Anchoring Motion, Refining Trajectories: Generalizable Continuous Neural Decoding

Brain activity encodes motor information across multiple temporal scales, enabling continuous neural control beyond discrete action recognition. However, existing methods are typically tailored to specific subjects and tasks, with direct neural-to-kinematic mappings that can entangle subject-specific neural variability with task-dependent motion patterns, limiting generalization. To address these limitations, we introduce AMRT, a shared neural decoding framework for heterogeneous electrode configurations and movement paradigms. Motivated by cortical encoding of movement direction and speed, AMRT adopts a two-stage design that anchors local motion through velocity estimation and refines the resulting trajectories over longer horizons, encouraging reuse of local motion tendencies across subjects and tasks. Across three datasets, AMRT generalizes to unseen trials and subjects and demonstrates transfer potential across neural recordings and movement paradigms, highlighting the potential of shared neural representations and anchor-and-refine decoding for generalizable continuous neural control. Our code is available at https://anonymous.4open.science/r/AMRT.