Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6261
Title: Analysis of EEG signals by implementing eigenvector methods/recurrent neural networks
Authors: Übeyli, Elif Derya
Keywords: Recurrent neural networks
Eigenvector methods
Electroencephalogram (EEG) signalsd
Issue Date: 2009
Publisher: Academic Press Inc Elsevier Science
Abstract: The implementation of recurrent neural network (RNN) employing eigenvector methods is presented for classification of electroencephalogram (EEG) signals. In practical applications of pattern recognition, there are often diverse features extracted from raw data which needs recognizing. Because of the importance of making the right decision, the present work is carried out for searching better classification procedures for the EEG signals. Decision making was performed in two stages: feature extraction by eigenvector methods and classification using the classifiers trained on the extracted features. The aim of the study is classification of the EEG signals by the combination of eigenvector methods and the RNN. The present research demonstrated that the power levels of the power spectral density (PSD) estimates obtained by the eigenvector methods are the features which well represent the EEG signals and the RNN trained on these features achieved high classification accuracies. (c) 2008 Elsevier Inc. All rights reserved.
URI: https://doi.org/10.1016/j.dsp.2008.07.007
https://hdl.handle.net/20.500.11851/6261
ISSN: 1051-2004
1095-4333
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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