脑电/肌电融合运动分类方法的性能评价

Jacob Tryon, Evan Friedman, A. L. Trejos
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引用次数: 18

摘要

可穿戴机器人系统已经显示出改善肌肉骨骼疾病患者生活的潜力;然而,要实际使用,它们需要可靠的控制方法。用户需要能够表明他们希望以一种直观和舒适的方式移动。一种被提出的检测运动意图的方法是通过肌肉活动(称为肌电图(EMG))和大脑活动(称为脑电图(EEG))的结合使用。其他研究小组已经开发了多种融合脑电/肌电信号的运动意图分类方法,但尚未完成对其性能的综合评价。本研究评估了手肘屈伸运动中脑电图/肌电图融合方法在不同参数下的表现,如运动速度、负重和肌肉疲劳。总的来说,使用脑电图/肌电图融合并不比单独使用肌电图更准确(86.81 \pm 3.98$%),一些融合方法的表现与肌电图相当(p=1.000)$。然而,脑电图/肌电图融合被证明对运动参数的变化不太敏感,这使得它在不同的速度/重量组合中表现得更加一致。这项工作的结果为使用脑电图/肌电图融合来控制可穿戴机器人设备提供了进一步的理由。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance Evaluation of EEG/EMG Fusion Methods for Motion Classification
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.
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