Transfer Learning Based on Optimal Transport for Motor Imagery Brain-Computer Interfaces

Objective: This paper tackles the cross-sessions variability of electroencephalography-based brain-computer interfaces (BCIs) in order to avoid the lengthy recalibration step of the decoding method before every use. Methods: We develop a new approach of domain adaptation based on optimal transport to tackle brain signal variability between sessions of motor imagery BCIs. We propose a backward method where, unlike the original formulation, the data from a new session are transported to a calibration session, and thereby avoiding model retraining. Several domain adaptation approaches are evaluated and compared. We simulated two possible online scenarios: i) block-wise adaptation and ii) sample-wise adaptation. In this study, we collect a dataset of 10 subjects performing a hand motor imagery task in 2 sessions. A publicly available dataset is also used. Results: For the first scenario, results indicate that classifier retraining can be avoided by means of our backward formulation yielding to equivalent classification performance as compared to retraining solutions. In the second scenario, classification performance rises up to 90.23% overall accuracy when the label of the indicated mental task is used to learn the transport. Adaptive time is between 10 and 80 times faster than the other methods. Conclusions: The proposed method is able to mitigate the cross-session variability in motor imagery BCIs. Significance: The backward formulation is an efficient retraining-free approach built to avoid lengthy calibration times. Thus, the BCI can be actively used after just a few minutes of setup. This is important for practical applications such as BCI-based motor rehabilitation.

Automated summary

This paper addresses the cross-sessions variability in electroencephalography-based brain-computer interfaces (BCIs) and proposes a backward method to avoid the need for classifier retraining and reduce calibration time. The results show that the proposed approach can effectively mitigate the variability between sessions while maintaining classification performance.