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dc.contributor.author오철-
dc.date.accessioned2022-07-27T00:56:50Z-
dc.date.available2022-07-27T00:56:50Z-
dc.date.issued2021-04-
dc.identifier.citationACCIDENT ANALYSIS AND PREVENTION, v. 154, Page. 106093-106093en_US
dc.identifier.issn0001-4575-
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S000145752100124X-
dc.identifier.urihttps://repository.hanyang.ac.kr/handle/20.500.11754/171762-
dc.description.abstractApplications of neuroimaging methods have substantially contributed to the scientific understanding of human factors during driving by providing a deeper insight into the neuro-cognitive aspects of driver brain. This has been achieved by conducting simulated (and occasionally, field) driving experiments while collecting driver brain signals of various types. Here, this sector of studies is comprehensively reviewed at both macro and micro scales. At the macro scale, bibliometric aspects of these studies are analysed. At the micro scale, different themes of neuroimaging driving behaviour research are identified and the findings within each theme are synthesised. The surveyed literature has reported on applications of four major brain imaging methods. These include Functional Magnetic Resonance Imaging (fMRI), Electroencephalography (EEG), Functional Near-Infrared Spectroscopy (fNIRS) and Magnetoencephalography (MEG), with the first two being the most common methods in this domain. While collecting driver fMRI signal has been particularly instrumental in studying neural correlates of intoxicated driving (e.g. alcohol or cannabis) or distracted driving, the EEG method has been predominantly utilised in relation to the efforts aiming at development of automatic fatigue/drowsiness detection systems, a topic to which the literature on neuro-ergonomics of driving particularly has shown a spike of interest within the last few years. The survey also reveals that topics such as driver brain activity in semiautomated settings or neural activity of drivers with brain injuries or chronic neurological conditions have by contrast been investigated to a very limited extent. Potential topics in driving behaviour research are identified that could benefit from the adoption of neuroimaging methods in future studies. In terms of practicality, while fMRI and MEG experiments have proven rather invasive and technologically challenging for adoption in driving behaviour research, EEG and fNIRS applications have been more diverse. They have even been tested beyond simulated driving settings, in field driving experiments. Advantages and limitations of each of these four neuroimaging methods in the context of driving behaviour experiments are outlined in the paper.en_US
dc.description.sponsorshipThis research was funded by Australian Research Council grants DP150103299 and DP180103718. The authors wish to thank the Editor-in-Chief and three anonymous referees of this article for their constructive remarks.en_US
dc.language.isoenen_US
dc.publisherPERGAMON-ELSEVIER SCIENCE LTDen_US
dc.subjectDriver brain activityen_US
dc.subjectSimulated drivingen_US
dc.subjectAlcohol and cannabisen_US
dc.subjectSecondary tasken_US
dc.subjectDriver decision-makingen_US
dc.subjectFatigue and drowsinessen_US
dc.subjectNeuroimagingen_US
dc.subjectFunctional Magnetic Resonance Imaging (fMRI)en_US
dc.subjectElectroencephalography (EEG)en_US
dc.subjectFunctional Near-Infrared Spectroscopy (fNIRS)en_US
dc.subjectMagnetoencephalography (MEG)en_US
dc.titleApplications of brain imaging methods in driving behaviour researchen_US
dc.typeArticleen_US
dc.relation.volume154-
dc.identifier.doi10.1016/j.aap.2021.106093-
dc.relation.page106093-106093-
dc.relation.journalACCIDENT ANALYSIS AND PREVENTION-
dc.contributor.googleauthorHaghani, Milad-
dc.contributor.googleauthorBliemer, Michiel C.J.-
dc.contributor.googleauthorFarooq, Bilal-
dc.contributor.googleauthorKim, Inhi-
dc.contributor.googleauthorLi, Zhibin-
dc.contributor.googleauthorOh, Cheol-
dc.contributor.googleauthorShahhoseini, Zahra-
dc.contributor.googleauthorMacDougall, Hamish-
dc.relation.code2021041108-
dc.sector.campusE-
dc.sector.daehakCOLLEGE OF ENGINEERING SCIENCES[E]-
dc.sector.departmentDEPARTMENT OF TRANSPORTATION AND LOGISTICS ENGINEERING-
dc.identifier.pidcheolo-
Appears in Collections:
COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > TRANSPORTATION AND LOGISTICS ENGINEERING(교통·물류공학과) > Articles
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