通过脑电检测意念运动起止，实现外骨骼辅助下的自然控制。
Characterizing the onset and offset of motor imagery during passive arm movements induced by an upper-body exoskeleton
- 用脑电信号识别意念运动的开始与结束时机。
- 离线解码准确率平均达60.7%（开始）和66.6%（停止）。
- 适合康复机器人、非侵入式脑机接口研究者参考。
近年来，机器人与脑机接口（BMIs）在运动康复领域备受关注。二者结合具有巨大临床潜力，但挑战在于：在穿戴康复外骨骼引发的被动运动和仪器噪声干扰下，能否通过非侵入式BMI准确捕捉用户运动意图。本研究提出一种替代连续控制的新思路——在上肢外骨骼诱导的被动臂动过程中，识别意念运动（MI）的起始与终止。10名参与者在机器人上执行右臂本体感觉意念运动，同时由LED提示目标抓取任务的起止。利用脑电图信号构建解码器，检测两阶段转换：ⅰ）静息到意念运动开始；ⅱ）维持到意念运动结束。离线评估显示，群体平均起始准确率为60.7%，终止准确率为66.6%，证明在佩戴外骨骼时仍可有效识别意念运动的起止。伪在线测试重现了该性能，预示未来可实现可靠的外骨骼在线控制。结果表明，即使存在干扰，用户仍能产生质量稳定、可依赖的皮层节律，为助行设备的脑控提供了新路径。
原文摘要 · Abstract (English)
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.
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