Determining mental state from EEG signals using parallel implementations of neural networks

Authors:
Charles W. Anderson;Saikumar V. Devulapalli;Erik A. Stolz
Affiliations:
-;-;-
Venue:
Scientific Programming - On applications analysis
Year:
1995

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Abstract

EEG analysis has played a key role in the modeling of the brain'scortical dynamics, but relatively little effort has been devoted todeveloping EEG as a limited means of communication. If severalmental states can be reliably distinguished by recognizing patternsin EEG, then a paralyzed person could communicate to a device suchas a wheelchair by composing sequences of these mental states. EEGpattern recognition is a difficult problem and hinges on thesuccess of finding representations of the EEG signals in which thepatterns can be distinguished. In this article, we report on astudy 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 networkimplemented on a CNAPS server (128 processor, SIMD architecture) byAdaptive Solutions, Inc. Execution time comparisons show over ahundred-fold speed up over a Sun Sparc 10. The best classificationaccuracy on untrained samples is 73% using the frequency-basedrepresentation.