Work

Real-time monitoring of driver cognitive states: linking driving behavior to neural oscillations

Year: 2025

Type: article

Abstract: Human error, often stemming from lapses in attention or fatigue, underlies most road accidents. Current driver-monitoring systems primarily track behavioral and ocular signals and therefore fail to capture the underlying cognitive dynamics that precede performance breakdowns. This study aims to identify neural oscillatory markers of steering-control demands and their relationships with gaze behavior and vehicle inputs to inform integrated real-time monitoring. Thirty-two drivers completed a simulated highway car-following task under three visibility scenarios (sunny, rainy, foggy) and two workload levels (low vs. high overtaking demand). Continuous EEG, eye movements, and vehicle-control signals were recorded synchronously. A principal-component–derived variation metric on steering, lane position, and pedal signals segmented epochs into low- and high-variation steering events. We quantified frontal theta, posterior alpha, and sensorimotor beta power around each event using time-frequency analyses; eye and vehicle indices were extracted in the same windows. Results showed that low-variation events exhibited sustained pre-event posterior-alpha, frontal-theta and sensorimotor-beta activity, whereas high-variation segments showed suppression of these rhythms followed by a post-event sensorimotor-beta rebound. Larger steering deviations and brake pressures were significantly correlated with alpha and beta desynchronization. High-variation events also elicited increased saccade amplitude and count without altering fixation duration, indicating broadened visual sampling. Moment-to-moment steering demands evoke a dual-mode neural control framework characterized by proactive maintenance under stable conditions and reactive adaptation to perturbations, and they demonstrate that multimodal EEG–ocular–vehicle monitoring can detect error-prone driver states early.

Source: Transportation Research Part F Traffic Psychology and Behaviour

DOI: 10.1016/j.trf.2025.103423

Authors: Emad Alyan, Julian Elias Reiser, Edmund Wascher

Institution: Leibniz Research Centre for Working Environment and Human Factors

Language: English

Topic: Human-Automation Interaction and Safety

Subfield: Social Psychology

Field: Psychology

Domain: Social Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $2,780