Analysis of Neurophysiological Correlates of Mental Fatigue in Both Monotonous and Demanding Driving Conditions
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Summary
This study addresses the critical road safety issue of mental fatigue during driving, specifically investigating whether neurophysiological biomarkers validated for passive fatigue (induced by monotony) remain effective under active fatigue conditions (induced by cognitive overload). While existing monitoring systems often fail to detect fatigue early or accurately in complex real-world scenarios, electroencephalogram (EEG)-based indices like the Mental Drowsiness (MDrow) index show promise. However, the MDrow index had previously been validated only in monotonous driving contexts. The authors aimed to evaluate the sensitivity and stability of the MDrow index in cognitively demanding environments and to assess the utility of complementary physiological signals, including heart activity and electrodermal activity, in these varied conditions. The researchers conducted a simulated driving experiment with 19 licensed drivers. The protocol included a baseline session followed by three driving scenarios with increasing complexity: monotonous driving (low stimulation, passive fatigue), demanding urban driving (high complexity, active fatigue), and dual-task driving (urban driving combined with a visual N-back task to induce cognitive overload). Data collection involved EEG, photoplethysmography (PPG), and electrodermal activity (EDA), alongside subjective assessments using the Karolinska Sleepiness Scale and Driver Activity Load Index. The MDrow index was calculated based on Alpha band Global Field Power over parietal sites, normalized to an eyes-closed resting baseline. Physiological parameters, including heart rate (HR), heart rate variability (HRV), skin conductance level, and skin conductance response, were analyzed during periods of maximum mental fatigue identified by the MDrow index. The results demonstrated that the MDrow index is sensitive to both passive and active mental fatigue, showing statistically significant increases ($p < 0.001$) across all driving conditions. This confirms the index’s stability and validity even when drivers face additional cognitive demands. In contrast, heart rate and HRV increased significantly during more complex tasks, indicating a heightened response to mental workload rather than mental fatigue alone. Electrodermal measures showed no sensitivity to mental fatigue-related changes. These findings suggest that while cardiac signals reflect workload intensity, they do not specifically track the progression of mental fatigue in the same way EEG-based markers do. The study concludes that the MDrow index serves as a robust, objective, and continuous marker of mental fatigue, applicable to both monotonous and cognitively demanding driving scenarios. This validation supports the potential integration of EEG-based monitoring into advanced driver assistance systems for real-world applications. Furthermore, the distinct behaviors of cardiac and electrodermal signals highlight the importance of using EEG as a ground truth for calibrating multimodal monitoring solutions, as other biosignals may conflate workload with fatigue. This work provides a scientific foundation for developing reliable, early-detecting fatigue monitoring technologies that account for the variability of real-world driving conditions.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-08-09 |
| archive | success | openalex | — | — | 5 | 2026-08-09 |
| extract | success | cached | — | — | 3 | 2026-08-10 |
| clean | success | clean | — | — | 1 | 2026-08-09 |
| chunk | success | chunk | — | — | 1 | 2026-08-09 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-08-09 |
| promote | success | — | — | — | 1 | 2026-08-09 |
| summarize | success | llm | qwen3.6-27b-nvidia | summ-v5 | 2 | 2026-08-10 |
| tag | success | vector_similarity | — | — | 11 | 2026-08-11 |
| verify | partial | — | — | — | 2 | 2026-08-10 |
Summary generated by qwen3.6-27b-nvidia on 2026-08-10; verification: verified_with_issues.
Topics
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- workload measurement
- mental demand
- drowsiness detection algorithms
- neuro workload indices
- drowsiness
- time on task
Information type
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- Empirical Findings: physiological data
- Methodological Resource: validation psychometrics
- Theoretical Contribution: theory or model