Zhang Xinyi, Xie Lanfang, Liu Wanting, Liang Shaoying, Huang Liyao, Wang Mingjun, Tian Lingling, Zhang Li, Liang Zhen, Li Hai, Huang Gan
School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, 518060, Guangdong, China.
Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen, 518060, Guangdong, China.
J Neuroeng Rehabil. 2025 Apr 26;22(1):97. doi: 10.1186/s12984-025-01627-7.
Brain-computer interfaces (BCIs) hold significant potential for post-stroke motor recovery, yet active movement-based BCIs face limitations in generalization due to inter-subject variability. This study investigates passive movement-based BCIs, driven by exoskeleton-guided rehabilitation, to address these challenges by evaluating electroencephalogram (EEG) responses and algorithmic generalization in both healthy subjects and stroke patients.
EEG signals were recorded from 20 healthy subjects and 10 stroke patients during voluntary and passive hand movements. Time and time-frequency domain analyses were performed to examine the event-related potential (ERP), event-related desynchronization (ERD), and synchronization (ERS) patterns. The performance of two BCI algorithms, Common Spatial Patterns (CSP) and EEGNet, was evaluated in both within-subject and cross-subject decoding tasks.
Time-domain and time-frequency analyses revealed that passive movements elicited stronger, more consistent ERPs in healthy subjects, particularly in bilateral motor cortices (contralateral: μV; ipsilateral: μV). Stroke patients exhibited impaired mu/beta ERD/ERS in the affected hemisphere during voluntary movements but demonstrated EEG patterns during passive movements resembling those of healthy subjects. Machine learning evaluation highlighted EEGNet's superior performance, achieving 84.19% accuracy in classifying affected vs. unaffected movements in patients, surpassing healthy subject left-right discrimination (58.38%). Cross-subject decoding further validated passive movement efficacy, with EEGNet attaining 86.00% (healthy) and 72.63% (stroke) accuracy, outperforming traditional CSP methods.
These findings underscore that passive movement elicits consistent neural responses, thereby enhancing the generalizability of decoding algorithms for stroke patients. By integrating exoskeleton-evoked proprioceptive feedback, this paradigm reduces inter-subject variability and improves clinical feasibility. Future work should explore the application of exoskeletons in the combination of active and passive movement for stroke rehabilitation.
脑机接口（BCIs）在中风后运动恢复方面具有巨大潜力，但基于主动运动的脑机接口由于个体差异在泛化方面存在局限性。本研究调查了由外骨骼引导康复驱动的基于被动运动的脑机接口，通过评估健康受试者和中风患者的脑电图（EEG）反应及算法泛化来应对这些挑战。
在健康受试者和中风患者进行自主和被动手部运动期间记录EEG信号。进行时域和时频域分析以检查事件相关电位（ERP）、事件相关去同步化（ERD）和同步化（ERS）模式。在受试者内和跨受试者解码任务中评估了两种脑机接口算法——共同空间模式（CSP）和EEGNet的性能。
时域和时频分析表明，被动运动在健康受试者中引发更强、更一致的ERP，特别是在双侧运动皮层（对侧： μV；同侧： μV）。中风患者在自主运动期间受影响半球的μ/β ERD/ERS受损，但在被动运动期间表现出与健康受试者相似的EEG模式。机器学习评估突出了EEGNet的优越性能，在对患者受影响与未受影响运动进行分类时准确率达到84.19%，超过了健康受试者左右辨别率（58.38%）。跨受试者解码进一步验证了被动运动的有效性，EEGNet的准确率分别为86.00%（健康受试者）和72.63%（中风患者），优于传统的CSP方法。
这些发现强调被动运动引发一致的神经反应，从而提高中风患者解码算法的泛化能力。通过整合外骨骼诱发的本体感觉反馈，该范式减少了个体差异并提高了临床可行性。未来的工作应探索外骨骼在中风康复主动和被动运动结合中的应用。
