Tryon Jacob, Friedman Evan, Trejos Ana Luisa
IEEE Int Conf Rehabil Robot. 2019 Jun;2019:971-976. doi: 10.1109/ICORR.2019.8779465.
Wearable robotic systems have shown potential to improve the lives of musculoskeletal disorder patients; however, to be used practically, they require a reliable method of control. The user needs to be able to indicate that they wish to move in a way that feels intuitive and comfortable. One proposed method for detecting motion intention is through the combined use of muscle activity, known as electromyography (EMG), and brain activity, known as electroencephalography (EEG). Other groups have developed various methods of fusing EEG/EMG signals for classification of motion intention, but a comprehensive evaluation of their performance has yet to be completed. This work evaluates EEG/EMG fusion methods during elbow flexion-extension motion while varying parameters, such as speed of motion, weight held, and muscle fatigue. Overall, the use of EEG/EMG fusion was found to not be more accurate than using just EMG alone $(86.81 \pm 3.98$%), with some fusion methods demonstrating equivalent performance to EMG $(p=1.000)$. EEG/EMG fusion was, however, demonstrated to be less sensitive to changes in motion parameters, allowing it to perform more consistently across different speed/weight combinations. The results of this work provide further justification for the use of EEG/EMG fusion for control of a wearable robotic device.
可穿戴机器人系统已显示出改善肌肉骨骼疾病患者生活的潜力；然而，要实际应用，它们需要一种可靠的控制方法。用户需要能够以一种直观且舒适的方式表明他们想要移动。一种检测运动意图的提议方法是通过结合使用肌肉活动（即肌电图，EMG）和大脑活动（即脑电图，EEG）。其他团队已经开发出各种融合EEG/EMG信号以对运动意图进行分类的方法，但对其性能的全面评估尚未完成。这项工作在肘关节屈伸运动期间评估EEG/EMG融合方法，同时改变运动速度、握持重量和肌肉疲劳等参数。总体而言，发现使用EEG/EMG融合并不比仅使用EMG更准确（86.81±3.98%），一些融合方法表现出与EMG相当的性能（p = 1.000）。然而，EEG/EMG融合对运动参数变化的敏感性较低，使其能够在不同速度/重量组合下更稳定地运行。这项工作的结果为使用EEG/EMG融合来控制可穿戴机器人设备提供了进一步的依据。
