Zhejiang University

A review analyses technical, hardware, and user-level bottlenecks facing SSVEP-BCI systems in real-world deployment, summarising solutions including EMG fusion denoising, novel sponge electrodes, and hybrid paradigms.

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 The study has not been peer reviewed.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decoding performance with high robustness and cross-day stability across extensive experiments; the work has not yet been peer reviewed.

A collaborative study involving authors from Hangzhou Dianzi University and other institutions developed a three-dimensional helical stretchable neural probe and achieved 1024-channel signal acquisition in the pig cerebral cortex. The researchers also report single-unit recordings with long-term stability in the mouse deep brain, while flexible planar microelectrode arrays recorded subcutaneous EMG signals in mice. The probe is fabricated using planar microfabrication followed by thermally driven helical shaping, expanding wiring space and accommodating tissue motion. Mechanical simulations and cyclic stretching tests examined strain mitigation and electrochemical stability, respectively. These device and animal results do not establish long-term performance in humans.

Researchers at Lanzhou University, Zhejiang University, the University of Hong Kong and Sun Yat-sen University have built MindShow, a unified generative framework that reconstructs images from fMRI at cohort level rather than one subject at a time. A hierarchically conditioned mixture-of-experts encoder separates population-shared latent representations from subject-specific neural traits, so a new subject can be adapted with limited calibration data; a gated Perceiver bottleneck maps fMRI features into fixed-size image and text tokens, an optimal transport loss aligns them with a pretrained vision-language model, and a frozen diffusion model renders the image. The authors report better high-level reconstruction metrics with competitive structural fidelity, in a study published in Medical Image Analysis on June 20, 2026.