Increase in regularity and decrease in variability seen in electroencephalography (EEG) signals from alert to fatigue during a driving simulated task

Tran, Y.; Wijesuryia, N.; Thuraisingham, R.A.; Craig, A.; Nguyen, H.T. · 2008 · Crossref

DOI: 10.1109/iembs.2008.4649351

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Summary

This study addresses the critical road safety issue of driver fatigue, which contributes to 20–40% of motor vehicle accidents. While existing countermeasure devices often rely on performance metrics or eye movement monitoring, these methods suffer from limitations such as late-stage detection and susceptibility to environmental factors. The authors investigate whether nonlinear analysis of electroencephalography (EEG) signals can provide a more reliable, early indicator of fatigue. Specifically, the research aims to determine if EEG signals exhibit distinct changes in regularity and variability when transitioning from an alert to a fatigued state, using two nonlinear techniques: sample entropy and second-order difference plots quantified by central tendency measure (CTM). These methods were chosen to simplify interpretation compared to traditional spectral analysis, which requires complex evaluation of multiple frequency bands. The experimental design involved 36 participants (19 males, 17 females) performing a monotonous driving simulation task using the Divided Attention Steering Simulator. The task required maintaining a straight path on a simulated road while reacting to visual stimuli, designed to induce boredom and fatigue. EEG data were recorded using a 32-channel Biosemi Active-Two system at a sampling rate of 2048 Hz, downsampled to 256 Hz. Pre-processing included high-pass filtering to remove drift and Independent Component Analysis to eliminate eye and muscle artifacts. Two sets of one-minute EEG data were analyzed: one from the initial alert state and another from a fatigued state, identified via video monitoring, physiological symptoms, and performance decrements. Sample entropy was calculated with parameters m=2 and r=15% of the standard deviation, while CTM was derived from second-order difference plots to quantify signal variability. The results demonstrated a significant increase in regularity and decrease in variability in EEG signals during fatigue. Sample entropy values decreased significantly (p<0.05) across 20 of the 32 EEG channels, indicating a global reduction in signal complexity across cortical regions. Similarly, CTM values, which measure the concentration of successive rate differences, decreased significantly (p<0.05) in 11 channels, particularly in frontal, central, and parietal regions. This reduction in CTM values signifies a decrease in the variability of the EEG time series. The findings confirm that both nonlinear measures effectively distinguish between alert and fatigued states, with lower sample entropy and CTM values corresponding to the fatigued condition. The study concludes that nonlinear analysis of EEG signals offers a promising approach for detecting driver fatigue. Unlike spectral analysis, these methods yield single, easily interpretable values that reflect changes in brain signal regularity and variability. The significant reduction in irregularity and variability associated with fatigue suggests that sample entropy and CTM can serve as robust biomarkers for real-time monitoring systems. Implementing these nonlinear techniques in countermeasure devices could enhance the early detection of fatigue, thereby improving driver alertness and reducing fatigue-related accidents.

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Topics

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• drowsiness detection algorithms
• drowsiness
• vigilance
• neuro workload indices

Information type

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• Empirical Findings: physiological data
• Methodological Resource: validation psychometrics