Neural networks in recognition of electrical signals recorded from two facial muscle sites

The present aim was to develop signal analysis methods to recognize muscle action potentials recorded with electromyography (EMG) from two facial muscle sites, corrugator supercilii (the muscle activated in frowning) and zygomaticus major (the muscle activated in smiling). Fourteen subjects produced volitional activations on both muscle sites, first, on a single muscle site activation basis and finally combinatory activations of both muscle sites. Wavelets and neural networks were used to analyse these voluntarily produced bursts of electric signals. Our results showed well over 90% recognition rates for all signal types. It is possible to utilize these communicative signals, for example, for multimodal human-computer interaction.