Larger and denser: an optimal design for surface grids of EMG electrodes to identify greater and more representative samples of motor units
Larger and denser: an optimal design for surface grids of EMG electrodes to identify greater and more representative samples of motor units
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更大、更密集：肌电图电极表面网格的优化设计，以识别更多、更具代表性的运动单位样本
DOI：
10.1101/2023.02.18.529050
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Caillet A
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作者：
Caillet A
脊髓运动神经元是唯一的神经细胞，其个体活动可以被非侵入性地识别。这通常使用表面肌电图（EMG）电极网格和源分离算法来完成;一种称为EMG分解的方法。在这项研究中，我们结合了计算和实验分析，以评估电极网格的设计参数如何影响所识别的电机单元的数量和属性。我们首先计算了运动单位的百分比，理论上可以区分在一个池中的200个模拟运动单位分解时记录的肌电图信号与网格的各种大小和电极间的距离（IED）。增加密度、电极数量和网格尺寸，增加了我们的分解算法理论上可以区分的电机单元的数量，即，高达模拟合并液的83.5%（条件范围：30.5-83.5%）。然后，我们确定了运动单位从实验肌电图信号记录在六个参与者与网格的各种大小（范围：2-36平方厘米）和IED（范围：4-16毫米）。具有最大数量的电极和最短IED的配置使识别的运动单元的数量（56 ± 14;范围：39-79）和这些样品中早期募集的运动单元的百分比（29 ± 14%）最大化。最后，确定的电机单元的数量进一步增加与原型网格的256个电极和IED的2毫米。两者合计，我们的研究结果表明，更大，更密集的表面网格的电极允许识别一个更具代表性的游泳池的电机单元比目前报道的实验研究。
The spinal motor neurons are the only neural cells whose individual activity can be noninvasively identified. This is usually done using grids of surface electromyographic (EMG) electrodes and source separation algorithms; an approach called EMG decomposition. In this study, we combined computational and experimental analyses to assess how the design parameters of grids of electrodes influence the number and the properties of the identified motor units. We first computed the percentage of motor units that could be theoretically discriminated within a pool of 200 simulated motor units when decomposing EMG signals recorded with grids of various sizes and interelectrode distances (IEDs). Increasing the density, the number of electrodes, and the size of the grids, increased the number of motor units that our decomposition algorithm could theoretically discriminate, i.e., up to 83.5% of the simulated pool (range across conditions: 30.5–83.5%). We then identified motor units from experimental EMG signals recorded in six participants with grids of various sizes (range: 2–36 cm2) and IED (range: 4–16 mm). The configuration with the largest number of electrodes and the shortest IED maximized the number of identified motor units (56 ± 14; range: 39–79) and the percentage of early recruited motor units within these samples (29 ± 14%). Finally, the number of identified motor units further increased with a prototyped grid of 256 electrodes and an IED of 2 mm. Taken together, our results showed that larger and denser surface grids of electrodes allow to identify a more representative pool of motor units than currently reported in experimental studies.