Adaptive Neuro-fuzzy Inference System for Classification of EEG Signals Using Fractal Dimension

This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) and Principle Component Analysis(PCA), for classification of electroencephalogram (EEG) signals. Different mental tasks have been used to understand the process in our mind and we have chosen relaxation and imagination for our study. As well as normal conscious state, we have considered mental tasks performed in hypnosis which is defined as a state of consciousness with high concentration. Decision making was performed in three stages: feature extraction by computation of Fractal Dimension, dimension reduction with PCA and classification by the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies on raw data and extracted features. The results confirmed that the proposed ANFIS model has potential in classifying the EEG signals.