Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/7314
Title: Probabilistic neural networks combined with wavelet coefficients for analysis of electroencephalogram signals
Authors: Übeyli, Elif Derya
Keywords: probabilistic neural networks
wavelet transform
electroencephalogram (EEG) signals
Issue Date: 2009
Publisher: Wiley
Abstract: In this paper, the probabilistic neural network is presented for classification of electroencephalogram (EEG) signals. Decision making is performed in two stages: feature extraction by wavelet transform and classification using the classifiers trained on the extracted features. The purpose is to determine an optimum classification scheme for this problem and also to infer clues about the extracted features. The present research demonstrates that the wavelet coefficients obtained by the wavelet transform are features which represent the EEG signals well. The conclusions indicate that the probabilistic neural network trained on the wavelet coefficients achieves high classification accuracies (the total classification accuracy is 97.63%).
URI: https://doi.org/10.1111/j.1468-0394.2009.00468.x
https://hdl.handle.net/20.500.11851/7314
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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