HaWCoS: the "hands-free" wheelchair control system
Proceedings of the fifth international ACM conference on Assistive technologies
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EEG analysis has played a key role in the modeling of the brain's cortical dynamics, but relatively little effort has been devoted to developing EEG as a limited means of communication. If several mental states can be reliably distinguished by recognizing patterns in EEG, then a paralyzed person could communicate to a device like a wheelchair by composing sequences of these mental states. EEG pattern recognition is a difficult problem and hinges on the success of finding representations of the EEG signals in which the patterns can be distinguished. In this article, we report on a study comparing three EEG representations, the raw signals, a reduced-dimensional representation using the K-L transform, and a frequency-based representation. Classification is performed with a two-layer neural network implemented on a CNAPS server (128 processor, SIMD architecture) by Adaptive Solutions, Inc.. The best classification accuracy on untrained samples is 73% using the frequency-based representation.