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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:一种高密度表面EMG框架,用于研究手动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:我们提出了一种基于高密度表面肌电图(HD-肌电)一个框架,以神经驱动识别肌肉控制人的手。高密度(320个信道)表面肌电信号从固有同时录制(四个背骨间和鱼际)和外源性(前臂)手部肌肉,然后分解成马达单元(MU)的动作电位的构成列车。与会者用大拇指的同时激活和其他手指正弦力的变化中的一个执行捏任务。亩之间跨越不同的肌肉的共同驱动经由平滑MU放电速率的主成分分析(PCA)萃取。所有识别出的运动神经元的平滑放电速率的第一主成分解释的总方差的48.7±15.4%在所有捏任务,表示由不同的肌肉前臂和手的共享的公共神经输入。当仅考虑亩从外在和内在的肌肉萃取,方差的百分比解释为48.3±15.3%和57.1±15.5%,分别。这个框架设想使用运动神经元活动比例肌电控制和康复技术。该框架的可佩带式改编,提出了未来的前景。

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