Department of Bioengineering, Faculty of Engineering, Imperial College London, London W12 7TA, UK.
Université Côte d'Azur, LAMHESS, Nice 06200, France; The University of Queensland, School of Biomedical Sciences, St Lucia 4072, QLD, Australia.
J Electromyogr Kinesiol. 2024 Aug;77:102886. doi: 10.1016/j.jelekin.2024.102886. Epub 2024 May 13.
We introduce the open-source software MUedit and we describe its use for identifying the discharge timing of motor units from all types of electromyographic (EMG) signals recorded with multi-channel systems. MUedit performs EMG decomposition using a blind-source separation approach. Following this, users can display the estimated motor unit pulse trains and inspect the accuracy of the automatic detection of discharge times. When necessary, users can correct the automatic detection of discharge times and recalculate the motor unit pulse train with an updated separation vector. Here, we provide an open-source software and a tutorial that guides the user through (i) the parameters and steps of the decomposition algorithm, and (ii) the manual editing of motor unit pulse trains. Further, we provide simulated and experimental EMG signals recorded with grids of surface electrodes and intramuscular electrode arrays to benchmark the performance of MUedit. Finally, we discuss advantages and limitations of the blind-source separation approach for the study of motor unit behaviour during tonic muscle contractions.
我们介绍了开源软件 MUedit,并描述了它在从使用多通道系统记录的各种肌电图 (EMG) 信号中识别运动单位放电时间方面的应用。MUedit 使用盲源分离方法进行 EMG 分解。之后,用户可以显示估计的运动单位脉冲串,并检查放电时间自动检测的准确性。必要时,用户可以校正放电时间的自动检测,并使用更新的分离向量重新计算运动单位脉冲串。在这里,我们提供一个开源软件和一个教程,指导用户(i)分解算法的参数和步骤,以及(ii)运动单位脉冲串的手动编辑。此外,我们提供使用表面电极网格和肌内电极阵列记录的模拟和实验性 EMG 信号,以基准测试 MUedit 的性能。最后,我们讨论了用于研究强直性肌肉收缩期间运动单位行为的盲源分离方法的优缺点。