Homomorphic Processing of Surface Recorded EMG Signals

Abstract

Electromyographic (EMG) signals contain both neural and muscle information. Consequently, EMG signals can be modelled as the composition of two component signals, one of these being a low frequency neural input, the other a relatively high frequency, constant spectrally shaped, stationary, unitary muscle response. Utilizing this model and homomorphic processing estimates of the two component signals can be obtained. These estimates contain neural and muscle information respectively. This thesis establishes the basis for the use of this multiplicative model. It also outlines the application of multiplicative homomorphic processing to EMG signals. The results of this processing are shown to be valid and to contain useful information. The thesis concludes that the model is both appropriate and useful. It also points out that the use of this model and homomorphic processing allows the simultaneous extraction of both neural and muscle information from the EMG signal,a result which is not possible with other currently used processing techniques.