Neural network and wavelet recognition of facial electromyographic signals

The present aim was to explore the possibilities of using neural networks for recognizing significant changes in electrical activity of human facial muscles. We used multilayer perceptron neural networks to recognize bursts of electromyographic signals recorded with bipolar surface electrodes from two subject's facial muscles. Wavelets were applied for the detection of high frequency components of electromyographic signals. Coefficients of wavelets were used as an input to a neural network in order to differentiate bursts from the signals. The results showed that the recognition of bursts was very successful resulting to 84-97 percent total accuracies. The results were very encouraging and suggest further that the measurement of facial muscle activity may be a potentially useful computer input signal, for example, for affective computing which can be seen as a future versatile interaction between the computer and the user.