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Softcomputing Techniques for Improved Electroencephalogram Signal Analysis


Author : Ojo O. Adedayo, Folorunso Oladipo and Ijeh-Ogboi Chris

Pages : 2181-2186
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Abstract

In clinical signal processing and computer aided diagnosis, noise and relativity of human judgment are two of the most critical challenges which researchers attempt to surmount using several softcomputing techniques. In this paper, the recent use of these techniques in application to the analysis of electroencephalogram (EEG) is explored. The trend and prospects of other softcomputing methods that could significantly improve signal processing of EEG are also presented. It was observed that disease diagnosis and decision making systems of medical experts on mental activities and modeling of electrical impulses of the human brain can be significantly improved using these techniques or a hybrid thereof.

Keywords: electroencephalogram (EEG), Genetic Algorithm, Artificial Neural Network, Fuzzy systems, clinical diagnosis.

Article published in International Journal of Current Engineering and Technology, Vol.5, No.3 (June-2015)

 

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