[论文解读] BCI-Based Strategies on Stroke Rehabilitation with Avatar and FES Feedback

Zhaoyang Qiu, Shugeng Chen+4 more|arXiv (Cornell University)|May 14, 2018

EEG and Brain-Computer Interfaces参考文献 30被引用 5

一句话总结

本研究提出了一种闭环脑-机接口-FES（功能性电刺激）系统，整合了基于运动想象的脑-机接口、功能性电刺激（FES）以及虚拟形象反馈，以增强中风后运动功能的康复效果。通过实时解码运动想象的脑电信号，当检测到预期的运动意图时，系统会触发对患侧肢体的FES刺激，从而改善运动功能：至第4周时，运动想象的平均准确率提升至71.3%；五名患者在Fugl-Meyer评估量表上的得分有所提高，其中一名患者在中风两周年后评分提升了15分。

ABSTRACT

Stroke is the leading cause of serious and long-term disability worldwide. Some studies have shown that motor imagery (MI) based BCI has a positive effect in poststroke rehabilitation. It could help patients promote the reorganization processes in the damaged brain regions. However, offline motor imagery and conventional online motor imagery with feedback (such as rewarding sounds and movements of an avatar) could not reflect the true intention of the patients. In this study, both virtual limbs and functional electrical stimulation (FES) were used as feedback to provide patients a closed-loop sensorimotor integration for motor rehabilitation. The FES system would activate if the user was imagining hand movement of instructed side. Ten stroke patients (7 male, aged 22-70 years, mean 49.5+-15.1) were involved in this study. All of them participated in BCI-FES rehabilitation training for 4 weeks.The average motor imagery accuracies of the ten patients in the last week were 71.3%, which has improved 3% than that in the first week. Five patients' Fugl-Meyer Assessment (FMA) scores have been raised. Patient 6, who has have suffered from stroke over two years, achieved the greatest improvement after rehabilitation training (pre FMA: 20, post FMA: 35). In the aspect of brain patterns, the active patterns of the five patients gradually became centralized and shifted to sensorimotor areas (channel C3 and C4) and premotor area (channel FC3 and FC4).In this study, motor imagery based BCI and FES system were combined to provided stoke patients with a closed-loop sensorimotor integration for motor rehabilitation. Result showed evidences that the BCI-FES system is effective in restoring upper extremities motor function in stroke. In future work, more cases are needed to demonstrate its superiority over conventional therapy and explore the potential role of MI in poststroke rehabilitation.

研究动机与目标

• 开发一种闭环BCI-FES系统，将实时运动想象反馈与功能性电刺激整合，用于中风后上肢康复。
• 通过在BCI反馈中引入神经肌肉激活（即FES），解决传统BCI反馈（如声音、虚拟形象）的局限性，以更真实地反映患者的运动意图。
• 评估该集成系统在改善慢性中风患者运动功能及脑网络重组方面的有效性。
• 探究将BCI与FES及视觉反馈结合，是否能带来比传统BCI反馈更显著的运动功能改善及神经生理学模式变化。

提出的方法

• 基于运动想象的BCI系统通过C3和C4导联采集脑电信号，以检测预期的手部运动意图。
• 当系统以高置信度检测到指定侧的运动想象时，即在患侧上肢触发FES刺激。
• 患者通过虚拟形象实时接收视觉反馈，显示预期肢体的运动动作。
• 利用分类算法处理脑电信号，以区分左、右手运动想象任务。
• 通过同步想象运动与视觉反馈，实现FES激活与运动想象的协同，构建闭环感觉运动整合。
• FES作用于患侧手部的伸肌和屈肌，以诱发自然的肌肉收缩。

实验结果

研究问题

• RQ1 能否通过整合运动想象、实时FES与虚拟形象反馈的BCI-FES系统，改善慢性中风患者的运动功能？
• RQ2 将FES与BCI及视觉反馈结合，是否比仅使用传统BCI反馈带来更显著的运动恢复效果？
• RQ3 在BCI-FES训练过程中，大脑运动网络如何重组？其EEG模式变化如何体现？
• RQ4 与离线或简单在线反馈系统相比，该系统在多大程度上真实反映了患者的运动意图？

主要发现

• 十名患者的平均运动想象准确率从基线水平提升至训练末周的71.3%，提高了3个百分点。
• 五名患者在Fugl-Meyer评估（FMA）量表上的得分出现可测量的改善，其中一名患者（患者6）在中风两周年后FMA评分提升了15分（从20分增至35分）。
• EEG分析显示，活跃的脑电活动模式变得更加集中，并向感觉运动区（C3、C4）及前运动区（FC3、FC4）转移，提示存在神经可塑性重组。
• 该系统成功通过同步想象运动与FES激活，实现了闭环感觉运动整合，增强了身体归属感与运动意图的表达。
• BCI-FES系统在慢性中风患者中表现出可行性与有效性，即使在病程较长的病例中亦能促进运动功能恢复。
• 将FES与BCI及虚拟形象反馈结合，相比传统反馈方式，提供了更直观、更具意图驱动的康复体验。