Fatigue during Prolonged Simulated Driving: An Electroencephalogram Study

Zuraida, Rida; Wijayanto, Titis; Iridiastadi, Hardianto · 2022 · Crossref

DOI: 10.14716/ijtech.v13i2.4820

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Summary

This study investigates the neural correlates of driver fatigue during prolonged simulated driving, addressing the significant public health issue of road traffic accidents in Indonesia, where fatigue is a recurrent contributing factor. While electroencephalography (EEG) is often considered the gold standard for assessing fatigue, previous research has yielded mixed results regarding consistent EEG patterns, particularly for driving durations exceeding three hours. This research aims to characterize fatigue progression over a five-hour driving period and determine if specific EEG parameters and cortical areas can reliably manifest fatigue under varying sleep conditions. The experimental design involved fourteen male participants with valid driving licenses and at least two years of experience. Each participant completed two separate 5-hour simulated driving sessions: one following a normal sleep period (7–8 hours) and another following partial sleep deprivation (3–4 hours). The simulations were conducted in the morning using a PC-based driving simulator with a 300-minute continuous task duration to induce fatigue. Brain activity was recorded using a 14-channel mobile EEG headset, capturing theta (4–7 Hz), alpha (8–13 Hz), and beta (13–30 Hz) waves. Data were analyzed using Fast Fourier Transform and power spectrum analysis to calculate relative power ratios. Subjective fatigue and sleepiness were measured using the Karolinska Sleepiness Scale and Subjective Fatigue Rating every 10 and 30 minutes, respectively. Statistical analysis employed two-way repeated measures ANOVA to assess the effects of sleep condition and time on both EEG signals and subjective reports. The results demonstrated a clear, linear increase in subjective fatigue and sleepiness as driving duration increased, with significant differences between the sleep-deprived and well-rested groups. However, EEG data did not show consistent, linear changes across all brain regions or frequency bands. While subjective measures indicated escalating fatigue, brain wave activities exhibited mixed patterns. Notably, theta waves in the temporal and occipital regions showed the most distinct differences between sleep conditions, with sleep-deprived participants exhibiting higher theta power in these areas compared to those with sufficient sleep. Beta wave activities were also prominent in the temporal cortical area. The study found that while alpha and beta power generally decreased with reduced sleep, theta waves increased, particularly in the temporal and occipital regions. The study concludes that while subjective measures reliably track fatigue progression, EEG signals are complex and vary significantly by cortical area and parameter. The authors suggest that theta waves, specifically from the temporal and occipital regions, are the most promising indicators for detecting fatigue during prolonged driving. However, they caution against interpreting EEG-based fatigue data without considering the specific driving context and cortical source, as patterns are not universally consistent. The findings imply that future fatigue detection systems should focus on these specific EEG markers and that further research is needed in real-world field settings to validate these laboratory-based observations.

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Topics

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• drowsiness detection algorithms
• time on task
• drowsiness
• vigilance
• sleep deprivation
• truck driver fatigue

Information type

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