Mind the road: attention related neuromarkers during automated and manual simulated driving captured with a new mobile EEG sensor system

Scanlon, Joanna Elizabeth Mary; Küppers, Daniel; Büürma, Anneke; Winneke, Axel Heinrich · 2025 · Crossref

DOI: 10.3389/fnrgo.2025.1542379

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Summary

This study investigates the impact of partially automated driving (PAD) on driver vigilance and fatigue, comparing it to manual driving using a novel mobile EEG sensor system. Motivated by the safety risks associated with driver fatigue and the potential for cognitive underload in automated vehicles, the research aims to identify neurophysiological biomarkers that can detect vigilance decrements earlier than behavioral measures. The study specifically evaluates the efficacy of a new, unobtrusive 10-channel mobile EEG sensor-grid system (trEEGrid) against a standard 24-channel EEG cap in a simulated driving environment. The experimental design involved 28 participants who completed two one-hour simulated driving sessions: one requiring manual control and one involving PAD where the vehicle handled steering and speed. Participants wore both EEG systems simultaneously, along with an eye tracker and heart rate monitor. Data collected included EEG spectral power (alpha, beta, and theta), percentage of eye closure (PERCLOS), lane deviation, and subjective ratings of workload, fatigue, and stress. The simulation included sporadic wind gusts to maintain engagement in the manual condition, while the PAD condition relied on automated lane-keeping. Results indicated that alpha, beta, and theta power, as well as PERCLOS, were significantly higher in the PAD condition compared to manual driving and increased over time in both scenarios. These spectral EEG effects were consistent across both the mobile trEEGrid and the standard EEG cap, validating the new sensor system’s reliability. Behavioral measures, specifically lane deviation in the manual condition, also increased over time, indicating performance degradation. Crucially, the EEG measures revealed significant signs of fatigue and vigilance decrement earlier than the behavioral driving metrics. The findings suggest that PAD induces higher levels of passive fatigue and vigilance loss than manual driving due to reduced task engagement. The study concludes that EEG-based monitoring, particularly using the new mobile trEEGrid system, offers a sensitive and early detection method for driver drowsiness, outperforming traditional behavioral indicators. This technology holds significant potential for integration into driver monitoring systems to enhance safety in both automated and manual driving contexts, as well as other safety-critical work environments requiring sustained vigilance.

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Topics

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• drowsiness detection algorithms
• vigilance
• drowsiness
• neuro workload indices
• hands on hands off engagement
• workload measurement

Information type

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• Empirical Findings: physiological data
• Methodological Resource: tool software, validation psychometrics