Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients

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 (WT) and the ANFIS trained with the backpropagation gradie...

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Bibliographic Details
Published in:Journal of neuroscience methods 2005-10, Vol.148 (2), p.113-121
Main Authors: Güler, Inan, Ubeyli, Elif Derya
Format: Article
Language:English
Subjects:
Adaptive neuro-fuzzy inference system (ANFIS)
Artificial Intelligence
Brain - physiology
Electroencephalogram (EEG) signals
Electroencephalography - methods
Evoked Potentials - physiology
Fuzzy Logic
Humans
Neural Networks (Computer)
Signal Processing, Computer-Assisted
Software
Wavelet transform
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Summary: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 (WT) 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.
ISSN:0165-0270
1872-678X
DOI:10.1016/j.jneumeth.2005.04.013