Multidisciplinary integration and application in Brain-Computer Interface (BCI) technology

Abstract

Brain–Computer Interface (BCI) technology embodies the deep integration of materials science, computer science, and medicine, offering a transformative pathway for neurorehabilitation, neurological disease treatment, and human–computer interaction. Advances in flexible nanomaterials such as graphene, carbon nanotubes, black phosphorus, and layered double hydroxides have effectively addressed the limitations of traditional rigid electrodes by enhancing biocompatibility, electrical conductivity, and mechanical compliance, while minimizing immune rejection and tissue injury. Concurrently, artificial intelligence–based decoding algorithms, including CNN– Kalman Filter (CNN-KF) architectures and AI-assisted control systems, have greatly improved motion intention recognition accuracy and real-time adaptability. Medical research further complements these developments by guiding electrode placement, ensuring biosafety, and validating long-term clinical performance. This crossdisciplinary synergy forms a closed-loop framework of “material design – algorithm optimization – clinical verification,” promoting BCIs toward high precision, low invasiveness, and stable functionality. Collectively, these advances establish the foundation for next-generation intelligent neural interfaces capable of restoring motor function, facilitating cognitive rehabilitation, and achieving seamless interaction between humans and machines.