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Citation |
Chen J, Wang H, Hua C, Wang Q, Liu C. Cogn. Neurodyn. 2018; 12(6): 569-581. |
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Affiliation |
Department of Mechanical Engineering and Automation, Northeastern University, Shenyang, 110819 Liaoning China. |
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Copyright |
(Copyright © 2018, Holtzbrinck Springer Nature Publishing Group) |
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DOI |
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PMID |
30483365 |
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PMCID |
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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. Language: en |
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Keywords |
Driver drowsiness; Electroencephalography (EEG); Functional connectivity; Graph theory; Minimum spanning tree |