A Neuroergonomic Approach Fostered by Wearable EEG for the Multimodal Assessment of Drivers Trainees

Di Flumeri, Gianluca; Giorgi, Andrea; Germano, Daniele; Ronca, Vincenzo; Vozzi, Alessia; Borghini, Gianluca; Tamborra, Luca; Simonetti, Ilaria; Capotorto, Rossella; Ferrara, Silvia; Sciaraffa, Nicolina; Babiloni, Fabio; Aricò, Pietro · 2023 · Crossref

DOI: 10.3390/s23208389

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Summary

This study addresses the limitations of traditional driving instruction, which relies primarily on instructors’ subjective evaluations of explicit behavior and performance. Such methods fail to capture the cognitive progress of trainees, specifically their ability to manage mental effort and allocate resources safely in complex traffic scenarios. The authors argue that obtaining a driver’s license does not guarantee safe driving competence, particularly among young drivers who exhibit high accident rates. To bridge this gap, the research investigates the validity of using a wearable electroencephalography (EEG) device to measure mental effort as an objective neurometric indicator of learning progress. The experimental design involved 22 young participants with little to no driving experience. Participants completed a driving training protocol using a high-fidelity car simulator equipped with a realistic cockpit and three-screen display. They drove along five different but similarly difficult urban routes. During these sessions, brain activity was recorded using the Mindtooth Touch wearable EEG system. The study employed a multimodal assessment approach, collecting EEG data alongside driving performance metrics, subjective self-assessment questionnaires, and reaction times to specific failure events (e.g., engine malfunction alarms). The goal was to determine if EEG-based measures could detect cognitive improvements that traditional behavioral metrics might miss. The results revealed a significant discrepancy between traditional and neurophysiological assessments. Analysis of subjective reports and standard driving performance metrics showed no detectable improvement in the trainees’ skills across the repetitions. However, the EEG-based neurometric of mental effort demonstrated a clear decrease in mental demand after the third repetition of the driving tasks. This reduction in cognitive load indicated that the driving tasks were becoming more automatic for the participants. These findings were corroborated by reaction time data, which showed significant improvement from the third repetition onward. Thus, while behavioral and subjective measures remained static, neurophysiological data successfully captured the underlying cognitive learning process. The significance of these findings lies in the potential for neuroergonomic tools to enhance driving education. By objectively measuring when a task becomes less mentally demanding, instructors can better assess a trainee’s readiness to handle additional tasks and unexpected events. This approach offers a more comprehensive evaluation of driver competence than traditional methods, potentially improving road safety by ensuring that license holders possess not just mechanical skill, but also the cognitive capacity to manage complex driving environments. The study supports the integration of wearable EEG technology into driving training programs to provide objective, real-time insights into learner progress.

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Topics

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• workload measurement
• neuro workload indices
• drowsiness detection algorithms

Information type

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