Abstract:

In order to make full use of visual and auditory perception channels and realize efficient brain-controlled character spelling, a two-level spelling paradigm based on the region is proposed. In the first level of the paradigm, the target region is selected based on the motion-onset visual evoked potential, and the code division multiple access method is introduced to improve the selection rate. In the second level, the target character is encoded based on the hybrid motion-onset visually evoked potential and auditory P300 to make full use of the visual and auditory hybrid effect to improve the accuracy of target character selection. In order to decode the collected EEG signals effectively, a classification recognition algorithm for EEG signals combined with a deep linear discriminant analysis is proposed. Experimental results show that the average classification accuracy of the deep linear discriminant analysis algorithm in the classification recognition of two-level EEG signals is 61.7% and 74%, respectively, which is obviously higher than that of the traditional method and the two convolutional neural network methods. Therefore, the algorithm can effectively improve the decoding performance of the two-level brain computer interface induced by the audio-visual hybrid.

Key words: brain computer interface, spelling paradigm, motion-onset visual evoked potential, deep learning