Neuromotor decoding from upper-limb electromyography (sEMG) can enhance human-machine interfaces and offer a more natural means of controlling prosthetic limbs, virtual reality, and household electronics. Unfortunately, current sEMG technology does not always perform consistently across users because individual differences such as age and body mass index, among many others, can substantially alter signal quality. This variability makes sEMG characteristics highly idiosyncratic, often necessitating laborious personalization and iterative tuning to achieve reliable performance. This variability has particular import for sEMG-based assistive devices and neural interfaces, where demographic biases in sEMG features could undermine broad and fair deployment. In this study, we explore how demographic differences affect the sEMG signals produced and their implications for machine learning-based gesture decoding. We analyze the data set provided by, in which we derive 147 common sEMG features extracted from 81 demographically diverse individuals performing discrete hand gestures. Using mixed-effects linear models and partial least squares (PLS) analysis, which take into consideration demographic variables (including age, sex, height, weight, skin properties, subcutaneous fat, and hair density), we identify that 33\% (49 of 147) of commonly used sEMG features show significant associations with demographic characteristics. These results may help guide the development of fair and unbiased sEMG-based neural interfaces across a diverse population.

翻译：从上肢表面肌电信号（sEMG）进行神经运动解码能够增强人机接口，并为控制假肢、虚拟现实和家用电器提供更自然的手段。遗憾的是，当前sEMG技术在不同用户间性能并不一致，因为年龄、身体质量指数等个体差异会显著改变信号质量。这种变异性使得sEMG特征高度个体化，通常需要繁琐的个性化调整和迭代优化才能实现可靠性能。这种变异性对基于sEMG的辅助设备和神经接口尤为重要，因为sEMG特征中的人口统计学偏倚可能会阻碍其广泛而公平的部署。本研究探讨人口统计学差异如何影响sEMG信号生成，以及这些差异对基于机器学习的动作解码的启示。我们分析了提供的数据集，从中提取了81名具有人口统计学多样性的个体执行离散手势时产生的147个常用sEMG特征。通过使用考虑人口统计学变量（包括年龄、性别、身高、体重、皮肤特性、皮下脂肪和毛发密度）的混合效应线性模型和偏最小二乘回归分析，我们发现33%（147个中的49个）的常用sEMG特征与人口统计学特征显著相关。这些结果可能有助于指导开发面向多样人群的公平且无偏倚的基于sEMG的神经接口。