U2Multi-UDA: A Unified Multilevel Multisource Unsupervised Domain Adaptation Method for Motor Imagery.

Abstract

Motor imagery (MI) is a core paradigm in Brain-computer interface (BCI) research, but its practical application remains limited by intersubject variability and the scarcity of labeled target-domain data. Existing methods usually focus on a single adaptation level, such as domain alignment, feature interaction, or model fine-tuning, which limits comprehensive cross-domain adaptation (DA). To address this issue, this study proposes U2Multi-UDA, a unified multilevel multisource unsupervised DA framework for MI decoding. U2Multi-UDA integrates these three adaptation levels into a single pipeline. First, optimal transport (OT) aligns source and target distributions, while mutual information estimates source-domain relevance weights to characterize the contribution of each source domain to the target domain. Second, spatio-temporal electroencephalography (EEG) features are extracted and fused through multisource cross-attention, where the source-domain relevance weights guide cross-domain feature fusion, and pseudolabels enhance target-domain feature learning. Finally, segmented weight-decomposed low-rank adaptation (DoRA) enables parameter-efficient target-domain fine-tuning while reducing overfitting. Experiments on BCI Competition IV 2a, BCI Competition IV 2b, and the self-constructed MI-GS dataset show that U2Multi-UDA improves mean accuracy by 2.69, 1.89, and 3.83 percentage points, respectively, over the best-performing baselines, with consistent gains in Kappa values. Ablation and sensitivity analyses further confirm the effectiveness, robustness, and physiological plausibility of the proposed framework.