Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6977
Title: Least squares support vector machine employing model-based methods coefficients for analysis of EEG signals
Authors: Übeyli, Elif Derya
Keywords: Least squares support vector machines
Model-based methods
Electroencephalogram (EEG) signals
Issue Date: 2010
Publisher: Pergamon-Elsevier Science Ltd
Abstract: The aim Of the study is classification of the electroencephalogram (EEG) signals by combination of the model-based methods and the least squares support vector machines (LS-SVMs). The LS-SVMs were implemented for classification of two types of EEG signals (set A - EEG signals recorded from healthy volunteers with eyes open and set E - EEG signals recorded from epilepsy patients during epileptic seizures). In order to extract the features representing the EEG signals, the spectral analysis of the EEG signals was performed by using the three model-based methods (Burg autoregressive - AR, moving average - MA, least squares modified Yule-Walker autoregressive moving average - ARMA methods). The present research demonstrated that the Burg AR coefficients are the features which well represent the EEG signals and the LS-SVM trained on these features achieved high classification accuracies. (C) 2009 Elsevier Ltd. All rights reserved.
URI: https://doi.org/10.1016/j.eswa.2009.05.012
https://hdl.handle.net/20.500.11851/6977
ISSN: 0957-4174
1873-6793
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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