Bias in Surface Electromyography Features across a Demographically Diverse Cohort

TL;DR

This study investigates how demographic factors like age and BMI influence surface electromyography features, revealing that a significant portion of features vary with demographics, impacting the fairness of neural interfaces.

Contribution

It provides a comprehensive analysis of demographic effects on sEMG features, highlighting the need for bias mitigation in neural interface development.

Findings

33% of common sEMG features are significantly associated with demographic variables.

Demographic factors such as age, sex, and body composition influence sEMG signal characteristics.

Results can inform the design of more equitable and personalized neural interface systems.

Abstract

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.