IEEE Trans Neural Syst Rehabil Eng. 2013 Sep;21(5):849-59. doi: 10.1109/TNSRE.2013.2267851. Epub 2013 Jun 18.
We propose a system for the neuro-motor rehabilitation of upper limbs in stroke survivors. The system is composed of a passive robotic device (Trackhold) for kinematic tracking and gravity compensation, five dedicated virtual reality (VR) applications for training of distinct movement patterns, and high-resolution EEG for synchronous monitoring of cortical activity. In contrast to active devices, the Trackhold omits actuators for increased patient safety and acceptance levels, and for reduced complexity and costs. VR applications present all relevant information for task execution as easy-to-understand graphics that do not need any written or verbal instructions. High-resolution electroencephalography (HR-EEG) is synchronized with kinematic data acquisition, allowing for the epoching of EEG signals on the basis of movement-related temporal events. Two healthy volunteers participated in a feasibility study and performed a protocol suggested for the rehabilitation of post-stroke patients. Kinematic data were analyzed by means of in-house code. Open source packages (EEGLAB, SPM, and GMAC) and in-house code were used to process the neurological data. Results from kinematic and EEG data analysis are in line with knowledge from currently available literature and theoretical predictions, and demonstrate the feasibility and potential usefulness of the proposed rehabilitation system to monitor neuro-motor recovery.
我们提出了一种用于脑卒中后上肢神经运动康复的系统。该系统由一个用于运动跟踪和重力补偿的被动机器人装置（Trackhold）、五个用于不同运动模式训练的专用虚拟现实（VR）应用程序和用于同步监测皮质活动的高分辨率脑电图（EEG）组成。与主动式设备相比，Trackhold 省略了执行器，以提高患者的安全性和接受度，降低复杂性和成本。VR 应用程序将执行任务所需的所有相关信息呈现为易于理解的图形，无需任何书面或口头指令。高分辨率脑电图（EEG）与运动数据采集同步，允许根据与运动相关的时间事件对 EEG 信号进行分段。两名健康志愿者参与了一项可行性研究，并按照建议的脑卒中后患者康复方案进行了操作。运动学数据通过内部代码进行分析。使用开源软件包（EEGLAB、SPM 和 GMAC）和内部代码对神经数据进行处理。运动学和脑电图数据分析的结果与现有文献和理论预测的知识相符，证明了所提出的康复系统用于监测神经运动康复的可行性和潜在有用性。
