Facilitating applications of SSVEP-BCI by effective Cross-Subject knowledge transfer

In steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI), improving the recognition performance for new subjects without calibration data is the key challenge for practical application. Unsupervised transfer learning is an effective way to overcome it. However, existing studies focus solely on what to transfer, rather than how to effectively transfer, resulting in unsatisfactory transfer effectiveness or even negative transfer. In this study, an innovative unsupervised cross-subject transfer learning method for SSVEP-BCI was proposed, named SUTL. It involves that subject transferability estimation (STE) and a multi-domain alignment method were proposed to alleviate the potential interference of differences in SSVEP signal distribution among subjects. STE screens appropriate transferable subjects from the source subject pool, while domain alignment directly makes all subjects more similar. Then, SUTL sufficiently exploits the information of the selected source subjects, learning and transferring both generalization knowledge and subject-specific knowledge to boost the recognition performance for the new subject. The performance of SUTL was evaluated on two public SSVEP datasets (benchmark dataset and BETA dataset) with 40 classes, the extensive experimental results reveal that SUTL markedly boosts the effectiveness of SSVEP cross-subject transfer and dramatically outperforms the state-of-art methods. SUTL significantly enhances the recognition performance of SSVEP-BCI for new subjects and facilitates its practical application.