Brain-computer interface system based on audio-visual matching error monitoring

Abstract

The present invention discloses a brain-computer interface system based on audio-visual matching error monitoring. According to the EEG data obtained from three feedback paradigms of audio-visual matching errors, ERP analysis is applied to extract important ERP components related to audio-visual matching errors, so as to interpret the neural response within a specific time window; then, based on time-frequency analysis, the dynamic changes of EEG signals in different bands are captured, and the phase consistency ITPC of EEG activity is combined to reveal the reliability and stability of EEG activity under different feedback modes; finally, through the improved EEGNet model, automatic feature extraction and efficient classification of signals are realized, and the robustness and generalization ability of the system are improved. EEG signals are highly nonlinear and specific. By combining ERP analysis, time-frequency analysis and deep learning models, it is helpful to more effectively understand the potential neural mechanism of audio-visual matching errors, improve the performance of brain-computer interface systems and apply them to clinical research on error processing related disorders.