Myoelectric signal analysis using neural networks.

It is shown that the capacity of a discrete Hopfield network for functional minimization allows it to extract the time-series parameters from a myoelectric signal (MES) at a faster rate than the previously used SLS algorithm. With a two-dimensional signal space consisting of one of the parameters and the signal power, a two-layer perceptron trained using back-propagation has been used to classify MES signals from different types of muscular contractions. The results suggest that neural networks may be suitable for MES analysis tasks and that further research in this direction is warranted.