A Windowed Eigenspectrum Method for Multivariate sEMG Classification During Reaching Movements

In this letter, we propose an eigenspectra-based feature extraction technique for classification of multivariate surface electromyographic (sEMG) recordings. The proposed method exploits the maximum eigenvalue vectors of the time-varying covariance patterns between sEMG channels. Together with a support vector machine (SVM) classifier, the proposed feature extraction technique is shown to be more reliable and robust, and it enhances classification between stroke and normal subjects, compared to the conventional univariate analysis methods that examine each muscle individually. In addition, analysis results show that the spatial whitening operation enhances the discriminability of eigenspectral features. This simple, easily-implemented, biologically-inspired approach is able to succinctly capture the subtle differences in muscle recruitment patterns between healthy and disease states. It appears to be a promising means to monitor motor performance in disease subjects.