A Multi-Constrained Transfer Learning for Cross-Subject Decoding of Motor Imagery-Based BCI

Individual differences and long calibration time present significant challenges to the practical implementation of brain–computer interfaces (BCIs). Domain adaptation technology can help mitigate these challenges by leveraging knowledge from existing subjects. Although domain adaptation methods have achieved progress in BCIs, there remains a need for further exploration in class structure and cross-domain dispersion. In this paper, we propose a novel framework, multi-constrained transfer learning with selective pseudo-label update (MCTLP). First, Euclidean alignment is applied to reduce inter-subject variability at the data level. Then, multi-constrained feature alignment (MCFA) is introduced, which iteratively constructs a kernel mapping space and then determines an optimized subspace to align both marginal and conditional distributions at the feature level under class structure and dispersion constraints. Moreover, in this iterative process of feature alignment, a selective pseudo-label update method is proposed to update the pseudo-labels of only the target samples with high classification confidence to realize more reliable conditional distribution alignment. Two benchmark datasets were used to verify the presented MCTLP. The results showed that MCTLP outperformed other existing methods, demonstrating its strong ability for cross-subject transfer.