Performance‐Recoverable Closed‐Loop Neuroprosthetic System

Abstract

Abstract Soft bioelectronics mechanically comparable to living tissues have driven advances in closed‐loop neuroprosthetic systems for the recovery of sensory‐motor functions. Despite notable progress in this field, critical challenges persist in achieving long‐term stable closed‐loop neuroprostheses, particularly in preventing uncontrolled drift in the electrical sensitivity and/or charge injection performance owing to material fatigue or mechanical damage. Additionally, the absence of an intelligent feedback loop has limited the ability to fully compensate for sensory‐motor function loss in nervous systems. Here, a novel class of soft, closed‐loop neuroprosthetic systems is presented for long‐term operation, enabled by spontaneous performance recovery and machine‐learning‐driven correction to address the material fatigue inherent in chronic wear or implantation environments. Central to this innovation is the development of a tough, self‐healing, and stretchable bilayer material with high conductivity and exceptional cyclic durability employed for robot‐interface touch sensors and peripheral‐nerve‐adaptive electrodes. Furthermore, two central processing units, integrated in a prosthetic robot and an artificial brain, support closed‐loop artificial sensory‐motor operations, ensuring accurate sensing, decision‐making, and feedback stimulation processes. Through these characteristics and seamless integration, our performance‐recoverable closed‐loop neuroprosthesis addresses challenges associated with chronic‐material‐fatigue‐induced malfunctions, as demonstrated by successful in vivo under 4 weeks of implantation and/or mechanical damage.