Neural network screening of electromyographic signals as the first phase to design novel human-computer interaction

The present aim was to describe the first phase attempts to recognise voluntarily produced changes in electromyographic signals measured from two facial muscles. Thirty subjects voluntarily activated two facial muscles, corrugator supercilii and zygomaticus major. We designed a neural network based recognition system that screened out muscle activations from the electromyographic signals. When several subjects were tested according to the same test protocol, the neural network system was able to correctly recognise more than 95 % of all muscle activations. This is a promising result and we shall next proceed to modify the system for real-time functioning and then design its utilisation for various multimodal human-computer interaction techniques. The subsequent phase in the future will be the interaction backwards: when a computer program first recognised the use of the facial muscles, it will then follow the instructions given by the user. For instance, by using the facial muscles the subject could select or activate objects on the computer screen. This would be one of the opportunities that we develop to help, e.g., disabled persons, who are unable to use their hands.