EEG classification using recurrent adaptive neuro-fuzzy network based on time-series prediction

Brain–computer interface (BCI) is a system that provides a way for brain and computer to communicate with each other directly. Electroencephalogram (EEG) is an important process in a BCI that can be used to determine whether the subject is doing action and/or imagination. This paper presents a motor imagery (MI) classification for BCI systems using recurrent adaptive neuro-fuzzy interface system (ANFIS). The classification system is based on time-series prediction where features are exploited from the EEG signals recorded from subjects imagining of the right hand, left hand, tongue, and foot movement. The classification system contains some recurrent ANFISes. Each recurrent ANFIS is trained on MI signals of one class and specializes in recognizing the signals of the same class from the signals of other categories. Recurrent ANFISes are employed to predict one step ahead for the EEG time-series data, and then, the classification is performed by mean square error (MSE) of the predicted signals. This approach is carried out on twelve subjects MI signals of four classes in online mode. Average prediction MSE of 0.0302 and average classification accuracy of 85.52% are obtained as results.