Optimized adaptive neuro-fuzzy inference system for motor imagery EEG signals classifications

A motor imagery related electroencephalogram (EEG) feature classification technique through the time-series prediction based on the adaptive neuro-fuzzy inference system (ANFIS) is presented for neural computation applications. We descries a method for classification of EEG using optimized ANFIS and the proposed method was focus on the validation of the Harmony Search algorithm based optimization procedure for ANFIS. The motor imagery related EEG signals recorded from subjects performing left, right hand and foots motor imagery. From this signal, features obtained from the difference of multiresolution fractal feature vectors between the predicted and actual signals by using time-series prediction technique. In order to optimize the ANFIS, Harmony Search algorithm is sufficiently adaptable to allow incorporation of other training techniques like feed-forward and gradient descents. In this paper, the proposed technique is employed to simulate the three types of motor imagery (left, right hand, right foots) EEG signals evaluation data which were used as input patterns of the optimized ANFIS classifier.