Butt Maryam, Naghdy Golshah, Naghdy Fazel, Murray Geoffrey, Du Haiping
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:2841-2844. doi: 10.1109/EMBC44109.2020.9175459.
Multi-session robot-assisted stroke rehabilitation program requires patients to perform repetitive tasks. It is challenging for the patient to maintain concentration during training sessions. A novel intervention strategy using Electroencephalography (EEG) signals is proposed to maintain concentration during training by enhancing the engagement of stroke patients using robot-assisted multi-session rehabilitation. The approach is illustrated by applying it to one stroke patient undergoing 12 training sessions of hand motor training on the AMADEO rehabilitation device. AMADEO offers four modes of training programs of increased intensity comprising passive training, passive training with biofeedback, assistive training as well as active 2D training games. The EEG signals are measured over eight electrode sites: FC4, C4, CP4, FC3, C3, CP3, Cz, and CPz during each training day to extract movement-related cortical potential (MRCP) signals. Moreover, functional hand recovery parameters are determined using the AMADEO assessment tool. The patient's level of engagement is determined by the negative amplitude of the MRCP signal. The rehabilitation program is switched to a more intense training mode when a consistent decrease is observed in the negative amplitude of MRCP signals from the monitored electrodes. Using this approach, the rehabilitation program becomes patient-specific and adaptive. In addition, it is shown that each training mode exhibits a different recovery level of the affected hand and maximum recovery is achieved when MRCP signals indicate that the patient is actively participating in the training.
多阶段机器人辅助中风康复计划要求患者执行重复性任务。患者在训练过程中保持专注具有挑战性。提出了一种使用脑电图（EEG）信号的新型干预策略，通过在机器人辅助的多阶段康复中增强中风患者的参与度来在训练期间保持专注。通过将其应用于一名在AMADEO康复设备上进行12次手部运动训练的中风患者来说明该方法。AMADEO提供四种强度递增的训练计划模式，包括被动训练、带生物反馈的被动训练、辅助训练以及主动二维训练游戏。在每个训练日，在八个电极部位（FC4、C4、CP4、FC3、C3、CP3、Cz和CPz）测量EEG信号，以提取与运动相关的皮层电位（MRCP）信号。此外，使用AMADEO评估工具确定手部功能恢复参数。患者的参与程度由MRCP信号的负振幅确定。当观察到来自监测电极的MRCP信号负振幅持续下降时，康复计划切换到更强化的训练模式。使用这种方法，康复计划变得针对患者且具有适应性。此外，结果表明，每种训练模式对手部的恢复程度不同，当MRCP信号表明患者积极参与训练时可实现最大程度的恢复。
