{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T16:02:39Z","timestamp":1780070559129,"version":"3.54.0"},"reference-count":45,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T00:00:00Z","timestamp":1722988800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Scientific Research Project of the Hubei Provincial Department of Education","award":["B2017216"],"award-info":[{"award-number":["B2017216"]}]},{"name":"Scientific Research Project of the Hubei Provincial Department of Education","award":["61876137"],"award-info":[{"award-number":["61876137"]}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["B2017216"],"award-info":[{"award-number":["B2017216"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61876137"],"award-info":[{"award-number":["61876137"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The present study utilizes the significant differences in \u03b8, \u03b1, and \u03b2 band power spectra observed in electroencephalograms (EEGs) during distracted versus focused driving. Three subtasks, visual distraction, auditory distraction, and cognitive distraction, were designed to appear randomly during driving simulations. The \u03b8, \u03b1, and \u03b2 band power spectra of the EEG signals of the four driving attention states were extracted, and SVM, EEGNet, and GRU-EEGNet models were employed for the detection of the driving attention states, respectively. Online experiments were conducted. The extraction of the \u03b8, \u03b1, and \u03b2 band power spectrum features of the EEG signals was found to be a more effective method than the extraction of the power spectrum features of the whole EEG signals for the detection of driving attention states. The driving attention state detection accuracy of the proposed GRU-EEGNet model is improved by 6.3% and 12.8% over the EEGNet model and PSD_SVM method, respectively. The EEG decoding method combining EEG features and an improved deep learning algorithm, which effectively improves the driving attention state detection accuracy, was manually and preliminarily selected based on the results of existing studies.<\/jats:p>","DOI":"10.3390\/s24165086","type":"journal-article","created":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T08:42:28Z","timestamp":1723020148000},"page":"5086","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Driving Attention State Detection Based on GRU-EEGNet"],"prefix":"10.3390","volume":"24","author":[{"given":"Xiaoli","family":"Wu","sequence":"first","affiliation":[{"name":"College of Physics and Electronic Engineering, Hanjiang Normal University, Shiyan 442000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changcheng","family":"Shi","sequence":"additional","affiliation":[{"name":"College of Physics and Electronic Engineering, Hanjiang Normal University, Shiyan 442000, China"},{"name":"Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lirong","family":"Yan","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,7]]},"reference":[{"key":"ref_1","first-page":"219","article-title":"Traffic safety and individual differences in drivers\u2019 attention and information processing capacity","volume":"9","author":"Shinar","year":"1993","journal-title":"Alcohol Drugs Driv. 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