| Sumario: | Brain - computer interface (BCI) technology, by establishing direct communication pathways between the brain and external devices, has brought groundbreaking advancements to the diagnosis, treatment and rehabilitation of neurological disorders. A typical BCI system comprises the core steps of "signal acquisition - decoding - control - feedback", an architecture that aligns closely with closed- loop neuromodulation systems, such as responsive neurostimulation (RNS) for epilepsy, which follows a "recording - decoding - intervention" paradigm. Thus, closed - loop neuromodulation can be viewed as an integrated form of BCI designed for specific therapeutic objectives. Currently, BCI has demonstrated significant potential in replacing, restoring and enhancing neural functions across various clinical domains, including the management of drug-resistant epilepsy, motor rehabilitation post-stroke, neuromodulation for Parkinson's disease, functional compensation in spinal cord injury, and objective assessment of consciousness disorder. However, the technology still faces multiple challenges, including biocompatibility, signal stability, algorithmic generalizability, clinical standardization, and ethical-regulatory considerations. This review systematically examines the clinical progress of BCI, with the aim of outlining its technological classifications (non - invasive, partially invasive, and invasive), current applications, and key issues. Furthermore, it explores future directions, such as high - precision bidirectional closed - loop interaction, multimodal neuromodulation integration, intelligent virtual rehabilitation systems, and international collaborative standardization. Ultimately, this review seeks to contribute to the evolution of BCI from an assistive tool toward an intelligent integrated paradigm, laying the groundwork for precision neurology.
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