Evaluating Pro- and Re-Active Driving Behavior by Means of the EEG

Wascher, Edmund; Arnau, Stefan; Gutberlet, Ingmar; Karthaus, Melanie; Getzmann, Stephan · 2018 · Crossref

DOI: 10.3389/fnhum.2018.00205

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Summary

This study investigates how cognitive controllability—specifically the distinction between pro-active and re-active driving behaviors—interacts with task load and time on task to influence drivers' mental states, as measured by electroencephalography (EEG). While EEG oscillatory activity is a established indicator of driver fatigue and vigilance, previous research has not sufficiently isolated the effects of situational controllability from other factors like monotony or workload. The authors aimed to determine if the degree to which a driving situation is predictable and controllable modulates EEG parameters similarly to time on task and cognitive demand. Thirty healthy participants were divided into two groups performing simulated driving tasks adjusted for comparable difficulty. The re-active group drove on a straight road while compensating for varying crosswind forces, requiring reactive corrections to unintended vehicle motion. The pro-active group drove along a winding road, allowing for intentional, forward-looking steering adjustments. Both tasks included low, medium, and high load conditions and lasted 54 minutes without interruption to induce potential mental fatigue. EEG data were recorded using a 64-channel system, with analysis focusing on total power (1–30 Hz) and relative power in Theta (4–7.5 Hz) and Alpha (8–12 Hz) bands at frontal and posterior electrode sites. Behavioral metrics included time off track, steering variability, and steering velocity. Behavioral results confirmed that task difficulty was balanced, as time off track did not differ significantly between groups. However, re-active driving exhibited higher steering variability, suggesting greater corrective effort, while pro-active driving showed higher steering velocity due to cornering. EEG analysis revealed that total power increased with time on task and decreased with task load. Crucially, relative Alpha power was significantly higher in the re-active task compared to the pro-active task, particularly at frontal sites, and this difference widened over time. Relative Alpha power also increased with time on task and decreased with task load. Conversely, relative Theta power showed an opposite pattern, decreasing with time on task and lower task load. The study found that the controllability of the driving situation had a distinct effect on oscillatory EEG activity, comparable in magnitude to the effects of time on task and task load. The findings suggest that re-active driving, characterized by lower controllability and unpredictability, promotes attentional withdrawal and disengagement, reflected by increased Alpha power. In contrast, pro-active driving maintains higher attentional engagement. The results imply that EEG measures, particularly relative Alpha power, can distinguish between driving strategies based on cognitive controllability. This has significant implications for developing driver monitoring systems, as it highlights that mental fatigue and vigilance are not solely functions of duration or workload but are also critically dependent on the predictability and controllability of the driving environment.

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Topics

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• neuro workload indices
• vigilance
• drowsiness detection algorithms
• drowsiness
• mental demand
• time on task

Information type

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• Empirical Findings: physiological data, behavioral performance data
• Theoretical Contribution: theory or model