Reliable AI Platform for Monitoring BCI Caused Brain Injury and Providing Real‐Time Protection

Abstract Invasive brain‐computer interface (BCI) holds great promise for restoring motor, sensory, and cognitive functions in patients with disabilities, yet chronic implantation induces neuroinflammation and degeneration at the electrode–tissue interface, impairing neural connectivity and device long‐term stability. Current brain injury assessment approaches cannot simultaneously meet the requirements of efficiency and interpretability in healthcare with high‐risk diagnoses and treatment. Meanwhile, limited and expensive biomechanics data pose significant challenges in AI training. Herein, feature‐based Gaussian process emulators are proposed to enable interpretable data‐driven modeling with limited biomechanics data under noise. Furthermore, a reliable AI platform, BrainGuard is developed, for efficiently providing a reliable and quantitative patient‐specific basis and real‐time monitoring of BCI caused brain injury. These results demonstrate exceptional performance of BrainGuard in rapidly and accurately predicting and monitoring the full‐field von Mises strain revealing the brain injury even under challenging noise conditions. By constructing interpretable digital brain twins to offer reliable digital healthcare solutions, the platform enhances real‐time patient protection and improves the security and durability of long‐term BCI‐based measurement and treatment strategies.