Abstract
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.