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混合BCI用于上肢康复:将MI与外围场SSVEP刺激集成在一起

Ruoqing Zhang¹, Zhaohui Li¹, Xiaohu Pan²

¹State Key Laboratory of Advanced Medical Materials and Devices, Institute of Biomedical Engineering, Tianjin Institutes of Health Science, Chinese Academy of Medical Science and Peking Union Medical College, Tianjin, China; Tianjin Key Laboratory of Neuromodulation and Neurorepair, Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China.

Journal of neuroscience methods

|July 18, 2025

概括

这项研究引入了一种混合脑计算机接口 (BCI) 系统,将运动图像 (MI) 和稳定状态视觉唤起潜力 (SSVEP) 结合起来,用于中风康复,在闭环实验中实现高精度和可行性.

科学领域:

神经科学是一个神经科学.
生物医学工程生物医学工程
康复技术康复技术康复技术

背景情况:

大脑-计算机接口 (BCI) 为中风患者的康复提供了显著的潜力.
目前的BCI主要使用运动图像 (MI),其性能和用户舒适性受到限制.
这项研究通过开发增强的混合BCI系统来解决这些局限性.

研究的目的:

开发和验证一个混合BCI系统,将MI与稳定状态视觉唤起潜能 (SSVEPs) 集成在一起.
为了提高基于BCI的中风患者康复系统的性能和用户舒适性.
建立一个闭环系统,用柔软的机器人手来增强反.

主要方法:

设计了一个混合BCI系统,结合了运动图像 (MI) 和稳定状态视觉唤起潜能 (SSVEPs).
使用外周视野刺激 (在34 Hz和35 Hz的同心环) 诱导SSVEPs.
一个闭环系统包含一个柔软的机器人手来提供反,并为同步MI和SSVEP任务提供中央视频指导.

主要成果:

线下 MI 的分类准确度为 70.65% ± 3.38%,SSVEP 的分类准确度为 96.04% ± 3.33%.
合并的BCI系统实现了96.23% ± 3.21%的离线准确度.
在线实验表明,在11名受试者中,系统可行性准确率为97.12% ± 2.09%.

结论:

结合MI和SSVEP的混合BCI系统是可行的和有效的,正如线下和在线实验所证实的那样.
拟议的系统提高了用户的舒适性,同时保持了高性能.
这一进步有望改善中风康复中的临床应用.

关键词:

运动图像中的运动图像.周边电场是一个外围电场.康复康复康复康复软软的机器人手套稳定状态视觉唤起了潜在的潜力.

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