State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, People's Republic of China.
Department of Bioengineering, Imperial College, London, United Kingdom.
J Neural Eng. 2021 Apr 6;18(5). doi: 10.1088/1741-2552/abf186.
Surface electromyography (EMG) decomposition techniques can be used to establish human-machine interfacing (HMI), but most investigations are implemented offline due to the computational load of the approach. Here, we generalize the offline decomposition algorithm to identify the motor unit (MU) activities in real time, and we propose a MU-based approach for online simultaneous and proportional control (SPC) of multiple motor tasks.High-density surface EMG signals recorded from forearm muscles were decomposed into motor unit spike trains (MUSTs) with the proposed decomposition method. The MUSTs were first pooled into clusters in the calibration phase and the cumulative discharges of active MUs in each group were extracted as the control signal for each motor task. Then the subjects were instructed to control a virtual cursor with multiple motor tasks involving grasp and wrist movements. Fifteen able-bodied subjects and two patients with limb deficiency participated in the experiments to validate the proposed control scheme.On average, over 20 MUSTs were identified in real time with an estimated decomposition accuracy>85%. The cumulative discharge in each pool was highly correlated with the activation of the specific motion (= 0.93 ± 0.05). Moreover, the proposed MU-based method had superior performance in online tests than conventional myo-control methods based on global EMG features.These results indicate the feasibility of real-time neural decoding in a non-invasive way. Moreover, the superior performance in online tests proves the potential of the MU-based approach for the SPC, promoting the application of EMG decomposition for HMI systems.
表面肌电图（EMG）分解技术可用于建立人机接口（HMI），但由于该方法的计算负荷，大多数研究都是离线实施的。在这里，我们将离线分解算法推广到实时识别运动单位（MU）活动，并提出了一种基于 MU 的在线同时和比例控制（SPC）多电机任务的方法。使用提出的分解方法，从前臂肌肉记录的高密度表面 EMG 信号分解为运动单位尖峰序列（MUST）。在标定阶段，首先将 MUST 聚类，然后提取每组中活动 MU 的累积放电作为每个运动任务的控制信号。然后，让受试者使用涉及抓握和手腕运动的多项运动任务来控制虚拟光标。15 名健康受试者和 2 名肢体缺失患者参与了实验，以验证所提出的控制方案。平均而言，实时识别出超过 20 个 MUST，估计的分解精度>85%。每个池中的累积放电与特定运动的激活高度相关（=0.93±0.05）。此外，与基于全局 EMG 特征的传统肌电控制方法相比，基于 MU 的方法在在线测试中具有更好的性能。这些结果表明非侵入性实时神经解码的可行性。此外，在线测试中的优异性能证明了基于 MU 的方法在 SPC 中的潜力，为 EMG 分解在 HMI 系统中的应用提供了支持。
