A Support Vector Neural Network for P300 EEG Signal Classification

摘要

Brain–computer interface (BCI) P300 speller can help severely disabled patients communicate and control with external machines or robots, so that the classification methods of P300 electroencephalogram (EEG) signal play an important role in the development of BCI system and technology. In this article, a novel support vector neural network (SVNN) is proposed and developed to obtain more accurate and effective EEG classification results. It is the first time to combine linear variational inequality based primal-dual neural network with convex quadratic programming problem based on support vector machine to solve the classification problem. It has been proved that the SVNN globally converges to the optimal solution of convex optimization problem and iterates the parameters in the form of matrix, which means that the method has global convergence and parallelism. The proposed SVNN method is used to solve the classification problem of P300 EEG signals. Experimental results on dataset IIb from BCI competition II and dataset II from BCI competition III show that the accuracy of the proposed SVNN method is 100% and 98%, respectively. Compared with most of the state-of-the-art algorithms, SVNN has the highest recognition accuracy and information transfer rate.