Classification of motor imagery using multisource joint transfer learning

As an important way for human-computer interaction, the motor imagery brain-computer interface (MI-BCI) can decode personal motor intention directly by analyzing electroencephalogram (EEG) signals. However, a large amount of labeled data has to be collected for each new subject since EEG patterns vary between individuals. The long calibration phase severely limits the further development of MI-BCI. To tackle this problem, multi-source joint domain adaption (MJDA) and multi-source joint Riemannian adaption (MJRA) algorithms are proposed in this paper. Both methods aim to transfer knowledge from other subjects to the current subject who has only a small amount of labeled data. First, the common spatial pattern with Euclidean alignment is used to select source subjects who have similar spatial patterns to the target subject. Second, the covariance matrices of EEG trials are aligned in Riemannian space by removing subject-specific baselines. These two steps are shared by MJDA and MJRA. In the last step, MJDA attempts to minimize the feature distribution mismatch in the Riemannian tangent space, while MJRA attempts to find an adaptive Riemannian classifier. Finally, the proposed methods are validated on two datasets: BCI Competition IV 2a and online event-related desynchronization (ERD)-BCI. The experimental results demonstrate that both MJDA and MJRA outperform the state-of-the-art approaches. The MJDA provides a new idea for the offline analysis of MI-BCI, while MJRA could make a big difference to the online calibration of MI-BCI. Published under an exclusive license by AIP Publishing

Automated summary

The MJDA and MJRA algorithms address the challenge of collecting labeled data for MI-BCI by transferring knowledge from other subjects. Both methods aim to help subjects with limited labeled data through multi-source joint domain adaption and multi-source joint Riemannian adaption.