Abstract
A brain-computer interface (BCI) is a communication system that uses as input the EEG (electroencephalography) of humans, to operate a computer without using a keyboard or the like. The purpose of BCIs is one of the communication tool of handicap users. In this paper we experimented with a hybrid BCI system using EEG of right and left hand motor imagery (MI) and steady-state visual evoked potential (SS-VEP). We examined the feature value performs frequency analysis for the experimental data. Furthermore, we evaluated the linear discriminant method as optimal feature extraction method. The identification rate obtained using a Bayesian classifier. As the result, all subjects was 90 % identification rate by linear discriminant method.
