Cross-subject fusion based on time-weighting canonical correlation analysis in SSVEP-BCIs

Abstract

Brain–computer interface technology provides new possibilities for medical rehabilitation and human–computer interaction. The steady-state visual evoked potential based brain–computer interface (SSVEP-BCI) is the preferred solution for controlling equipment because of its ease of operation, low training requirements, and high information transfer rate (ITR). However, the low recognition accuracy of SSVEP in short time limits its further improvement in ITR. To address this issue, this paper proposes time-weighting canonical correlation analysis (TWCCA), a time-domain enhancement CCA-based method to improve the recognition accuracy of SSVEP in short recognition time. The TWCCA method evaluates the SSVEP components of EEG signals at different time periods and performs a time-dimension weighted CCA on the original signal. The method integrates the features of all stimulus targets based on CCA and is insensitive to the specific number of targets, so it does not require a large amount of calibration data. To further shorten the calibration time for a certain user, this paper also proposes a cross-subject fusion method to calibrate the parameters of the target user using the acquired data from other users. The effectiveness of the proposed method is evaluated using EEG signals from the occipital and ear regions. The experiment results show that the TWCCA method significantly improves the average recognition accuracy by 3.86% and the cross-subject fusion method shortens the calibration time by 78.5% without compromising recognition accuracy. The proposed combination of cross-subject fusion method and TWCCA (CF-TWCCA) increases recognition accuracy by 13.16% on average (p<0.001).