Improved EMG pattern recognition using the distribution plot of cepstrum

In this paper, a real-time on-line plot is developed which recognizes user motion using the first and second cepstral coefficients for pattern recognition of the electromyogram (EMG). The cepstral coefficients, derived from autoregressive coefficients and estimated by a recursive least square algorithm, are used as the recognition features. The features are then discriminated using a modified maximum likelihood distance classifier. The cross distribution of the first and second cepstral coefficients can be plotted real-time and on-line. The physician or user can adjust the specific motions to attain optimal recognition results using this information. Subjects can be trained to contract muscles in specified and easily achievable patterns by the distribution plot. The recognition results can be used as myoelectric prosthetic control, or providing commands for the human-computer interface.