Feature extraction of Motion-onset visual evoked potential based on CSP and FBCSP

Motion-onset visual evoked potential (mVEP) has been gradually applied in brain computer interface systems due to its maximum amplitude and minimum difference between subjects. In this paper, three feature extraction algorithms including downsampling stack average algorithm, common spatial pattern (CSP) and filter bank common spatial pattern (FBCSP) were used to extract the features of mVEP, and the experimental results show that the average classification accuracy of CSP algorithm and FBCSP algorithm in mVEP-BCI is 89.0% and 91.2% respectively, which is 3.8% and 6% higher than that of the downsampling stack average algorithm. And indicating that the CSP algorithm and the FBCSP algorithm are suitable for exercise initiation visual evoked potential brain-computer interface system and the FBCSP algorithm is in the system The feature extraction process can play a more obvious effect.