Chinese Academy of Sciences

Researchers at the Technical Institute of Physics and Chemistry of the Chinese Academy of Sciences and collaborating institutions have developed a neural electrode interface that, they report, significantly extended electrode operational lifetime and improved signal fidelity over six months of in vivo implantation. The material, a benzyloxycarbonyl-substituted poly(ornithine-alt-glycine) coating abbreviated OGCbz, resists biofouling and immunogenic rejection without degrading electrical performance, targeting what the authors call the critical obstacle to electrodes that combine long-term recording with tunable biofunctional control: an immune-mediated foreign body response in which glial scar encapsulates implants like cement. Substituting the Cbz group with other functional moieties preserves those antifouling and biocompatibility properties while adding new ones, and as a proof of concept the peptide sequence IKVAV and the antibody cetuximab gave the interface neuron affinity and tumor cell proliferation inhibition respectively; the six-month result came from the IKVAV-functionalized version.

BCI research has long been slowed by inconsistent data formats, divergent decoder implementations and incompatible deployment toolchains. BCIJelly standardizes 18 BCI datasets into inputs ready for AI training and integrates 15 benchmark decoders and 80 reusable modules. Its automated architecture search generates task-specific decoders without manual design and can extend into a large language model-driven closed-loop mode that supports single-task, multitask and cross-species decoder design; the system also offers interactive visualization software that requires no coding. A single-command pipeline compiles trained decoders onto neuromorphic hardware, cutting power consumption 30- to 50-fold while maintaining decoding performance. The work has been validated in humans, macaques and mice across five paradigms: motor, visual, speech, emotion and auditory. It is a preprint that has not been peer reviewed.

A game theory-based adaptive human-machine joint learning method for online MI-BCI decoding improves accuracy by 9.7% and 5.6% in 14-subject online experiments (both p < 0.01).