Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6201
Title: Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients
Authors: Güler, İnan
Übeyli, Derya Elif
Keywords: adaptive neuro-fuzzy inference system (ANFIS)
fuzzy logic
wavelet transform
electroencephalogram (EEG) signals
Issue Date: 2005
Publisher: Elsevier
Abstract: This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for classification of electroencephalogram (EEG) signals. Decision making was performed in two stages: feature extraction using the wavelet transform (V T) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Five types of EEG signals were used as input patterns of the five ANFIS classifiers. To improve diagnostic accuracy, the sixth ANFIS classifier (combining ANFIS) was trained using the outputs of the five ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the saliency of features on classification of the EEG signals were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in classifying the EEG signals. (c) 2005 Elsevier B.V. All rights reserved.
URI: https://doi.org/10.1016/j.jneumeth.2005.04.013
https://hdl.handle.net/20.500.11851/6201
ISSN: 0165-0270
1872-678X
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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