A Survey and Tutorial of EEG-Based Brain Monitoring for Driver State Analysis

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dc.contributor.authorZhang, Ceen
dc.contributor.authorEskandarian, Azimen
dc.date.accessioned2022-03-16T11:40:59Zen
dc.date.available2022-03-16T11:40:59Zen
dc.date.issued2021-07-01en
dc.date.updated2022-03-15T22:26:04Zen
dc.description.abstractThe driver's cognitive and physiological states affect his/her ability to control the vehicle. Thus, these driver states are essential to the safety of automobiles. The design of advanced driver assistance systems (ADAS) or autonomous vehicles will depend on their ability to interact effectively with the driver. A deeper understanding of the driver state is, therefore, paramount. Electroencephalography (EEG) is proven to be one of the most effective methods for driver state monitoring and human error detection. This paper discusses EEG-based driver state detection systems and their corresponding analysis algorithms over the last three decades. First, the commonly used EEG system setup for driver state studies is introduced. Then, the EEG signal preprocessing, feature extraction, and classification algorithms for driver state detection are reviewed. Finally, EEG-based driver state monitoring research is reviewed in-depth, and its future development is discussed. It is concluded that the current EEG-based driver state monitoring algorithms are promising for safety applications. However, many improvements are still required in EEG artifact reduction, real-time processing, and between-subject classification accuracy.en
dc.description.notesAccepted by IEEE/CAA Journal of Automatica Sinicaen
dc.description.versionAccepted versionen
dc.format.extentPages 1222-1242en
dc.format.extent21 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1109/JAS.2020.1003450en
dc.identifier.eissn2329-9274en
dc.identifier.issn2329-9266en
dc.identifier.issue7en
dc.identifier.orcidEskandarian, Azim [0000-0002-4117-7692]en
dc.identifier.urihttp://hdl.handle.net/10919/109349en
dc.identifier.volume8en
dc.language.isoenen
dc.publisherIEEEen
dc.relation.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000658364800002&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=930d57c9ac61a043676db62af60056c1en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectAutomation & Control Systemsen
dc.subjectAdvanced driver assistance systems (ADAS)en
dc.subjectdata analysisen
dc.subjectelectroencephalography (EEG)en
dc.subjectintelligent vehiclesen
dc.subjectmachine learning algorithmsen
dc.subjectneural networken
dc.subjectCANONICAL CORRELATION-ANALYSISen
dc.subjectCOMMON SPATIAL-PATTERNSen
dc.subjectFATIGUE DETECTIONen
dc.subjectARTIFACTen
dc.subjectREMOVALen
dc.subjectCLASSIFICATIONen
dc.subjectENTROPYen
dc.subjectEXTRACTIONen
dc.subjectALGORITHMen
dc.subjectSIGNALSen
dc.subjecteess.SPen
dc.subjectcs.HCen
dc.subjectcs.LGen
dc.titleA Survey and Tutorial of EEG-Based Brain Monitoring for Driver State Analysisen
dc.title.serialIEEE-CAA Journal of Automatica Sinicaen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherJournalen
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/Mechanical Engineeringen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen

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