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A high-density surface EMG framework for the study of motor neurons controlling the intrinsic and extrinsic muscles of the hand

机译:高密度表面肌电图框架,用于研究控制手部内在和外在肌肉的运动神经元

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We propose a framework based on high-density surface electromyography (HD-sEMG) to identify the neural drive to muscles controlling the human hand. High-density (320 channels) sEMG signals were recorded concurrently from intrinsic (the four dorsal interossei and thenar) and extrinsic (forearm) hand muscles and then decomposed into the constituent trains of motor unit (MU) action potentials. The participants performed pinch tasks with simultaneous activation of the thumb and one of the other fingers with sinusoidal force variations. The common drive among MUs across different muscles was extracted via principal component analysis (PCA) of the smoothed MU discharge rates. The first principal component of the smoothed discharge rates of all identified motor neurons explained 48.7 ± 15.4% of the total variance across all pinching tasks, indicating a common neural input shared by different muscles of the forearm and the hand.. When considering only the MUs extracted from extrinsic and intrinsic muscles, the percent of variance explained was 48.3 ± 15.3% and 57.1 ± 15.5%, respectively. This framework is conceived to use motor neuron activity for a proportional myoelectric control and rehabilitation technologies. A wearable adaptation of the framework is proposed for future perspectives.
机译:我们提出了一个基于高密度表面肌电图(HD-sEMG)的框架,以识别控制人手肌肉的神经驱动。高密度(320通道)sEMG信号是同时从内在(四背骨和鱼际)和外在(前臂)手部肌肉记录的,然后分解为运动单元(MU)动作电位的组成序列。参与者通过正弦力变化同时激活拇指和另一只手指之一来执行捏捏任务。通过平滑的MU放电速率的主成分分析(PCA)提取了MU在不同肌肉之间的共同驱动力。所有已识别的运动神经元的平滑放电率的第一个主要成分解释了所有捏捏任务的总方差的48.7±15.4%,表明前臂和手的不同肌肉共享一个共同的神经输入。提取自外部和内部肌肉的方差百分比分别为48.3±15.3%和57.1±15.5%。该框架被构想为将运动神经元活动用于成比例的肌电控制和康复技术。提出了该框架的可穿戴适应性,以供将来使用。

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