Neural networks for pattern recognition
Neural networks for pattern recognition
Affective computing
Human factors issues in the neural signals direct brain-computer interfaces
Assets '00 Proceedings of the fourth international ACM conference on Assistive technologies
Onset detection in surface electromyographic signals: a systematic comparison of methods
EURASIP Journal on Applied Signal Processing
Communications of the ACM
Pupil size variation as an indication of affective processing
International Journal of Human-Computer Studies - Application of affective computing in human—Computer interaction
Gazing and frowning as a new human--computer interaction technique
ACM Transactions on Applied Perception (TAP)
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The goal of this research was to investigate neural network-based methods to be applied in the processing of biomedical signals. We developed a neural network-based method for the detection of voluntarily produced changes in facial muscle action potentials. Electromyographic signals were recorded from the corrugator supercilii and zygomaticus major facial muscles. The facial muscle action potentials of thirty subjects were measured while they performed a series of voluntary contractions of these muscles. Wavelet denoising or digital bandpass filtering was applied to the preprocessing of the signals. A neural network was exploited for an offline classification of various phases of these signals. The results show that the neural network-based technique developed functioned very well, producing a reliable recognition accuracy of 96 to 99%. Because of these promising results, we will proceed in the development of this method for real-time applications that benefit from the analysis of electromyographic signals.