Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients
作者： İnan Güler , Elif Derya Übeyli
DOI: 10.1016/J.JNEUMETH.2005.04.013
关键词:
摘要: This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for classification electroencephalogram (EEG) signals. Decision making was performed in two stages: feature extraction using wavelet transform (WT) and ANFIS trained with backpropagation gradient descent method combination least squares method. Five types EEG signals were used as input patterns five classifiers. To improve diagnostic accuracy, sixth classifier (combining ANFIS) outputs classifiers data. The proposed combined neural network capabilities fuzzy logic qualitative approach. Some conclusions concerning saliency features on obtained through analysis ANFIS. performance evaluated terms training accuracies results confirmed that has potential classifying
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