AUTHOR=Giorgi Andrea , Ronca Vincenzo , Capotorto Rossella , Vozzi Alessia , Rossi Dario , Aricò Pietro , Borghini Gianluca , Van Gasteren Marteyn , Melus Javier , Petrelli Marco , Sportiello Simone , Polidori Carlo , Picardi Manuel , Babiloni Fabio , Di Flumeri Gianluca 

TITLE=Beyond the time-on-task: an EEG-driven approach for effective physiological assessment of mental fatigue in simulated and real driving

JOURNAL=Frontiers in Bioengineering and Biotechnology

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2025.1682103

DOI=10.3389/fbioe.2025.1682103

ISSN=2296-4185

ABSTRACT=IntroductionFatigue is a major factor contributing to road accidents, and extensive research has focused on its physiological and behavioral characterization. Due to safety and economic constraints, studies on driving fatigue are commonly conducted in simulated environments, where fatigue is typically induced through prolonged tasks and assessed using a Time-on-Task (ToT) approach. However, ToT-based labeling may not accurately reflect individual variations in fatigue onset.MethodsThis study compared fatigue onset in matched simulated and real driving conditions by evaluating two labeling approaches: the traditional ToT-driven method and a novel physiology-driven method based on electroencephalographic (EEG) parameters. Experimental periods of Low and High Fatigue were defined using both approaches, and physiological and behavioral responses were analyzed through ocular and cardiac activity.ResultsWhen using the ToT-driven approach, no significant differences emerged between low and high fatigue periods across the two environments. In contrast, the EEG-driven labeling revealed clear physiological responses to fatigue onset, as evidenced by changes in ocular and heart activity.DiscussionThe findings demonstrate that the method used to define fatigue substantially influences the detection of fatigue onset. The results highlight the importance of physiology-based labeling for capturing individual fatigue dynamics and provide novel insights into how fatigue manifests differently in simulated and real driving contexts.