This paper provides a comprehensive review of state-of-the-art brain neurochip technologies designed to restore visual perception in individuals affected by blindness. The survey encompasses key neural interfacing strategies, including intracortical, epi-retinal, subretinal, and optic nerve stimulation approaches, and highlights their respective advantages, limitations, and clinical applicability. Particular focus is placed on the signal acquisition and processing pipeline, where multimodal sensor data is pre-processed, encoded, and mapped to neural activation patterns. Advanced stimulation paradigms ranging from simple current-based techniques to spatiotemporal patterned stimulation are evaluated in terms of their ability to evoke interpretable precepts. The role of machine-learning algorithms in optimizing visual encoding and decoding is examined, with emphasis on adaptive, patient-specific models that can dynamically improve performance through closed-loop feedback. Building upon these foundations, we propose a prototype framework for a minimally invasive brain neurochip system. The framework integrates hybrid sensing (e.g., camera-based and bio-signal-driven inputs), adaptive stimulation tailored to neural plasticity, and real-time closed-loop feedback for continuous performance optimization. Safety-by-design principles are embedded into the architecture, ensuring resilience against overstimulation, thermal effects, and long-term biocompatibility risks. Furthermore, we outline potential performance metrics—including visual acuity restoration, task-specific functionality, and quality-of-life impact—as well as experimental validation steps necessary to move from laboratory prototypes toward large-scale preclinical and clinical studies. Finally, ethical considerations, regulatory challenges, and translational pathways are discussed to provide a roadmap for accelerating the integration of brain neurochip vision restoration systems into clinical practice.
Vishal Khanna (Sun,) studied this question.