Chai Xiaoke, Wang Nan, Song Jiuxiang, Yang Yi
Brain-Computer Interface Translation Research Center, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, P. R. China.
Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, P. R. China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2025 Jun 25;42(3):447-454. doi: 10.7507/1001-5515.202502027.
Clinical grading diagnosis of disorder of consciousness (DOC) patients relies on behavioral assessment, which has certain limitations. Combining multi-modal technologies and brain-computer interface (BCI) paradigms can assist in identifying patients with minimally conscious state (MCS) and vegetative state (VS). This study collected electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals under motor BCI paradigms from 14 DOC patients, who were divided into two groups based on clinical scores: 7 in the MCS group and 7 in the VS group. We calculated event-related desynchronization (ERD) and motor decoding accuracy to analyze the effectiveness of motor BCI paradigms in detecting consciousness states. The results showed that the classification accuracies for left-hand and right-hand movement tasks using EEG were 93.28% and 76.19% for the MCS and VS groups, respectively; the classification precisions using fNIRS were 53.72% and 49.11% for these groups. When combining EEG and fNIRS features, the classification accuracies for left-hand and right-hand movement tasks in the MCS and VS groups were 95.56% and 87.38%, respectively. Although there was no statistically significant difference in motor decoding accuracy between the two groups, significant differences in ERD were observed between different consciousness states during left-hand movement tasks ( < 0.001). This study demonstrates that motor BCI paradigms can assist in assessing the level of consciousness, with EEG being more sensitive for evaluating residual motor intention intensity. Moreover, the ERD feature of motor intention intensity is more sensitive than BCI classification accuracy.
意识障碍（DOC）患者的临床分级诊断依赖于行为评估，而行为评估存在一定局限性。结合多模态技术和脑机接口（BCI）范式有助于识别最低意识状态（MCS）和植物状态（VS）的患者。本研究在运动BCI范式下收集了14名DOC患者的脑电图（EEG）和功能近红外光谱（fNIRS）信号，这些患者根据临床评分分为两组：MCS组7例，VS组7例。我们计算了事件相关去同步化（ERD）和运动解码准确率，以分析运动BCI范式在检测意识状态方面的有效性。结果表明，MCS组和VS组使用EEG对左手和右手运动任务的分类准确率分别为93.28%和76.19%；使用fNIRS的分类精度分别为53.72%和49.11%。当结合EEG和fNIRS特征时，MCS组和VS组左手和右手运动任务的分类准确率分别为95.56%和87.38%。虽然两组之间的运动解码准确率没有统计学上的显著差异，但在左手运动任务期间，不同意识状态之间的ERD存在显著差异（<0.001）。本研究表明，运动BCI范式有助于评估意识水平，其中EEG在评估残余运动意图强度方面更敏感。此外，运动意图强度的ERD特征比BCI分类准确率更敏感。
