A Novel Channel Selection Method Based on Partial KL Information Measure for EMG-based Motion Classification

Abstract

To control machines using electromyograms (EMGs), subjects' intentions have to be correctly estimated and classified. However, the accuracy of classification is greatly influenced by individual physical abilities and measuring positions, making it necessary to select optimal channel positions for each subject. This paper proposes a novel online channel selection method using probabilistic neural networks (PNNs). In this method, measured data are regarded as probability variables, and data dimensions are evaluated by a partial KL information measure that is newly defined as a metric of effective dimensions. In the experiments, channels were selected using this method, and EMGs measured from the forearm were classified. The results showed that the number of channels is reduced with 33.33 ± 11.8%, and the average classification rate using the selected channels is almost the same as that using all channels. This demonstrates that the method is capable of selecting effective channels for classification.