Kumar Akshay, Gao Lin, Li Jiaming, Ma Jiaxin, Fu Jianming, Gu Xudong, Mahmoud Seedahmed S, Fang Qiang
Department of Biomedical Engineering, College of Engineering, Shantou University, Shantou, China.
OMRON SINIC X Corporation, Tokyo, Japan.
Front Neurorobot. 2022 Apr 25;16:837119. doi: 10.3389/fnbot.2022.837119. eCollection 2022.
Conventional rehabilitation systems typically execute a fixed set of programs that most motor-impaired stroke patients undergo. In these systems, the brain, which is embodied in the body, is often left out. Including the brains of stroke patients in the control loop of a rehabilitation system can be worthwhile as the system can be tailored to each participant and, thus, be more effective. Here, we propose a novel brain-computer interface (BCI)-based robot-assisted stroke rehabilitation system (RASRS), which takes inputs from the patient's intrinsic feedback mechanism to adapt the assistance level of the RASRS. The proposed system will utilize the patients' consciousness about their performance decoded through their error-related negativity signals. As a proof-of-concept, we experimented on 12 healthy people in which we recorded their electroencephalogram (EEG) signals while performing a standard rehabilitation exercise. We set the performance requirements beforehand and observed participants' neural responses when they failed/met the set requirements and found a statistically significant ( < 0.05) difference in their neural responses in the two conditions. The feasibility of the proposed BCI-based RASRS was demonstrated through a use-case description with a timing diagram and meeting the crucial requirements for developing the proposed rehabilitation system. The use of a patient's intrinsic feedback mechanism will have significant implications for the development of human-in-the-loop stroke rehabilitation systems.
传统的康复系统通常执行一套固定的程序，大多数运动功能受损的中风患者都会接受这些程序。在这些系统中，作为身体一部分的大脑往往被忽视。将中风患者的大脑纳入康复系统的控制回路可能是值得的，因为这样系统可以针对每个参与者进行定制，从而更有效。在此，我们提出一种基于新型脑机接口（BCI）的机器人辅助中风康复系统（RASRS），该系统从患者的内在反馈机制获取输入，以调整RASRS的辅助水平。所提出的系统将利用通过与错误相关的负性信号解码的患者对自身表现的意识。作为概念验证，我们对12名健康人进行了实验，在他们进行标准康复锻炼时记录他们的脑电图（EEG）信号。我们预先设定了表现要求，并观察参与者在未达到/达到设定要求时的神经反应，发现两种情况下他们的神经反应存在统计学显著差异（<0.05）。通过带有时序图的用例描述以及满足开发所提出的康复系统的关键要求，证明了所提出的基于BCI的RASRS的可行性。患者内在反馈机制的使用将对人在回路中风康复系统的开发产生重大影响。
