Multi-channel surface EMG classification based on a quasi-optimal selection of motions and channels

This paper introduces a motion and channel selection method based on a partial Kullback-Leibler (KL) information measure. In the proposed method, the probability density functions of recorded data are estimated through learning involving a probabilistic neural network based on the KL information theory. Partial KL information is defined to support evaluation of the contribution of each dimension and class for classification. Effective dimensions and classes can then be selected by eliminating ineffective choices one by one based on this information, respectively. In the experiments, effective channels for classification were first selected for each of the six subjects, and the number of channels was reduced by 32.1 ± 25.5%. After channel selection, appropriate motions for classification were chosen, and the average classification rate for the motions selected using the proposed method was found to be 91.7 ± 2.5%. These outcomes indicate that the proposed method can be used to select effective channels and motions for accurate classification.