Exploring the Brain Responses to Driving Fatigue Through Simultaneous EEG and fNIRS Measurements

Lin, Chin-Teng; King, Jung-Tai; Chuang, Chun-Hsiang; Ding, Weiping; Chuang, Wei-Yu; Liao, Lun-De; Wang, Yu-Kai · 2019 · Crossref

DOI: 10.1142/s0129065719500187

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Summary

This study investigates the neural correlates of driving fatigue by simultaneously recording electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) data. The research addresses the critical safety issue of fatigue-induced driving errors, aiming to clarify the relationships between hemodynamic responses, electrical brain activity, and driving performance. While previous studies have examined these markers separately, this work integrates multimodal data to provide a comprehensive view of tonic (baseline) and phasic (event-related) brain dynamics during a simulated night driving task. Sixteen healthy adult participants performed a 60-minute lane-keeping task in a virtual reality environment where the vehicle randomly drifted from the lane. Researchers measured reaction times (RTs) and categorized performance into three groups: optimal, suboptimal, and poor, based on normalized RT ratios. EEG and fNIRS signals were recorded from the occipital and parietal regions. Data analysis focused on tonic variations (two seconds before deviation) and phasic variations (15 seconds after deviation), correlating EEG power bands (delta, theta, alpha, beta) and oxygenated hemoglobin (HbO2) concentrations with performance metrics. The results revealed distinct patterns for tonic and phasic responses. Tonic analysis showed that increased HbO2 concentrations and elevated EEG power in theta, alpha, and beta bands were significantly correlated with deteriorating performance. Specifically, the suboptimal performance group exhibited the highest HbO2 levels, suggesting increased cognitive effort to maintain alertness, whereas the poor performance group showed the lowest HbO2 levels, indicating a failure to compensate for fatigue. Phasic analysis demonstrated event-related desynchronization in alpha and beta bands immediately following deviation onset. HbO2 concentrations initially increased after deviation but decreased during the recovery phase; this decrease was more pronounced in the poor performance group. Negative correlations were found between tonic EEG delta/alpha power and HbO2 oscillations, linking reduced hemodynamic activation to mental fatigue. The study concludes that combining hemodynamic and electrodynamic measurements provides a more complete understanding of brain responses to driving fatigue than either modality alone. The findings suggest that HbO2 increases in the suboptimal group reflect compensatory cognitive effort, while decreases in the poor group signal a breakdown in this compensation. These multimodal biomarkers offer potential evidence for detecting state changes during fatigue driving, which could inform the development of more effective driver monitoring systems.

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Topics

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• neuro workload indices
• drowsiness detection algorithms
• drowsiness
• workload measurement

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• Empirical Findings: physiological data