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

@article{citeulike:2122013, 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 (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.}, address = {Department of Electronics and Computer Education, Faculty of Technical Education, Gazi University, 06500 Teknikokullar, Ankara, Turkey. iguler@gazi.edu.tr}, author = {Guler, I. and Ubeyli, E.}, citeulike-article-id = {2122013}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.jneumeth.2005.04.013}, citeulike-linkout-1 = {http://linkinghub.elsevier.com/retrieve/pii/S0165027005001172}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/16054702}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=16054702}, day = {30}, doi = {10.1016/j.jneumeth.2005.04.013}, issn = {01650270}, journal = {Journal of Neuroscience Methods}, keywords = {decoding, eeg, wavelet}, month = {October}, number = {2}, pages = {113--121}, posted-at = {2009-10-28 12:12:32}, priority = {2}, title = {Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients}, url = {http://dx.doi.org/10.1016/j.jneumeth.2005.04.013}, volume = {148}, year = {2005} }

TY - JOUR ID - citeulike:2122013 L3 - citeulike-article-id:2122013 N2 - 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. IS - 2 JF - Journal of Neuroscience Methods SN - 01650270 AD - Department of Electronics and Computer Education, Faculty of Technical Education, Gazi University, 06500 Teknikokullar, Ankara, Turkey. iguler@gazi.edu.tr EP - 121 TI - Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients VL - 148 SP - 113 KW - decoding KW - eeg KW - wavelet AU - Guler, I AU - Ubeyli, E PY - 2005/10/30/ UR - http://dx.doi.org/10.1016/j.jneumeth.2005.04.013 ER -