The impact of driver sleepiness on fixation‐related brain potentials

Ahlström, Christer; Solis‐Marcos, Ignacio; Nilsson, Emma; Åkerstedt, Torbjörn · 2019 · Crossref

DOI: 10.1111/jsr.12962

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Summary

This study investigates the efficacy of the fixation-related lambda response, an electroencephalogram (EEG) event-related potential, as an objective measure of driver sleepiness. While driver sleepiness is traditionally quantified through deteriorated performance, increased blink durations, and subjective ratings, these methods often fail to capture the rapid wake-state instability characteristic of sleep-deprived drivers. The authors hypothesized that the lambda response, a positive wave occurring 80–100 ms after fixation onset that reflects visual information processing, would decrease with sleep deprivation and time on task, mirroring the reduced cortical responsiveness observed in other ERP components. The experiment involved thirty young male drivers participating in a driving simulator study across six sessions: three during the day (full sleep) and three at night (sleep deprived). Participants drove rural and suburban roads under simulated daylight and darkness conditions. Physiological data, including 30-channel EEG and electrooculography, were recorded to calculate eye-fixation-related potentials (EFRPs). The lambda response was extracted from the POz electrode, time-locked to fixation onsets derived from saccadic eye movements. Subjective sleepiness was measured using the Karolinska Sleepiness Scale (KSS), and driving performance was assessed via line crossings. Statistical analyses employed mixed-model ANOVAs and linear mixed-effects models to evaluate the effects of sleep condition, time on task, and environmental factors on the lambda response and its relationship to sleepiness indicators. Results demonstrated that lambda response amplitudes were significantly lower during night driving and decreased with longer time on task. A clear dose-response relationship was observed between the lambda response and subjective sleepiness, with the highest amplitudes associated with low KSS scores (≤5) and the lowest with high scores (9). Linear mixed-effects modeling revealed that lower lambda responses predicted higher subjective sleepiness and increased line crossings; specifically, a 1 μV increase in lambda response corresponded to a 0.17 decrease in KSS level and 0.04 fewer line crossings per five minutes. The models explained 33% of the variability in subjective sleepiness and 26% in line crossings. The findings suggest that the lambda response is a sensitive indicator of the cognitive impairment induced by sleep deprivation, reflecting a general decrement in cortical responsiveness to visual stimuli. Although the effect size was small, the lambda response’s continuous and internally generated nature makes it a promising candidate for objective sleepiness monitoring in driving contexts. The authors conclude that while further refinement is needed to address individual differences and improve model fit, the lambda response offers a viable method for investigating driving impairment without the interference of artificial stimuli.

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Topics

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• drowsiness detection algorithms
• drowsiness
• sleep deprivation
• microsleep
• vigilance
• circadian factors

Information type

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