Graph analysis of functional brain network topology using minimum spanning tree in driver drowsiness

Abstract

A large number of traffic accidents due to driver drowsiness have been under more attention of many countries. The organization of the functional brain network is associated with drowsiness, but little is known about the brain network topology that is modulated by drowsiness. To clarify this problem, in this study, we introduce a novel approach to detect driver drowsiness. Electroencephalogram (EEG) signals have been measured during a simulated driving task, in which participants are recruited to undergo both alert and drowsy states. The filtered EEG signals are then decomposed into multiple frequency bands by wavelet packet transform. Functional connectivity between all pairs of channels for multiple frequency bands is assessed using the phase lag index (PLI). Based on this, PLI-weighted networks are subsequently calculated, from which minimum spanning trees are constructed-a graph method that corrects for comparison bias. Statistical analyses are performed on graph-derived metrics as well as on the PLI connectivity values. The major finding is that significant differences in the delta frequency band for three graph metrics and in the theta frequency band for five graph metrics suggesting network integration and communication between network nodes are increased from alertness to drowsiness. Together, our findings also suggest a more line-like configuration in alert states and a more star-like topology in drowsy states. Collectively, our findings point to a more proficient configuration in drowsy state for lower frequency bands. Graph metrics relate to the intrinsic organization of functional brain networks, and these graph metrics may provide additional insights on driver drowsiness detection for reducing and preventing traffic accidents and further understanding the neural mechanisms of driver drowsiness.

The impact of mental fatigue on brain activity: A comparative study between resting state and task state using EEG

“…These changes in EEG can be used to detect mental fatigue [10,15], which is especially important and meaningful for driving fatigue estimation [7,11]. From the above, we can conclude that EEG has become the most effective technical means for exploring the neuromechanism and detection of mental fatigue [18,19].…”

Drowsiness, Fatigue and Poor Sleep’s Causes and Detection: A Comprehensive Study

“…The results suggested that their methodology can achieve an average classification accuracy of 69.5%, and a maximum accuracy of 83.5%. Chen et al, 2018 [132] proposed a novel brainnetworks-based approach to detect drowsiness. EEG signals from 15 male subjects were acquired in a simulated driving task.…”

Phase Fluctuation Analysis in Functional Brain Networks of Scaling EEG for Driver Fatigue Detection

“…Unfortunately, EEG recordings are generated from the cortex and collected from the scalp. In EEG measurements there always appear complex and non-linear characteristics [6][7][8][9][10]. Understanding the non-linear and complex dynamics underlying EEG measurements is a significant and challenging problem [11].…”