| Sumario: | Accurate assessment and intervention for prolonged consciousness disorder remain critical challenges in neuroscience. Brain-computer interface (BCI) offers a novel pathway for diagnosis and treatment of consciousness disorder by decoding neural signals, reconstructing conscious communication, and guiding closed - loop neuromodulation. In diagnostic applications, EEG and functional near - infrared spectroscopy (fNIRS) detect brain signals to differentiate consciousness levels, while auditory P300 and steady - state visual evoked potential (SSVEP) paradigms show promise in identifying residual cognitive function. For communication, EEG - based BCI enable functional interaction in select patients, though accuracy and stability vary across modalities. Rehabilitation strategies leveraging BCI, such as neurofeedback and sensorimotor rhythm modulation, demonstrate potential for neural plasticity enhancement, particularly when combined with electrical stimulation. However, challenges persist, including high false- negative rates, signal instability, and limited generalizability of algorithms. Future directions emphasize interdisciplinary collaboration to optimize signal processing, validate multimodal approaches, and explore endovascular BCI for enhanced spatial resolution. Bridging neuroscientific insights with engineering innovations will be pivotal in translating BCI into clinical tools for improving outcomes in prolonged consciousness disorder patients.
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