EEG signal classification with different signal representations

Author

Anderson, Charles W. ; Devulapalli, Saikumar V. ; Stolz, Erik A.

Author_Institution

Dept. of Comput. Sci., Colorado State Univ., Fort Collins, CO, USA

fYear

1995

fDate

31 Aug-2 Sep 1995

Firstpage

475

Lastpage

483

Abstract

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. In this article, the authors report on a study comparing four representations of EEG signals and their classification by a two-layer neural network with sigmoid activation functions. The neural network is implemented on a CNAPS server (128 processor, SIMD architecture) by Adaptive Solutions Inc., gaining a 100-fold decrease in training time over a Sun Sparc10 for a large number of hidden units

Keywords

electroencephalography; neural nets; pattern classification; signal representation; Adaptive Solutions Inc; CNAPS server; EEG signal classification; SIMD architecture; mental states; paralyzed person; sigmoid activation functions; signal representations; two-layer neural network; Bayesian methods; Biological neural networks; Computer science; Data mining; Electrodes; Electroencephalography; Frequency; Pattern classification; Signal analysis; Signal representations;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks for Signal Processing [1995] V. Proceedings of the 1995 IEEE Workshop

Conference_Location

Cambridge, MA

Print_ISBN

0-7803-2739-X

Type

conf

DOI

10.1109/NNSP.1995.514922

Filename

514922