Nanhu Brain-Computer Interface Institute

A preprint proposes UnSPC (uncertainty-guided self-paced cyclic learning), a framework that integrates domain adaptation (DA) and domain generalization (DG) in an iterative cycle to address neural drift in long-term invasive brain-machine interfaces, where drift erodes decoding performance and forces frequent recalibration and where existing methods rely on DA or DG alone. Under an uncertainty-guided, self-paced pseudo-labeling scheme with a noise-robust ranking strategy, UnSPC iteratively mines reliable pseudo-labeled samples and, through cyclic adaptation and generalization, gradually mitigates both global and sub-domain drift. Experiments on multiple neural decoding datasets validated its effectiveness and robustness; the authors say it is the first method to integrate DA and DG through a pseudo-label loop. The study has not been peer reviewed.

A preprint proposes SSCDL, a self-supervised consistency-enhanced disentangled learning framework that decomposes motor signals into velocity, direction, and speed to capture representations invariant to neural drift and significantly enhance cross-day decoding generalization for invasive BMIs. The researchers report state-of-the-art decoding performance with high robustness and cross-day stability across extensive experiments; the work has not yet been peer reviewed.