High-Density surface Electromyography (HD-sEMG) is the most established technique for the non-invasive analysis of single motor unit (MU) activity in humans. It provides the possibility to study the central properties (e.g., discharge rate) of large populations of MUs by analysis of their firing pattern. Additionally, by spike-triggered averaging, peripheral properties such as MUs conduction velocity can be estimated over adjacent regions of the muscles and single MUs can be tracked across different recording sessions. In this tutorial, we guide the reader through the investigation of MUs properties from decomposed HD-sEMG recordings by providing both the theoretical knowledge and practical tools necessary to perform the analyses. The practical application of this tutorial is based on openhdemg, a free and open-source community-based framework for the automated analysis of MUs properties built on Python 3 and composed of different modules for HD-sEMG data handling, visualisation, editing, and analysis. openhdemg is interfaceable with most of the available recording software, equipment or decomposition techniques, and all the built-in functions are easily adaptable to different experimental needs. The framework also includes a graphical user interface which enables users with limited coding skills to perform a robust and reliable analysis of MUs properties without coding.

高密度表面肌电图 (HD-sEMG) 是对人类单运动单位 (MU) 活动进行非侵入性分析的最成熟的技术。它提供了通过分析大量 MU 的放电模式来研究其中心特性（例如放电率）的可能性。此外，通过尖峰触发平均，可以估计肌肉相邻区域的 MU 传导速度等外围特性，并且可以在不同的记录会话中跟踪单个 MU。在本教程中，我们通过提供执行分析所需的理论知识和实用工具，引导读者从分解的 HD-sEMG 记录中研究 MU 属性。本教程的实际应用基于openhdemg，这是一个免费的开源社区框架，用于自动分析 MU 属性，基于 Python 3 构建，由用于 HD-sEMG 数据处理、可视化、编辑和分析的不同模块组成。openhdemg可与大多数可用的记录软件、设备或分解技术接口，并且所有内置功能都可以轻松适应不同的实验需求。该框架还包括一个图形用户界面，使编码技能有限的用户无需编码即可对 MU 属性进行稳健且可靠的分析。