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

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