Xie Ping, Wang Zihao, Li Zengyong, Wang Ying, Wang Nianwen, Liang Zhenhu, Wang Juan, Chen Xiaoling
Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, China.
National Research Center for Rehabilitation Technical Aids, Beijing, China.
Front Aging Neurosci. 2022 Jun 30;14:892178. doi: 10.3389/fnagi.2022.892178. eCollection 2022.
It is difficult for stroke patients with flaccid paralysis to receive passive rehabilitation training. Therefore, virtual rehabilitation technology that integrates the motor imagery brain-computer interface and virtual reality technology has been applied to the field of stroke rehabilitation and has evolved into a physical rehabilitation training method. This virtual rehabilitation technology can enhance the initiative and adaptability of patient rehabilitation. To maximize the deep activation of the subjects motor nerves and accelerate the remodeling mechanism of motor nerve function, this study designed a brain-computer interface rehabilitation training strategy using different virtual scenes, including static scenes, dynamic scenes, and VR scenes. Including static scenes, dynamic scenes, and VR scenes. We compared and analyzed the degree of neural activation and the recognition rate of motor imagery in stroke patients after motor imagery training using stimulation of different virtual scenes, The results show that under the three scenarios, The order of degree of neural activation and the recognition rate of motor imagery from high to low is: VR scenes, dynamic scenes, static scenes. This paper provided the research basis for a virtual rehabilitation strategy that could integrate the motor imagery brain-computer interface and virtual reality technology.
对于患有弛缓性麻痹的中风患者来说,接受被动康复训练存在困难。因此,将运动想象脑机接口与虚拟现实技术相结合的虚拟康复技术已应用于中风康复领域,并发展成为一种物理康复训练方法。这种虚拟康复技术可以提高患者康复的主动性和适应性。为了最大程度地深度激活受试者的运动神经并加速运动神经功能的重塑机制,本研究设计了一种使用不同虚拟场景的脑机接口康复训练策略,包括静态场景、动态场景和虚拟现实场景。我们比较并分析了使用不同虚拟场景刺激进行运动想象训练后中风患者的神经激活程度和运动想象识别率。结果表明,在这三种场景下,神经激活程度和运动想象识别率从高到低的顺序为:虚拟现实场景、动态场景、静态场景。本文为整合运动想象脑机接口和虚拟现实技术的虚拟康复策略提供了研究依据。