Scientific Programming

Scientific Programming / 1995 / Article
Special Issue

Applications Analysis

View this Special Issue

Open Access

Volume 4 |Article ID 603414 | https://doi.org/10.1155/1995/603414

Charles W. Anderson, Saikumar V. Devulapalli, Erik A. Stolz, "Determining Mental State from EEG Signals Using Parallel Implementations of Neural Networks", Scientific Programming, vol. 4, Article ID 603414, 13 pages, 1995. https://doi.org/10.1155/1995/603414

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

Received16 May 1994
Accepted16 Dec 1994

Abstract

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.

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.


More related articles

 PDF Download Citation Citation
 Order printed copiesOrder
Views101
Downloads611
Citations

Related articles

We are committed to sharing findings related to COVID-19 as quickly as possible. We will be providing unlimited waivers of publication charges for accepted research articles as well as case reports and case series related to COVID-19. Review articles are excluded from this waiver policy. Sign up here as a reviewer to help fast-track new submissions.