%0 Journal Article
	%A Sozon H. Papavlasopoulos and  Marios S. Poulos and  George D. Bokos and  Angelos M. Evangelou
	%D 2007
	%J International Journal of Psychological and Behavioral Sciences
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 2, 2007
	%T Classification Control for Discrimination between Interictal Epileptic and Non – Epileptic Pathological EEG Events
	%U https://publications.waset.org/pdf/12795
	%V 2
	%X In this study, the problem of discriminating between interictal epileptic and non- epileptic pathological EEG cases, which present episodic loss of consciousness, investigated. We verify the accuracy of the feature extraction method of autocross-correlated coefficients which extracted and studied in previous study. For this purpose we used in one hand a suitable constructed artificial supervised LVQ1 neural network and in other a cross-correlation technique. To enforce the above verification we used a statistical procedure which based on a chi- square control. The classification and the statistical results showed that the proposed feature extraction is a significant accurate method for diagnostic discrimination cases between interictal and non-interictal EEG events and specifically the classification procedure showed that the LVQ neural method is superior than the cross-correlation one.

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