title: Investigating Driver Fatigue versus Alertness Using the Granger Causality Network.
abstract: Driving fatigue has been identified as one of the main factors affecting drivers' safety. The aim of this study was to analyze drivers' different mental states, such as alertness and drowsiness, and find out a neurometric indicator able to detect drivers' fatigue level in terms of brain networks. Twelve young, healthy subjects were recruited to take part in a driver fatigue experiment under different simulated driving conditions. The Electroencephalogram (EEG) signals of the subjects were recorded during the whole experiment and analyzed by using Granger-Causality-based brain effective networks. It was that the topology of the brain networks and the brain's ability to integrate information changed when subjects shifted from the alert to the drowsy stage. In particular, there was a significant difference in terms of strength of Granger causality (GC) in the frequency domain and the properties of the brain effective network i.e., causal flow, global efficiency and characteristic path length between such conditions. Also, some changes were more significant over the frontal brain lobes for the alpha frequency band. These findings might be used to detect drivers' fatigue levels, and as reference work for future studies.
authors.affiliation: College of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China. kongwanzeng@hdu.edu.cn.|College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310027, China. kongwanzeng@hdu.edu.cn.
authors.forename: Wanzeng
authors.initials: W
authors.lastname: Kong
authors.affiliation: College of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China. tuoluo2004@163.com.
authors.forename: Weicheng
authors.initials: W
authors.lastname: Lin
authors.affiliation: Department of Molecular Medicine, University of Rome \"Sapienza\", Rome 00185, Italy. fabio.babiloni@uniroma1.it.
authors.forename: Fabio
authors.initials: F
authors.lastname: Babiloni
authors.affiliation: College of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China. sqhu@hdu.edu.cn.
authors.forename: Sanqing
authors.initials: S
authors.lastname: Hu
authors.affiliation: Department of Physiology and Pharmacology, University of Rome \"Sapienza\", Rome 00185, Italy. gianluca.borghini@gmail.com.|IRCCS Fondazione Santa Lucia, via Ardeatina, 306, Rome 00142, Italy. gianluca.borghini@gmail.com.
authors.forename: Gianluca
authors.initials: G
authors.lastname: Borghini
mesh_terms: D000328:Adult; D000465:Algorithms; D001334:Automobile Driving; D004569:Electroencephalography; D005221:Fatigue / Q000503:physiopathology*; D006801:Humans; D013647:Task Performance and Analysis; D014851:Wakefulness; D055815:Young Adult
keywords: brain effective network; driving fatigue; eeg; frequency domain; granger causality