Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/7097
Title: Multiclass support vector machines for EEG-signals classification
Authors: Güler, İnan
Übeyli, Elif Derya
Keywords: electroencephalogram (EEG) signals
Lyapunov exponents
multiclass support vector machine (SVM)
probabilistic neural network (PNN)
wavelet coefficients
Issue Date: 2007
Publisher: IEEE-Inst Electrical Electronics Engineers Inc
Abstract: In this paper, we proposed the multiclass support vector machine (SVM) with the error-correcting output codes for the multiclass electroencephalogram (EEG) signals classification problem. The probabilistic neural network (PNN) and multilayer perceptron neural network were also tested and benchmarked for their performance on the classification of the EEG signals. Decision making was performed in two stages: feature extraction by computing the wavelet coefficients and the Lyapunov exponents and classification using the classifiers trained on the extracted features. The purpose was to determine an optimum classification scheme for this problem and also to infer clues about the extracted features. Our research demonstrated that the wavelet coefficients and the Lyapunov exponents are the features which well represent the EEG signals and the multiclass SVM and PNN trained on these features achieved high classification accuracies.
URI: https://doi.org/10.1109/TITB.2006.879600
https://hdl.handle.net/20.500.11851/7097
ISSN: 1089-7771
1558-0032
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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