A Novel Feature Extraction Scheme for Myoelectric Signals Classification Using Higher Order Statistics

K. Nazarpour, A. R. Sharafat, S. M. Firoozabadi

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

Abstract

We present a novel feature extraction scheme for surface myoelectric signal (sMES) classification. We employ a multilayer perceptron (MLP) in which the feature vector is a mix of the second-, the third-, and the fourth order cumulants of the sMES stationary segments obtained from two recording channels. To reduce the number of features to a sufficient minimum, while retaining their discriminatory information, we employ the method of principle components analysis (PCA). The detected sMES is used to classify four upper limb primitive motions, i.e., elbow flexion (F), elbow extension (E), wrist supination (S), and wrist pronation (P). Simulation results indicate a substantial reduction in the required computations to achieve similar results as compared to existing methods
Original languageEnglish
Title of host publicationConference Proceedings. 2nd International IEEE EMBS Conference on Neural Engineering, 2005.
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages293-296
Number of pages4
ISBN (Print)0-7803-8710-4
DOIs
Publication statusPublished - 18 Apr 2005
Event2nd International IEEE EMBS Conference on Neural Engineering, 2005. - Arlington, United States
Duration: 16 Mar 200519 Mar 2005

Publication series

Name
PublisherIEEE
ISSN (Print)1948-3546
ISSN (Electronic)1948-3554

Conference

Conference2nd International IEEE EMBS Conference on Neural Engineering, 2005.
Country/TerritoryUnited States
CityArlington
Period16/03/0519/03/05

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