Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/7079
Title: Modified mixture of experts employing eigenvector methods and Lyapunov exponents for analysis of electroencephalogram signals
Authors: Übeyli, Elif Derya
Keywords: modified mixture of experts
eigenvector methods
Lyapunov exponents
diverse features
electroencephalogram (EEG) signals
Issue Date: 2009
Publisher: Wiley
Abstract: The use of diverse features in detecting variability of electroencephalogram (EEG) signals is presented. The classification accuracies of the modified mixture of experts (MME), which was trained on diverse features, were obtained. Eigenvector methods (Pisarenko, multiple signal classification - MUSIC, and minimum-norm) were selected to generate the power spectral density estimates. The features from the power spectral density estimates and Lyapunov exponents of the EEG signals were computed and statistical features were calculated to depict their distribution. The statistical features, which were used for obtaining the diverse features of the EEG signals, were then input into the implemented neural network models for training and testing purposes. The present study demonstrated that the MME trained on the diverse features achieved high accuracy rates (total classification accuracy of the MME is 98.33%).
URI: https://doi.org/10.1111/j.1468-0394.2009.00490.x
https://hdl.handle.net/20.500.11851/7079
ISSN: 0266-4720
1468-0394
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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