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.

翻译：sEMG模式识别算法在解码运动意图方面已被广泛探索，但已知其易受记录条件变化影响，在不同受试者之间甚至不同实验轮次之间表现出显著的性能下降。多通道表面肌电（也称为高密度sEMG，HD-sEMG）系统通过使用额外电极收集信息来提升性能。然而，由于数据集的限制以及难以解决诸如电极放置等变异性来源，鲁棒性不足的问题始终存在。在本研究中，我们提出在输入通道子集集合上进行训练，并通过来自不同电极位置的数据增强训练分布，同时解决电极偏移问题和降低输入维度。我们的方法增强了对抗电极偏移的鲁棒性，并显著提升了跨受试者和分类算法的轮次间性能。