在沉浸式VR-BCI康复期间,基于EEG的运动恢复预测器
Madalena Valente1, Diogo Branco2,3, Sergi Bermúdez I Badia2,3
1Bioengineering Department, Institute for Systems and Robotics - Lisboa, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal.
Scientific reports
|February 9, 2026
概括
由事件相关脱同步 (ERD) 测量的基线脑活动模式可以预测脑电脑接口 (BCI) 和虚拟现实 (VR) 康复中风患者的运动恢复. 在ERD的短期变化没有显示预测价值.
科学领域:
- 神经科学是一个神经科学.
- 康复医学 康复医学 康复医学
- 生物医学工程 生物医学工程
背景情况:
- 脑卒中后的运动障碍会导致长期严重的残疾和降低生活质量.
- 脑计算机接口 (BCI) 系统与运动图像 (MI) 和虚拟现实 (VR) 结合,显示了增强神经可塑性和改善患者参与的潜力.
- 确定可靠的基于脑电图 (EEG) 的生物标志物来预测中风恢复至关重要,但仍然具有挑战性.
研究的目的:
- 调查MI-VR训练期间与事件相关的脱同步 (ERD) 动态和慢性中风幸存者的运动恢复之间的关系.
- 为了确定基线ERD特征或ERD随时间变化是否预测了运动功能的改善.
主要方法:
- 14名患有慢性中风的参与者接受了为期4周的VR-BCI干预,其中9名在实验组,5名在对照组.
- 收集了EEG数据以评估运动图像任务期间的ERD.
- 使用线性混合效应模型和两阶段回归来分析ERD调制及其与Fugl-Meyer评估 (FMA) 评分的相关性.
主要成果:
- 与非中风对照队伍相比,中风参与者表现出明显减少的ERD.
- 在中风组内的训练课程中没有观察到ERD的显著变化.
- 基线ERD振幅是运动改善 (FMA增长) 的重要预测指标,而ERD进展则不是.
- 在缺血性中风患者中发现了双侧ERD的补偿趋势.
结论:
- 基线ERD振幅似乎是慢性中风中运动恢复的更强大的预后生物标志物,而不是短期ERD动态.
- 这些发现支持开发个性化VR-BCI康复策略,以个体患者的生物标志物量身定制.
- 进一步的研究可以改进EEG生物标志物的使用,以优化中风康复结果.
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