Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6869
Title: Implementing eigenvector methods/probabilistic neural networks for analysis of EEG signals
Authors: Übeyli, Elif Derya
Keywords: Probabilistic neural networks
Eigenvector methods
Electroencephalogram (EEG) signals
Issue Date: 2008
Publisher: Pergamon-Elsevier Science Ltd
Abstract: A new approach based on the implementation of probabilistic neural network (PNN) 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 PNN. The purpose is to determine an Optimum Classification scheme for this problem and also to infer clues about the extracted features. 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 PNN trained on these features achieved high classification accuracies. (C) 2008 Elsevier Ltd. All rights reserved.
URI: https://doi.org/10.1016/j.neunet.2008.08.005
https://hdl.handle.net/20.500.11851/6869
ISSN: 0893-6080
1879-2782
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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