Two distinct technologies have gained attention lately due to their prospects for motor rehabilitation: robotics and brain-machine interfaces (BMIs). Harnessing their combined efforts is a largely uncharted and promising direction that has immense clinical potential. However, a significant challenge is whether motor intentions from the user can be accurately detected using non-invasive BMIs in the presence of instrumental noise and passive movements induced by the rehabilitation exoskeleton. As an alternative to the straightforward continuous control approach, this study instead aims to characterize the onset and offset of motor imagery during passive arm movements induced by an upper-body exoskeleton to allow for the natural control (initiation and termination) of functional movements. Ten participants were recruited to perform kinesthetic motor imagery (MI) of the right arm while attached to the robot, simultaneously cued with LEDs indicating the initiation and termination of a goal-oriented reaching task. Using electroencephalogram signals, we built a decoder to detect the transition between i) rest and beginning MI and ii) maintaining and ending MI. Offline decoder evaluation achieved group average onset accuracy of 60.7% and 66.6% for offset accuracy, revealing that the start and stop of MI could be identified while attached to the robot. Furthermore, pseudo-online evaluation could replicate this performance, forecasting reliable online exoskeleton control in the future. Our approach showed that participants could produce quality and reliable sensorimotor rhythms regardless of noise or passive arm movements induced by wearing the exoskeleton, which opens new possibilities for BMI control of assistive devices.

翻译：两种技术因在运动康复领域的潜力近期备受关注：机器人技术与脑机接口（BMIs）。将两者协同应用仍是亟待探索且极具临床前景的方向。然而，关键挑战在于：在康复外骨骼产生的仪器噪声和被动运动干扰下，能否通过非侵入式脑机接口准确检测用户的运动意图。本研究摒弃传统的连续控制范式，转而旨在刻画上肢外骨骼被动运动期间运动想象的启动与终止特征，以实现功能性运动的自然控制（启动与终止）。招募十名受试者在与机器人连接状态下执行右臂动觉运动想象（MI），同时通过LED指示灯同步提示目标导向抓取任务的启动与终止。利用脑电图信号构建解码器，分别检测以下状态转换：i) 静息态到MI起始，ii) MI维持到MI终止。离线解码器评估显示，组平均起始检测准确率达60.7%，终止检测准确率达66.6%，证明在佩戴机器人时仍可识别MI的启动与停止。伪在线评估进一步验证该性能，预示未来可实现可靠的在线外骨骼控制。研究表明，无论外骨骼穿戴引发的噪声或被动手臂运动如何，受试者均能产生高质量且稳定的感觉运动节律，这为基于脑机接口的辅助设备控制开辟了新路径。