Online semi-supervised learning with KL distance weighting for Motor Imagery-based BCI

Studies had shown that Motor Imagery-based Brain Computer Interface (MI-based BCI) system can be used as a therapeutic tool such as for stroke rehabilitation, but had shown that not all subjects could perform MI well. Studies had also shown that MI and passive movement (PM) could similarly activate the motor system. Although the idea of calibrating MI-based BCI system from PM data is promising, there is an inherent difference between features extracted from MI and PM. Therefore, there is a need for online learning to alleviate the difference and improve the performance. Hence, in this study we propose an online batch mode semi-supervised learning with KL distance weighting to update the model trained from the calibration session by using unlabeled data from the online test session. In this study, the Filter Bank Common Spatial Pattern (FBCSP) algorithm is used to compute the most discriminative features of the EEG data in the calibration session and is updated iteratively on each band after a batch of online data is available for performing semi-supervised learning. The performance of the proposed method was compared with offline FBCSP, and results showed that the proposed method yielded slightly better results in comparison with offline FBCSP. The results also showed that the use of the model trained from PM for online session-to-session transfer compared to the use of the calibration model trained from MI yielded slightly better performance. The results suggest that using PM, due to its better performance and ease of recording is feasible and performance can be improved by using the proposed method to perform online semi-supervised learning while subjects perform MI.