A subband coding scheme and the Bayesian neural network for EMG function analysis

A subband coding scheme and Bayesian neural network (BNN) approach to the analysis of electromyographic (EMG) signals of upper extremity limb functions are presented. Three channels of EMG signals recorded from the biceps, triceps and one muscle of the forearm are used for discriminating six primitive motions associated with the limb. A set of parameters is extracted from the spectrum of the EMG signals combining with the subband coding technique for data compression. Each sequence of EMG signals is cut into five frames from the primary point located by the energy threshold method. From each frame, the parameters are then obtained by the integration of the subbands. The temporal as well as the spectral characteristics can be implicitly or directly included in the parameters. The BNN is used as a subnet for discriminating one motion. From the results, it is shown that an average recognition rate of 85% may be achieved