Completed in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Psychology, The University of Mississippi. Data collection was performed while the senior author was a predoctoral associate at the Psychophysiology Laboratory and Research Clinic, Yale University. Tobin Maginnis. Fridlund and The University of Mississippi, reprinted with permission. This research was supported in part by a grant from the National Science Foundation to G.
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EMG-based facial gesture recognition through versatile elliptic basis function neural network
Recently, the recognition of different facial gestures using facial neuromuscular activities has been proposed for human machine interfacing applications. Facial electromyograms EMGs analysis is a complicated field in biomedical signal processing where accuracy and low computational cost are significant concerns. In this paper, a very fast versatile elliptic basis function neural network VEBFNN was proposed to classify different facial gestures. The effectiveness of different facial EMG time-domain features was also explored to introduce the most discriminating.
EMG-based facial gesture recognition through versatile elliptic basis function neural network
Patients with no or limited hand function usually have difficulty in using conventional input devices such as a mouse or a touch screen. Having the ability of manipulating electronic devices can give patients full access to the digital world, thereby increasing their independence and confidence, and enriching their lives. In this study, a hands-free human-computer interface was developed in order to help patients manipulate computers using facial movements.
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