A confidence-gated source selection strategy for cross-session transfer in brain-computer interfaces

Cross-session variability remains a major obstacle to the reliable operation of motor imagery (MI)-based brain-computer interfaces (BCI), particularly when systems are reused across multiple days. When multiple prior sessions from the same subject are available, two key questions arise before domain transfer: which source sessions to select and how to effectively utilize them. We address these questions by developing confidence-gated, selective-transfer pipelines: a Minimum-Distance Multi-Source Pipeline (MMP) that only uses source sessions close to the target, and a Bridge Domain Pipeline (BDP) that exploits both near and far sources to improve robustness. We evaluated these novel pipelines against uniform-pooling (MAP) and distance-weighted-pooling (DWP) baseline methods on two public motor imagery EEG datasets under matched experimental configurations of feature extraction, classification, and domain adaptation algorithms. The benchmark results support an endpoint-specific interpretation rather than a single accuracy ranking. Specifically, MAP achieved the highest maximum-configuration accuracy, DWP demonstrated the highest average accuracy across configurations, and on the primary dataset MAP, DWP, and BDP exhibited no statistically significant differences as the top-performing pipelines for data-driven configuration selection. In contrast, MMP_mta performed similarly under fixed configurations but proved less effective during data-driven configuration selection. Overall, the best-performing proposed pipeline (BDP) ranks among the highest-performing approaches with respect to accuracy while requiring substantially reduced execution time and fewer source sessions than full pooling-demonstrating that BDP transforms the CI-gated retention idea into a more reliable and computationally efficient framework for automated configuration selection. These results indicate that in cross-session MI decoding, the primary challenge in selective transfer extends beyond session selection alone to encompass how retained sessions are integrated downstream, offering significant implications for longitudinal rehabilitation and assistive BCI use.