Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/7528
Title: Statistics over features: EEG signals analysis
Authors: Übeyli, Elif Derya
Keywords: Electroencephalogram (EEG) signals
Feature extraction/selection
Lyapunov exponents
Wavelet coefficients
Eigenvector methods
Issue Date: 2009
Publisher: Pergamon-Elsevier Science Ltd
Abstract: This paper presented the usage of statistics over the set of the features representing the electroencephalogram (EEG) signals. Since classification is more accurate when the pattern is simplified through representation by important features, feature extraction and selection play an important role in classifying systems such as neural networks. Multilayer perceptron neural network (MLPNN) architectures were formulated and used as basis for detection of electroencephalographic changes. Three types of EEG signals (EEG signals recorded from healthy volunteers with eyes open, epilepsy patients in the epileptogenic zone during a seizure-free interval, and epilepsy patients during epileptic seizures) were classified. The selected Lyapunov exponents, wavelet coefficients and the power levels of power spectral density (PSD) values obtained by eigenvector methods of the EEG signals were used as inputs of the MLPNN trained with Levenberg-Marquardt algorithm. The classification results confirmed that the proposed MLPNN has potential in detecting the electroencephalographic changes. (C) 2009 Elsevier Ltd. All rights reserved.
URI: https://doi.org/10.1016/j.compbiomed.2009.06.001
https://hdl.handle.net/20.500.11851/7528
ISSN: 0010-4825
1879-0534
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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