Physiological and Neurophysiological Measures Used To Quantify Cognitive Workload of UAS Operators

Authors

Pooja Jain, Drexel University
Cooper Molloy, Drexel University
Patricia A. Shewokis, Drexel University
Kurtulus Izzetoglu, Drexel University

Document Type

Article

Publication Date

2025

Abstract

Cognitive workload, the mental effort required to complete a task, is a factor thatcan impair performance in safety-critical missions, such as operating unmannedaerial systems (UAS). Traditional assessments like the NASA Task Load Index(NASA-TLX) rely on subjective ratings but can be intrusive and inaccurate. Thisstudy explores objective and continuous measures with functional near-infraredspectroscopy (fNIRS) and eye-tracking. Thirty-five participants completedsimulated flight scenarios, and data were collected from eye-tracking, fNIRSbiomarkers, performance scores, and questionnaire responses to analyze cognitiveworkload. Machine learning classifiers, including Support Vector Machine andLogistic Regression, trained using biomarkers derived from feature selectionalgorithms, achieved moderate success (LR classifier trained on forward selectedneurophysiological feature set: 77% accuracy, 86% F1 score) in classifyingcognitive workload states. Future research will aim to enhance the predictiveaccuracy of these models by integrating biomarkers derived from eye-trackingand fNIRS.

Comments

Presented at the 23rd International Symposium on Aviation Psychology, May 27-30, 2025, Hosted by Oregon State University

Repository Citation

Jain, P., Molloy, C., Shewokis, P. A., & Izzetoglu, K. (2025). Physiological and Neurophysiological Measures Used To Quantify Cognitive Workload of UAS Operators. Proceedings of the 23rd International Symposium on Aviation Psychology, 174-179.
https://corescholar.libraries.wright.edu/isap_2025/30