Design of a video feedback SSVEP-BCI system for car control based on improved MUSIC method

Abstract

Brain computer interface (BCI) based on visual stimulus is widely used, however, subjects have to focus on the stimulus rather than the object they want to control. Therefore, a video feedback car control system based on steady state visual evoked potential (SSVEP) was designed in this paper. We added a video feedback screen surround by the visual stimulators. As a result, subject could know the location as well as the status of the car. Meanwhile, we studied an improved multiple signal classification (MUSIC) method to classify SSVEP signal to improve the performance of frequency-domain analysis, and compared it with canonical correlation analysis and cyclic convolution method, it showed the highest accuracy. Moreover, we added an online training session to ensure that subject could master the using of the system, and according to the result of training session, the average online accuracy for four directions is 87.5%. Experiment results show that in our video feedback car control system, subjects could control the smart car by adjusting their distribution of the attention and drive the car through an obstacle fluently.