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Scientific Programming
Volume 4, Issue 3, Pages 171-183

Determining Mental State from EEG Signals Using Parallel Implementations of Neural Networks

Charles W. Anderson,1 Saikumar V. Devulapalli,1 and Erik A. Stolz2

1Department of Computer Science, Colorado State University, Fort Collins, CO 80523, USA
2Department of Electrical Engineering, Colorado State University, Fort Collins, CO 80523, USA

Received 16 May 1994; Accepted 16 December 1994

Copyright © 1995 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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 such as 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 unprocessed signals, a reduced-dimensional representation using the Karhunen – Loève 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. Execution time comparisons show over a hundred-fold speed up over a Sun Sparc 10. The best classification accuracy on untrained samples is 73% using the frequency-based representation.