Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/7080
Title: Modified mixture of experts for analysis of EEG signals
Authors: Übeyli, Elif Derya
Keywords: modified mixture of experts
wavelet coefficients
Lyapunov exponents
diverse features
Issue Date: 2007
Publisher: IEEE
Source: 29th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society -- AUG 22-26, 2007 -- Lyon, FRANCE
Series/Report no.: PROCEEDINGS OF ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY
Abstract: In this paper, the usage of diverse features in detecting variability of electroencephalogram (EEG) signals was presented. The classification accuracies of modified mixture of experts (MATE), which were trained on diverse features, were obtained. The wavelet coefficients 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 diverse features achieved high accuracy rates.
URI: https://doi.org/10.1109/IEMBS.2007.4352598
https://hdl.handle.net/20.500.11851/7080
ISBN: 978-1-4244-0787-3
ISSN: 1094-687X
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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