High-Density surface Electromyography (HDsEMG) has emerged as a pivotal resource for Human-Computer Interaction (HCI), offering direct insights into muscle activities and motion intentions. However, a significant challenge in practical implementations of HD-sEMG-based models is the low accuracy of inter-session and inter-subject classification. Variability between sessions can reach up to 40% due to the inherent temporal variability of HD-sEMG signals. Targeting this challenge, the paper introduces the MoEMba framework, a novel approach leveraging Selective StateSpace Models (SSMs) to enhance HD-sEMG-based gesture recognition. The MoEMba framework captures temporal dependencies and cross-channel interactions through channel attention techniques. Furthermore, wavelet feature modulation is integrated to capture multi-scale temporal and spatial relations, improving signal representation. Experimental results on the CapgMyo HD-sEMG dataset demonstrate that MoEMba achieves a balanced accuracy of 56.9%, outperforming its state-of-the-art counterparts. The proposed framework's robustness to session-to-session variability and its efficient handling of high-dimensional multivariate time series data highlight its potential for advancing HD-sEMG-powered HCI systems.

翻译：高密度表面肌电图已成为人机交互的关键资源，能够直接反映肌肉活动与运动意图。然而，基于HD-sEMG的模型在实际应用中面临的主要挑战是跨会话与跨被试分类准确率较低。由于HD-sEMG信号固有的时间变异性，不同会话间的准确率差异最高可达40%。针对这一挑战，本文提出MoEMba框架——一种利用选择性状态空间模型增强基于HD-sEMG手势识别的新方法。该框架通过通道注意力技术捕捉时间依赖性与跨通道交互作用，并集成小波特征调制以捕获多尺度时空关系，从而改善信号表征能力。在CapgMyo HD-sEMG数据集上的实验结果表明，MoEMba实现了56.9%的平衡准确率，性能优于现有先进方法。所提框架对会话间变异性的鲁棒性及其对高维多变量时间序列数据的高效处理能力，彰显了其在推进HD-sEMG驱动的人机交互系统发展方面的潜力。