Softcomputing Techniques for Improved Electroencephalogram Signal Analysis

Authors

  • Ojo O. Adedayo Department of Electrical Electronic and Computer Engineering, College of Engineering, Afe Babalola University, P.M.B. 5454, Km 8.5, Afe Babalola Way, Ado-Ekiti, Ekiti State, Nigeria Author
  • Folorunso Oladipo Department of Electrical Electronic and Computer Engineering, College of Engineering, Afe Babalola University, P.M.B. 5454, Km 8.5, Afe Babalola Way, Ado-Ekiti, Ekiti State, Nigeria Author
  • Ijeh-Ogboi Chris Department of Electrical Electronic and Computer Engineering, College of Engineering, Afe Babalola University, P.M.B. 5454, Km 8.5, Afe Babalola Way, Ado-Ekiti, Ekiti State, Nigeria Author

Keywords:

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

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.

References

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Published

2015-06-30

Issue

Vol.5, No.3 (May/Jun 2015)

Section

Articles

How to Cite

Softcomputing Techniques for Improved Electroencephalogram Signal Analysis. (2015). International Journal of Current Engineering and Technology, 5(3), 2181-2186. https://ijcet.evegenis.org/index.php/ijcet/article/view/2378