sEMG pattern recognition algorithms have been explored extensively in decoding movement intent, yet are known to be vulnerable to changing recording conditions, exhibiting significant drops in performance across subjects, and even across sessions. Multi-channel surface EMG, also referred to as high-density sEMG (HD-sEMG) systems, have been used to improve performance with the information collected through the use of additional electrodes. However, a lack of robustness is ever present due to limited datasets and the difficulties in addressing sources of variability, such as electrode placement. In this study, we propose training on a collection of input channel subsets and augmenting our training distribution with data from different electrode locations, simultaneously targeting electrode shift and reducing input dimensionality. Our method increases robustness against electrode shift and results in significantly higher intersession performance across subjects and classification algorithms.

通过使用多通道肌电信号识别算法，该研究提出通过训练不同通道子集的集合，并利用来自不同电极位置的数据来增加鲁棒性，同时解决电极变动和降低输入维度的问题，从而显著提高了跨受试者和分类算法的会话间性能。

利用HD-EMG电极子集解决手势识别中的电极偏移问题