Abstract:

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.

Key words: Consciousness disorders, Brain-computer interfaces, Neural networks, computer, Medicine-engineering convergenece (not in MeSH), Review

摘要：

慢性意识障碍的精准评估与干预是神经科学的重大挑战。脑机接口技术通过解码脑电信号、重建意识沟通、引导闭环神经调控，为意识障碍的诊断与治疗开辟新的路径。诊断方面，脑电图和功能性近红外光谱成像可检测大脑信号判断意识水平；功能性沟通方面，初步验证基于脑电图的脑机接口系统有效，多种范式各有优劣；康复方面，基于脑机接口反馈的电刺激疗法可改善症状，但特定的康复系统尚待完善。然而，脑机接口的应用面临假阴性率高、结果稳定性不足等挑战。未来应跨学科合作，优化技术以提高信号处理的准确性和实时性，发挥血管内脑机接口的优势，采用多模态方法全面评估和治疗意识障碍，推动其临床应用与发展。

关键词: 意识障碍, 脑-机接口, 神经网络, 计算机, 医工融合(非MeSH词), 综述

CLC Number: 

• R741

• TP381