sEMG, and compute the second-, the third- and the fourth order statistics of the sEMG as its features. These features are used to classify four upper limb primitive motions, i.e., elbow flexion (EF), elbow extension (EE), forearm supination (FS), and forearm pronation (FP). We used the sequential forward selection (SFS) method to reduce the number of HOS features to a sufficient minimum while retaining their discriminatory information, and apply the Knearest neighbor method for classification. Our approach is robust against statistical variations in noise, and does not require additional computations compared to existing methods for providing high rates of correct classification of the sEMG, which makes it useful in devising real-time sEMG controlled prostheses.

Keywords

Main Subjects

Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing

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Iranian Journal of Biomedical Engineering

Surface Electromyogram Signal Classification Using Higher Order Statistics

References

References

Volume 1, Issue 3 - Serial Number 3
June 2007
Pages 189-199

Surface Electromyogram Signal Classification Using Higher Order Statistics

References

References

Volume 1, Issue 3 - Serial Number 3June 2007Pages 189-199

Volume 1, Issue 3 - Serial Number 3
June 2007
Pages 189-199