Detect-Then-Adapt: Continuous-Stream Neural Decoding for Online Adaptation

Intracortical brain-computer interfaces (iBCIs) hold great promise for restoring motor and communication abilities in individuals with paralysis. However, the inherent nonstationarity of neural systems poses a major challenge to the long-term stability of iBCI systems. To avoid frequent recalibration, existing approaches typically apply predefined domain adaptation techniques to adjust the neural decoder prior to each use, yet they fail to consider when and how the adaptation should be performed. As a result, decoder performance can vary substantially under different neural signal conditions. In this work, we propose a **Detect-Then-Adapt** framework for long-term iBCI operation. The framework first *detects* whether decoder re-adaptation is needed based on continuous neural input, and then automatically constructs a suitable adapter tailored to the current state of neural signals when adaptation is required. Experiments on multi-session motor recordings from two non-human primates demonstrate that continuous neural activity provides informative cues for both detecting and readapting to neural signal drifts. Our method consistently outperforms domain-adaptation and neural-alignment baselines, highlighting the potential of leveraging continuous neural streams as a practical foundation for reliable, long-term iBCI adaptation.