Learning Transferable Cross-Day Representations for Few-Shot Neural Decoding

Intracortical brain–computer interfaces (iBCIs) can restore movement and communication, but neural drift can degrade performance across recording sessions. Because recalibration adds user burden, there is a need for decoders that adapt from minimal calibration data. Although session-specific input layers are a common way to handle cross-session drift, they can underperform in the extreme few-shot regime because the shared decoder is trained downstream of well-estimated session-specific layers, but must rely at deployment on a new-session layer fit from only a few calibration trials. To avoid this dependence, we propose a two-stage approach: first, train a single GRU decoder on pooled held-in sessions without session-specific input layers, encouraging the backbone to handle cross-session variability directly; second, freeze this backbone and meta-learn a lightweight alignment layer for rapid calibration. We evaluate on FALCON H2, an official human handwriting iBCI benchmark designed for few-shot cross-session decoding, and achieve 7.41\% word error rate using only three released calibration sentences per held-out session. These results suggest that, under extreme calibration limits, learning a transferable backbone before adding session-specific adaptation can be more effective than jointly training flexible session-specific layers.