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

## TL;DR

This study shows that using EEG-based methods to assess mental fatigue in driving is more effective than traditional time-based approaches.

## Contribution

A novel physiology-driven approach using EEG parameters to assess mental fatigue in driving.

## Key findings

- ToT-driven labeling failed to show differences in fatigue between simulated and real driving.
- EEG-driven labeling revealed clear physiological changes during fatigue onset.
- Physiology-based methods better capture individual fatigue dynamics.

## Abstract

Fatigue 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.

This 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.

When 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.

The 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.