Aghchehli Eisa, Jabbari Milad, Ma Chenfei, Dyson Matthew, Nazarpour Kianoush
School of Engineering, Newcastle University, Newcastle upon Tyne, United Kingdom.
School of Informatics, The University of Edinburgh, Edinburgh, United Kingdom.
Front Neurorobot. 2025 Apr 30;19:1531815. doi: 10.3389/fnbot.2025.1531815. eCollection 2025.
Electromyography (EMG) systems are essential for the advancement of neuroprosthetics and human-machine interfaces. However, the gap between low-density and high-density systems poses challenges to researchers in experiment design and knowledge transfer. Medium-density surface EMG systems offer a balanced alternative, providing greater spatial resolution than low-density systems while avoiding the complexity and cost of high-density arrays. In this study, we developed a research-friendly medium-density EMG system and evaluated its performance with eleven volunteers performing grasping tasks. To enhance decoding accuracy, we introduced a novel spatio-temporal convolutional neural network that integrates spatial information from additional EMG sensors with temporal dynamics. The results show that medium-density EMG sensors significantly improve classification accuracy compared to low-density systems while maintaining the same footprint. Furthermore, the proposed neural network outperforms traditional gesture decoding approaches. This work highlights the potential of medium-density EMG systems as a practical and effective solution, bridging the gap between low- and high-density systems. These findings pave the way for broader adoption in research and potential clinical applications.
肌电图（EMG）系统对于神经假体和人机接口的发展至关重要。然而，低密度和高密度系统之间的差距给研究人员在实验设计和知识转移方面带来了挑战。中密度表面肌电图系统提供了一种平衡的选择，它比低密度系统具有更高的空间分辨率，同时避免了高密度阵列的复杂性和成本。在本研究中，我们开发了一种便于研究的中密度肌电图系统，并让11名志愿者执行抓握任务来评估其性能。为了提高解码精度，我们引入了一种新颖的时空卷积神经网络，该网络将来自额外肌电图传感器的空间信息与时间动态相结合。结果表明，与低密度系统相比，中密度肌电图传感器在保持相同占地面积的情况下显著提高了分类精度。此外，所提出的神经网络优于传统的手势解码方法。这项工作突出了中密度肌电图系统作为一种实用且有效解决方案的潜力，弥合了低密度和高密度系统之间的差距。这些发现为其在研究和潜在临床应用中的更广泛采用铺平了道路。
