A System-Level Neuromorphic Framework for Closed-Loop Brain-Computer Interfaces toward Restorative Neuroprosthetic Control

Brain–computer interfaces (BCIs) and neuroprosthetic systems have demonstrated significant progress in decoding neural intent and enabling assistive motor and communication functions. However, achieving stable, long-term restoration of motor and autonomic function remains a central challenge in neural engineering, particularly under constraints of power efficiency, latency, and adaptability required for implantable systems. Neuromorphic computing offers a biologically inspired, event-driven computational paradigm that aligns closely with the operational requirements of closed-loop neural interfaces. This paper presents a system-level neuromorphic framework for closed-loop brain– computer interfaces aimed at restorative neuroprosthetic control. Rather than proposing a complete cognitive replacement, the framework is designed as a modular neural bypass that integrates spiking neural networks, bidirectional BCI architectures, and adaptive feedback mechanisms to support motor execution and autonomic regulation. The framework is grounded in established principles of neural engineering, neuroprosthetics, and implantable system design, with explicit consideration of biological integration, long-term stability, and ethical constraints. By articulating a coherent neuromorphic architecture tailored to closed-loop BCI operation, this work aims to provide a foundational reference for future experimental validation and translational development of neuromorphic neuroprosthetic systems within the neural engineering community.