@inproceedings{tiwariQuantifyingBiologicalSex2026,

title = {Quantifying Biological Sex Leakage in Electroencephalography-Based Mental Workload Measurement and Its Impact on Model Performance},

author = {A. Tiwari and R. Arrabito and A. Davoust and T. H. Falk},

url = {https://www.scopus.com/pages/publications/105045575255?origin=resultslist},

doi = {10.1109/ICHMS69701.2026.11602273},

isbn = {979-833154511-6 (ISBN)},

year  = {2026},

date = {2026-01-01},

booktitle = {IEEE Int. Conf. Hum.-Mach. Syst., ICHMS},

pages = {392–397},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Detection of mental workload is essential for designing systems that can monitor and adapt to the needs of human operators, thereby ensuring their optimal performance and well-being. Features derived from neurophysiological signals, particularly electroencephalograms (EEG), provide a realtime, unobtrusive, and objective indicator of mental workload. However, EEG signals are often correlated with demographic variables, including biological sex and age, which can introduce systematic performance differences across demographic groups, while unintentionally encoding demographic information into models trained on EEG data. In this work, we investigate and quantify the influence of user demographics on mental workload detection models using fairness analysis. Furthermore, we propose domain-adversarial training, in which an adversary penalizes the model for retaining sex-related information, to mitigate leakage of private demographic information (biological sex). The proposed approach simultaneously increases the odds ratio for sex from 0.83 to 0.90 (values closer to 1 indicate reduced demographic disparity) while reducing statistical significance. This approach, however, also leads to a reduction in mental workload detection performance, with a 12.7% decrease in balanced accuracy (BACC), suggesting an inherent trade-off between performance and privacy protection. © 2026 IEEE.},

note = {Journal Abbreviation: IEEE Int. Conf. Hum.-Mach. Syst., ICHMS},

keywords = {Biomedical signal processing, Demographic information, Designing systems, Economic and social effects, Electroencephalography, Electrophysiology, Human operator, Mental workload, Modeling performance, Optimal performance, Performance, Population statistics, Real- time, Well being, Workload measurements},

pubstate = {published},

tppubtype = {inproceedings}

}