Lara Jaime E, Cheng Leo K, Rohrle Oliver, Paskaranandavadivel Niranchan
IEEE Trans Biomed Eng. 2022 May;69(5):1758-1766. doi: 10.1109/TBME.2021.3131297. Epub 2022 Apr 21.
Dexterous hand motion is critical for object manipulation. Electrophysiological studies of the hand are key to understanding its underlying mechanisms. High-density electromyography (HD-EMG) provides spatio-temporal information about the underlying electrical activity of muscles, which can be used in neurophysiological research, rehabilitation and control applications. However, existing EMG electrodes platforms are not muscle-specific, which makes the assessment of intrinsic hand muscles difficult.
Muscle-specific flexible HD-EMG electrode arrays were developed to capture intrinsic hand muscle myoelectric activity during manipulation tasks. The arrays consist of 60 individual electrodes targeting 10 intrinsic hand muscles. Myoelectric activity was displayed as spatio-temporal amplitude maps to visualize muscle activation. Time-domain and temporal-spatial HD-EMG features were extracted to train cubic support vector machine machine-learning classifiers to classify the intended user motion.
Experimental data was collected from 5 subjects performing a range of 10 common hand motions. Spatio-temporal EMG maps showed distinct activation areas correlated to the muscles recruited during each movement. The thenar muscle fiber conduction velocity (CV) was estimated to be at 4.7±0.3 m/s for all subjects. Hand motions were successfully classified and average accuracy for all subjects was directly related to spatial resolution based on the number of channels used as inputs; ranging from 74±4% when using only 5 channels and up to 92±2% when using 41 channels. Temporal-spatial features were shown to provide increased motion-specific accuracy when similar muscles were recruited for different gestures.
Muscle-specific electrodes were capable of accurately recording HD-EMG signals from intrinsic hand muscles and accurately predicting motion.
The muscle-specific electrode arrays could improve electrophysiological research studies using EMG decomposition techniques to assess motor unit activity and in applications involving the analysis of dexterous hand motions.
灵活的手部动作对于物体操作至关重要。手部的电生理研究是理解其潜在机制的关键。高密度肌电图（HD-EMG）提供了有关肌肉潜在电活动的时空信息，可用于神经生理学研究、康复和控制应用。然而，现有的肌电图电极平台并非肌肉特异性的，这使得对手部固有肌肉的评估变得困难。
开发了肌肉特异性柔性HD-EMG电极阵列，以在操作任务期间捕获手部固有肌肉的肌电活动。该阵列由针对10块手部固有肌肉的60个独立电极组成。肌电活动以时空振幅图的形式显示，以可视化肌肉激活情况。提取时域和时空HD-EMG特征，以训练立方支持向量机机器学习分类器，对用户的预期动作进行分类。
从5名受试者执行一系列10种常见手部动作中收集了实验数据。时空肌电图显示了与每次运动中募集的肌肉相关的不同激活区域。所有受试者的鱼际肌纤维传导速度（CV）估计为4.7±0.3米/秒。手部动作被成功分类，所有受试者的平均准确率与基于用作输入的通道数量的空间分辨率直接相关；仅使用5个通道时为74±4%，使用41个通道时高达92±2%。当为不同手势募集相似肌肉时，时空特征显示出能提高动作特异性准确率。
肌肉特异性电极能够准确记录手部固有肌肉的HD-EMG信号并准确预测动作。
肌肉特异性电极阵列可改善使用肌电图分解技术评估运动单位活动的电生理研究，以及涉及灵活手部动作分析的应用。
