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Driving Fatigue Recognition with Functional Connectivity Based on Phase Synchronization

Wang, Hongtao and Liu, Xucheng and Li, Junhua and Xu, Tao and Bezerianos, Anastasios and Sun, Yu and Wan, Feng (2021) 'Driving Fatigue Recognition with Functional Connectivity Based on Phase Synchronization.' IEEE Transactions on Cognitive and Developmental Systems, 13 (3). pp. 668-678. ISSN 2379-8920

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Abstract

Accumulating evidences showed that the optimal brain network topology was altered with the progression of fatigue during car driving. However, the extent of discriminative power of functional connectivity that contribute to the driving fatigue detection is still unclear. In this study, we extracted two types of features (network properties and critical connections) to explore their usefulness in driving fatigue detection. EEG data were recorded twice from twenty healthy subjects during a simulated driving experiment. Multi-band functional connectivity matrices were established using phase lag index, which serve as input for the following graph theoretical analysis and critical connections determination between the most vigilant and fatigued states. We found a reorganisation of brain network towards less efficient architecture in fatigue state across all frequency bands. Further interrogations showed that the discriminative connections were mainly connected to frontal areas, i.e., most of the increased connections are from frontal pole to parietal or occipital regions. Moreover, we achieved a satisfactory classification accuracy (96.76%) using the discriminative connection features in β band. Our study demonstrated that graph theoretical properties and critical connections are of discriminative power for manifesting fatigue alterations and the critical connection is an efficient feature for driving fatigue detection.

Item Type: Article
Uncontrolled Keywords: Driving fatigue; electroencephalogram; functional connectivity; graph theory
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 27 Apr 2020 13:33
Last Modified: 23 Sep 2022 19:39
URI: http://repository.essex.ac.uk/id/eprint/27369

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