Chen Chen, Yu Yang, Sheng Xinjun, Zhu Xiangyang
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:4791-4794. doi: 10.1109/EMBC44109.2020.9176362.
Surface electromyography (EMG) decomposition techniques have been applied for human-machine interfacing by decoding neural information, while most of decomposition approaches work offline. Here, we apply an online decomposition scheme to decode motor unit activities during three motor tasks, and measure the recognition accuracy of motor type and activation level using the decomposition results. High-density surface EMG signal were recorded from forearm muscles of six able-bodied subjects. The EMG signals were decomposed into motor unit spike trains (MUST) with a sliding window of 100 ms. The computation complexity had time consumption < 50 ms in each window. Most identified motor units discharged during only one motor task. On average, over 5 MUSTs were identified for each motion and the recognition accuracy based on motor unit activities was > 99%. The discharge rate of motor units was highly correlated with the activation level of each motion with an average correlation coefficient of 0.94 ± 0.04. These results indicate the feasibility of an online, multi-motion, and proportional control scheme based on neural decoding in a non-invasive way.
表面肌电图（EMG）分解技术已通过解码神经信息应用于人机交互，而大多数分解方法是离线工作的。在此，我们应用一种在线分解方案来解码三项运动任务期间的运动单位活动，并使用分解结果测量运动类型和激活水平的识别准确率。从六名身体健全的受试者的前臂肌肉记录高密度表面肌电图信号。肌电图信号通过100毫秒的滑动窗口被分解为运动单位放电序列（MUST）。每个窗口中的计算复杂度耗时<50毫秒。大多数识别出的运动单位仅在一项运动任务期间放电。平均而言，每个动作识别出超过5个运动单位放电序列，基于运动单位活动的识别准确率>99%。运动单位的放电率与每个动作的激活水平高度相关，平均相关系数为0.94±0.04。这些结果表明了一种基于神经解码的在线、多动作和比例控制方案以非侵入性方式的可行性。
