A Time-Varying Eigenspectrum/SVM Method for Semg Classification of Reaching Movements in Healthy and Stroke Subjects

TitleA Time-Varying Eigenspectrum/SVM Method for Semg Classification of Reaching Movements in Healthy and Stroke Subjects
Publication TypeConference Paper
Year of Publication2006
AuthorsChiang, J., Z. J. Wang, and M. J. McKeown
Conference NameAcoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on
PaginationII -II
Date Publishedmay.
Keywordsbiologically-inspired approach, eigenspectral feature vector, eigenvalues and eigenfunctions, electromyography, healthy subjects, medical signal processing, pattern classification, reaching movements, sEMG classification, sEMG muscle channels, signal classification, stroke subjects, support vector machines, SVM classifier, SVM method, time-varying covariance patterns, time-varying eigenspectrum
Abstract

A method for classification of sEMG recordings based on the time-varying covariance patterns between sEMG muscle channels is proposed. The proposed eigenspectral feature vector appears to enhance classification of sEMG patterns with an SVM classifier. The method is shown to be more reliable, robust and enhances classification between stroke and normal subjects, compared to standard analysis methods that examine each muscle individually. This simple, easily-implemented, biologically-inspired approach appears to be a promising means to monitor motor performance in healthy and disease subjects

URLhttp://dx.doi.org/10.1109/ICASSP.2006.1660561
DOI10.1109/ICASSP.2006.1660561

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