Title :
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
fDate :
31 Aug-2 Sep 1995
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;
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
DOI :
10.1109/NNSP.1995.514922
