Sheng Wang, Xiaobin Song, Xiaopan Song, Xiaopan Song, Zhuangzhuang Cong, Yang Gu, Zhuangzhuang Cong, Yi Shen, Linwei Yu
BCI technology holds considerable promise in ophthalmology, but significant technical and translational challenges remain. Future advances in artificial intelligence, flexible electronics, virtual reality, wireless systems, and closed-loop strategies are expected to improve precision, safety, adaptability, and accessibility, thereby enabling more effective diagnostic and visual rehabilitation solutions for patients with severe visual impairment.
The development of non-invasive brain-computer interfaces (BCIs) relies on multidisciplinary integration across neuroscience, artificial intelligence, flexible electronics, and systems engineering. Recent advances in deep learning have significantly improved the accuracy and robustness of neural signal decoding. Parallel progress in electrode design-particularly through the use of flexible and stretchable materials like nanostructured conductors and novel fabrication strategies-has enhanced wearability and operational stability. Nevertheless, key challenges persist, including individual variability, biocompatibility limitations, and susceptibility to interference in complex environments. Further validation and optimization are needed to address gaps in generalization capability, long-term reliability, and real-world operational robustness. This review systematically examines the representative progress in neural decoding algorithms and flexible bioelectronic platforms over the past decade, highlighting key design principles, material innovations, and integration strategies that are poised to advance non-invasive BCI capabilities. It also discusses the importance of multimodal data fusion, hardware-software co-optimization, and closed-loop control strategies. Furthermore, the review discusses the application potential and associated engineering challenges of this technology in clinical rehabilitation and industrial translation, aiming to provide a reference for advancing non-invasive BCIs toward practical and scalable deployment.