A Novel EEG-Based Assessment of Distraction in Simulated Driving under Different Road and Traffic Conditions

Ronca, Vincenzo; Brambati, Francois; Napoletano, Linda; Marx, Cyril; Trösterer, Sandra; Vozzi, Alessia; Aricò, Pietro; Giorgi, Andrea; Capotorto, Rossella; Borghini, Gianluca; Babiloni, Fabio; Di Flumeri, Gianluca · 2024 · Crossref

DOI: 10.3390/brainsci14030193

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Summary

This study addresses the critical road safety issue of driver distraction, which the World Health Organization identifies as a primary cause of accidents. Despite extensive research, a significant gap remains in the field: there is no univocal, gold-standard tool to objectively assess the degree of distraction or detect specific distracting events in real-time. Existing methods often rely on subjective self-reports or behavioral metrics, which are susceptible to bias and fail to capture unconscious cognitive processes. To bridge this gap, the authors developed and validated a novel EEG-based "Distraction Index" that combines neurometrics of mental workload and attention. The study aimed to determine if this index could reliably identify distraction levels across different driving environments and pinpoint which secondary tasks most significantly impact driver attention. The experimental design involved 25 licensed drivers (aged 18–69) participating in a simulated driving study using a high-fidelity car simulator equipped with a 140° screen, motion feedback, and an infotainment system. Participants drove in two distinct scenarios: "City" (urban) and "Highway." The protocol included baseline periods of focused driving followed by segments where participants performed specific secondary tasks designed to elicit different types of distraction: cognitive (Auditory Continuous Performance Task), cognitive and visual (Matrix pattern recognition), and visual and manual (Visual Search Task). Data collection was multimodal, utilizing a wearable EEG system with dry electrodes to record neurophysiological signals, alongside subjective self-assessments via Likert scales and behavioral measures including driving parameters and ocular movements. The EEG data were processed to compute the Distraction Index, which was then statistically analyzed against the subjective and behavioral data to validate its reliability. The results demonstrated that the proposed EEG-based Distraction Index was highly reliable in identifying driver distraction across both City and Highway conditions, with statistical significance reported for all comparisons (p < 0.001). The index successfully differentiated between baseline focused driving and distracted states. Furthermore, the analysis revealed that the index was effective in distinguishing the impact of different secondary tasks on driver distraction (p < 0.01), allowing for the identification of which specific tasks—cognitive, visual, or manual—were most detrimental to attention. The EEG-derived metrics correlated well with subjective reports and behavioral indicators, confirming the index's validity as an objective measure. The significance of this work lies in providing a robust, objective methodology for assessing driver distraction that overcomes the limitations of subjective and purely behavioral measures. By validating an EEG-based index that can detect distraction under varying road and traffic conditions, the study offers a potential tool for developing advanced driver assistance systems and interventions. This approach enables the real-time monitoring of cognitive states, facilitating the creation of technologies that can mitigate the risks associated with distracted driving and improve overall road safety.

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Topics

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• external distraction
• distraction detection algorithms
• visual
• mind wandering
• cognitive
• auditory

Information type

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• Empirical Findings: physiological data, behavioral performance data
• Methodological Resource: tool software