There is a growing interest in decomposing high-density surface electromyography (HDsEMG) into motor unit spike trains to improve knowledge on the neural control of muscle contraction. However, the reliability of decomposition approaches is sometimes questioned, especially because they require manual editing of the outputs. We aimed to assess the inter-operator reliability of the identification of motor unit spike trains. Eight operators with varying experience in HDsEMG decomposition were provided with the same data extracted using the convolutive kernel compensation method. They were asked to manually edit them following established procedures. Data included signals from three lower leg muscles and different submaximal intensities. After manual analysis, 126 ± 5 motor units were retained (range across operators: 119–134). A total of 3380 rate of agreement values were calculated (28 pairwise comparisons × 11 contractions/muscles × 4–28 motor units). The median rate of agreement value was 99.6%. Inter-operator reliability was excellent for both mean discharge rate and time at recruitment (intraclass correlation coefficient > 0.99). These results show that when provided with the same decomposed data and the same basic instructions, operators converge toward almost identical results. Our data have been made available so that they can be used for training new operators.

人们越来越有兴趣将高密度表面肌电图（HDsEMG）分解为运动单位峰值序列，以提高对肌肉收缩的神经控制的认识。但是，有时会质疑分解方法的可靠性，特别是因为它们需要手动编辑输出。我们的目标是评估操作员之间的可靠性，以识别电机单元尖峰轮系。使用卷积核补偿方法为八位在HDsEMG分解方面具有不同经验的算子提供了相同的数据。他们被要求按照既定程序手动编辑它们。数据包括来自三条小腿肌肉和不同次最大强度的信号。经过手动分析后，保留了126±5个电机单元（操作员范围：119–134）。总共计算了3380个一致性值率（28个成对比较×11个收缩/肌肉×4–28个运动单位）。协议价值的中位数率为99.6％。作业者之间的可靠性对于平均出院率和平均招募时间都非常出色（类内相关系数> 0.99）。这些结果表明，当提供相同的分解数据和相同的基本指令时，运算符将趋向于几乎相同的结果。我们已经提供了数据，以便可以将其用于培训新操作员。这些结果表明，当提供相同的分解数据和相同的基本指令时，运算符将趋向于几乎相同的结果。我们已经提供了数据，以便可以将其用于培训新操作员。这些结果表明，当提供相同的分解数据和相同的基本指令时，运算符将趋向于几乎相同的结果。我们已经提供了数据，以便可以将其用于培训新操作员。