EEG feature extraction and selection techniques for epileptic detection: A comparative study
Author | Hussein R. |
Author | Mohamed A. |
Author | Shahan K. |
Author | Mohamed A.A. |
Available date | 2022-04-21T08:58:35Z |
Publication Date | 2013 |
Publication Name | IEEE Symposium on Computers and Informatics, ISCI 2013 |
Resource | Scopus |
Identifier | http://dx.doi.org/10.1109/ISCI.2013.6612397 |
Abstract | Epileptic detection techniques rely heavily on the Electroencephalography (EEG) as representative signal carrying valuable information pertaining to the current brain state. For these techniques to be efficient and reliable, a set of discriminant, epileptic-related features has first to be obtained. Furthermore, depending on the classifier model used, a subset of these features is identified and selected for the classifier to yield an optimum performance. Many feature extraction and selection techniques have been reported in the literature, utilizing different strategies. The aim of this work is to review the most widely used ones and to evaluate their performance in terms of their overall complexity and classification accuracy. For this purpose, the support vector machine (SVM) is chosen as a classifier model to study the performance of the obtained features. Extensive experimental work has been carried out and the comparative results and trade-offs are reported. 2013 IEEE. |
Language | en |
Publisher | IEEE Computer Society |
Subject | Electroencephalography Electrophysiology Feature extraction Information science Classification accuracy Classifier models Comparative studies Epileptic detection Epileptic seizures Feature extraction and selection feature selsection Optimum performance Support vector machines |
Type | Conference Paper |
Pagination | 170-175 |
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Computer Science & Engineering [2342 items ]