Multimodal Neuroelectric Interface Development

Abstract

We are developing electromyographic and electroencephalographic methods, which draw control signals for human-computer interfaces from the human nervous system. We have made progress in four areas: a) real-time pattern recognition algorithms for decoding sequences of forearm muscle activity associated with control gestures, b) signal-processing strategies for computer interfaces using EEG signals, c) a flexible computation framework for neuroelectric interface research, d) non-contact sensors, which measure EMG or EEG signals without resistive contact to the body.


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