Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81642
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dc.contributorDepartment of Industrial and Systems Engineering-
dc.creatorDong, Nen_US
dc.creatorLi, YJen_US
dc.creatorGao, ZKen_US
dc.creatorIp, WHen_US
dc.creatorYung, KLen_US
dc.date.accessioned2020-02-10T12:28:22Z-
dc.date.available2020-02-10T12:28:22Z-
dc.identifier.issn2169-3536en_US
dc.identifier.urihttp://hdl.handle.net/10397/81642-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rightsThis work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/en_US
dc.rightsThe following publication N. Dong, Y. Li, Z. Gao, W. H. Ip and K. L. Yung, "A WPCA-Based Method for Detecting Fatigue Driving From EEG-Based Internet of Vehicles System," in IEEE Access, vol. 7, pp. 124702-124711, 2019 is available at https://dx.doi.org/10.1109/ACCESS.2019.2937914en_US
dc.subjectAR modelen_US
dc.subjectDriving fatigue detectionen_US
dc.subjectFeature reductionen_US
dc.subjectInternet of Vehiclesen_US
dc.subjectWPCA algorithmen_US
dc.titleA WPCA-based method for detecting fatigue driving from EEG-based internet of vehicles systemen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage124702en_US
dc.identifier.epage124711en_US
dc.identifier.volume7en_US
dc.identifier.doi10.1109/ACCESS.2019.2937914en_US
dcterms.abstractFatigue driving is the main cause of traffic accidents. Analysis of electroencephalogram (EEG) signals has attracted wide attention for identifying fatigue driving. With the development of the Internet of Vehicles (IoV), we hope to establish an EEG-based IoV traffic management system to improve traffic safety. In the proposed system, real-time diagnosis is a significant factor, and improvement of the detection speed is our main concern. EEG signals generate a large amount of spatially oriented data over a relatively short duration; hence, their dimension needs to be reduced effectively before being analysed. We proposes a feature reduction method, based on a novel weighted principal component analysis (WPCA) algorithm for EEG signals. First, the EEG features are extracted by an autoregressive (AR) model. Second, we calculate the influence of different features on the classified performance of fatigue state. The accuracy reduction values of different features are normalised as the weights of the features. Finally, these weights are assigned to the WPCA to reduce the EEG features. To verify the effectiveness of the algorithm, we carried out a simulated driving experiment involving eight participants. For comparison, power spectral density and differential entropy models were also introduced to extract EEG features. Support Vector Machine was adopted as a classifier to establish a fatigue driving classification experiment. The experimental results show that the WPCA method can effectively reduce the feature dimension of different EEG feature extraction methods, speed up calculations, and achieve a much higher classification accuracy of fatigue driving.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIEEE access, 2019, v. 7, p. 124702-124711en_US
dcterms.isPartOfIEEE accessen_US
dcterms.issued2019-
dc.identifier.isiWOS:000487829700003-
dc.identifier.scopus2-s2.0-85077962312-
dc.description.validate202002 bcrc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.pubStatusPublisheden_US
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