EEG classification based on artificial neural network in brain computer interface

Abstract

Aiming at the topic of electroencephalogram (EEG) pattern recognition in brain computer interface (BCI), a classification method based on probabilistic neural network (PNN) with supervised learning was presented in this paper. It applied the recognition rate of training samples to the learning progress of network parameters, The learning vector quantization is employed to group training samples and the Genetic algorithm (GA) is used for training the network's smoothing parameters and hidden central vector for determining hidden neurons. Utilizing the standard dataset I(a) of BCI Competition 2003 and comparing with other classification methods, the experiment results show that this way has the best performance of pattern recognition, and the classification accuracy can reach 93.8%, which improves over 5% compared with the best result (88.7%) of the competition. This technology provides an effective way to EEG classification in practical system of BCI. © 2010 Springer-Verlag.