UPCSD: Unsupervised Prefix Calibration with State-Dynamic Modeling for Long-Term Intracortical Neural Decoding

Cross-day neural drift degrades decoding performance in intracortical brain-computer interfaces (iBCIs), challenging robust long-term decoding under practical deployment constraints. We propose unsupervised prefix calibration with state–dynamic modeling (UPCSD), which uses the first 30 s of unlabeled test-day neural activity to correct prefix-estimable first-order cross-day variation and training-time augmentation to improve robustness to residual recording variation. A state–dynamic representation separately models slowly varying neural states and fast multiscale fluctuations, which are fused and decoded by a lightweight LSTM. Chronological offline evaluation on four iBCI datasets covering center-out (CO) and random target tracking (RTT) tasks showed that UPCSD achieved the numerically highest mean daily $R^2$ on each dataset. Mean daily $R^2$ ranged from 0.62 to 0.85 within 100 days and remained 0.57 and 0.46 over the full 700- and 757-day spans of Link-CO and Link-RTT, respectively. Under strictly causal inference using only past and current neural activity, UPCSD achieved the numerically highest mean daily $R^2$ on these four datasets and 0.672 $\pm$ 0.091 across 12 online test days on an additional NW-CO dataset. On the Lynxi HE200, on-chip inference closely matched GPU decoding performance while reducing active device power by approximately 92%. These results support UPCSD as a label-free approach for practical, long-term iBCI decoding.