Data-Efficient Cross-Session Adaptation for Brain–Computer Interfaces via Structured Drift Modeling

Long-term brain–computer interface (BCI) use requires neural decoders that can rapidly recover stable performance across recording sessions despite substantial neural nonstationarity. Existing cross-session adaptation methods often rely on batch-style recalibration and relatively high-dimensional parameter updates, limiting their effectiveness when only a small amount of new-session data is available. To address this limitation, we present the Distribution-based Drift Adaptation (DiDA) framework, which captures session-specific drift state through a compact distributional representation while preserving shared latent dynamics and a stable behavioral readout. By adapting this distribution rather than re-optimizing the full observation mapping, DiDA enables efficient cross-session adaptation as new trials become available sequentially. We further enable adaptation by leveraging lightweight feedback as a weak supervisory signal, rather than relying on purely unsupervised objectives. We evaluate the framework on motor cortical recordings from macaques performing center-out and random-target reaching tasks. Our experiments examine trial-by-trial adaptation under supervised, unsupervised, and preference-guided objectives, with offline batch fitting serving as a performance reference. Across these settings, DiDA enables rapid, data-efficient cross-session recalibration, highlighting its potential for long-term BCI use. Code is available at https://anonymous.4open.science/r/DiDA-3069/.