Pattern classification of time-series EMG signals using neural networks

This paper proposes a pattern classification method of time-series EMG signals for prosthetic control. To achieve successful classification for non-stationary EMG signals, a new neural network structure that combines a common back-propagation neural network with recurrent neural filters is used. A convergence time of the network learning can be regulated by a new learning method based on dynamics of a terminal attractor. The experiments of pattern classification and prosthetic control are carried out for several subjects including an amputee. It is shown from the results that the proposed method improves learning/classification ability for stationary and non-stationary EMG signals during a series of continuous motions.