{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T00:30:15Z","timestamp":1778286615077,"version":"3.51.4"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"20","license":[{"start":{"date-parts":[[2021,5,4]],"date-time":"2021-05-04T00:00:00Z","timestamp":1620086400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,5,4]],"date-time":"2021-05-04T00:00:00Z","timestamp":1620086400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100012542","name":"Sichuan Province Science and Technology Support Program","doi-asserted-by":"publisher","award":["2020YFSY0070"],"award-info":[{"award-number":["2020YFSY0070"]}],"id":[{"id":"10.13039\/100012542","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012542","name":"Sichuan Province Science and Technology Support Program","doi-asserted-by":"publisher","award":["2021JDRC0118"],"award-info":[{"award-number":["2021JDRC0118"]}],"id":[{"id":"10.13039\/100012542","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012542","name":"Sichuan Province Science and Technology Support Program","doi-asserted-by":"publisher","award":["2021JDRC0096"],"award-info":[{"award-number":["2021JDRC0096"]}],"id":[{"id":"10.13039\/100012542","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51975490"],"award-info":[{"award-number":["51975490"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51774241"],"award-info":[{"award-number":["51774241"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2021,10]]},"DOI":"10.1007\/s00521-021-06038-y","type":"journal-article","created":{"date-parts":[[2021,5,4]],"date-time":"2021-05-04T05:03:00Z","timestamp":1620104580000},"page":"13965-13980","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":73,"title":["Vehicle driver drowsiness detection method using wearable EEG based on convolution neural network"],"prefix":"10.1007","volume":"33","author":[{"given":"Miankuan","family":"Zhu","sequence":"first","affiliation":[]},{"given":"Jiangfan","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Haobo","family":"Li","sequence":"additional","affiliation":[]},{"given":"Fujian","family":"Liang","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Han","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2641-2049","authenticated-orcid":false,"given":"Zutao","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,5,4]]},"reference":[{"key":"6038_CR1","unstructured":"Statistical Communiqu\u00e9 of the People's Republic of China on the 2019 National Economic and Social Development, http:\/\/www.stats.gov.cn\/tjsj\/zxfb\/202002\/t20200228_1728913.html, accessed November 2020"},{"key":"6038_CR2","unstructured":"The Prevalence and Impact of Drowsy Driving, https:\/\/aaafoundation.org\/prevalence-impact-drowsy-driving\/, accessed November 2020"},{"key":"6038_CR3","unstructured":"PRECISE NUMBERS OF DROWSY-DRIVING CRASHES, INJURIES, AND FATALITIES ARE HARD TO NAIL DOWN. https:\/\/www.nhtsa.gov\/risky-driving\/drowsy-driving, accessed November 2020"},{"key":"6038_CR4","unstructured":"Eyetracker Warns against Momentary Driver Drowsiness. http:\/\/www.fraunhofer.de\/en\/press\/research-news\/2010\/10\/eye-tracker-driver-drowsiness.html, accessed November 2020"},{"issue":"6","key":"6038_CR5","doi-asserted-by":"publisher","first-page":"3017","DOI":"10.1109\/TITS.2015.2462084","volume":"16","author":"S Kaplan","year":"2015","unstructured":"Kaplan S, Guvensan MA, Yavuz AG, Karalurt Y (2015) Driver behavior analysis for safe driving: a survey. IEEE Trans Intell Transp Syst 16(6):3017\u20133032","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"6038_CR6","doi-asserted-by":"publisher","unstructured":"Ullah MR, Aslam M, Ullah MI, Maria MEA (2018) Driver\u2019s drowsiness detection through computer vision: a review. In: Mexican International Conference on Artificial Intelligence. Springer: Cham. Doi: https:\/\/doi.org\/10.1007\/978-3-030-02840-4_22","DOI":"10.1007\/978-3-030-02840-4_22"},{"issue":"5","key":"6038_CR7","doi-asserted-by":"publisher","first-page":"1046","DOI":"10.1109\/TITS.2016.2600300","volume":"18","author":"C Bila","year":"2017","unstructured":"Bila C, Sivrikaya F, Khan MA, Albayrak S (2017) Vehicles of the future: a survey of research on safety issues. IEEE Trans Intell Transp Syst 18(5):1046\u20131065","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"6038_CR8","doi-asserted-by":"publisher","first-page":"3155","DOI":"10.1007\/s00521-020-05209-7","volume":"33","author":"M Dua","year":"2020","unstructured":"Dua M, Singla R, Raj S, Jangra A (2020) Deep CNN models-based ensemble approach to driver drowsiness detection. Neural Comput Appl 33:3155\u20133168","journal-title":"Neural Comput Appl"},{"key":"6038_CR9","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.neucom.2013.01.048","volume":"126","author":"B Cyganek","year":"2014","unstructured":"Cyganek B, Gruszczynski S (2014) Hybrid computer vision system for drivers\u2019 eye recognition and fatigue monitoring. Neurocomputing 126:78\u201394","journal-title":"Neurocomputing"},{"issue":"12","key":"6038_CR10","first-page":"1693","volume":"44","author":"F Gharagozlou","year":"2015","unstructured":"Gharagozlou F, Saraji GN, Mazloumi A et al (2015) Detecting driver mental fatigue based on EEG alpha power changes during simulated driving. Iran J Public Health 44(12):1693\u20131700","journal-title":"Iran J Public Health"},{"issue":"12","key":"6038_CR11","doi-asserted-by":"publisher","first-page":"2726","DOI":"10.1109\/TCSI.2005.857555","volume":"52","author":"CT Lin","year":"2005","unstructured":"Lin CT, Wu RC, Liang SF et al (2005) EEG-based drowsiness estimation for safety driving using independent component analysis. IEEE Trans Circuits Syst I Regul Pap 52(12):2726\u20132738","journal-title":"IEEE Trans Circuits Syst I Regul Pap"},{"issue":"4","key":"6038_CR12","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1016\/j.jsr.2009.04.005","volume":"40","author":"CC Liu","year":"2009","unstructured":"Liu CC, Hosking SG, Lenne MG (2009) Predicting driver drowsiness using vehicle measures: recent insights and future challenges. J Saf Res 40(4):239\u2013245","journal-title":"J Saf Res"},{"issue":"2","key":"6038_CR13","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/j.jsr.2005.11.003","volume":"37","author":"AV Desai","year":"2006","unstructured":"Desai AV, Haque MA (2006) Vigilance monitoring for operator safety: a simulation study on highway driving. J Saf Res 37(2):139\u2013147","journal-title":"J Saf Res"},{"issue":"3","key":"6038_CR14","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1007\/s12239-009-0045-x","volume":"10","author":"A Mortazavi","year":"2009","unstructured":"Mortazavi A, Eskandarian A, Sayed RA (2009) Effect of drowsiness on driving performance variables of commercial vehicle drivers. Int J Automot Technol 10(3):391\u2013404","journal-title":"Int J Automot Technol"},{"key":"6038_CR15","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1016\/j.aap.2012.05.005","volume":"50","author":"PM Forsman","year":"2013","unstructured":"Forsman PM, Vila BJ, Short RA et al (2013) Efficient driver drowsiness detection at moderate levels of drowsiness. Accid Anal Prev 50:341\u2013350","journal-title":"Accid Anal Prev"},{"issue":"3","key":"6038_CR16","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1109\/TITS.2016.2582900","volume":"8","author":"B Mandal","year":"2017","unstructured":"Mandal B, Li L, Wang GS et al (2017) Towards detection of bus driver fatigue based on robust visual analysis of eye state. IEEE Trans Intell Transp Syst 8(3):545\u2013557","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"6038_CR17","first-page":"183","volume":"6","author":"M Saradadevi","year":"2008","unstructured":"Saradadevi M, Bajaj P (2008) Driver fatigue detection using mouth and yawning analysis. Int J Comput Sci Netw Secur 6:183\u2013188","journal-title":"Int J Comput Sci Netw Secur"},{"key":"6038_CR18","doi-asserted-by":"crossref","unstructured":"Cyganek B, Gruszczynski S (2013) Eye recognition in near-infrared images for driver's drowsiness monitoring. In: IEEE Intelligent Vehicles Symposium (IV), pp 397\u2013402","DOI":"10.1109\/IVS.2013.6629501"},{"key":"6038_CR19","doi-asserted-by":"crossref","unstructured":"Tawari A, Trivedi M (2014) Robust and continuous estimation of driver gaze zone by dynamic analysis of multiple face videos. In: IEEE Intelligent Vehicles Symposium (IV), pp 344\u2013349","DOI":"10.1109\/IVS.2014.6856607"},{"issue":"3","key":"6038_CR20","doi-asserted-by":"publisher","first-page":"1462","DOI":"10.1109\/TITS.2013.2262098","volume":"14","author":"RO Mbouna","year":"2013","unstructured":"Mbouna RO, Kong SG, Chun MG (2013) Visual analysis of eye state and head pose for driver alertness monitoring. IEEE Trans Intell Transp Syst 14(3):1462\u20131469","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"6038_CR21","doi-asserted-by":"publisher","unstructured":"Begum S (2013) Intelligent driver monitoring systems based on physiological sensor signals: a review. In: International IEEE Conference on Intelligent Transportation Systems (ITSC), pp 282\u2013289, doi: https:\/\/doi.org\/10.1109\/ITSC.2013.6728246","DOI":"10.1109\/ITSC.2013.6728246"},{"issue":"1","key":"6038_CR22","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1109\/TBME.2010.2077291","volume":"58","author":"RN Khushaba","year":"2011","unstructured":"Khushaba RN, Kodagoda S, Lal S, Dissanayake G (2011) Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm. IEEE Trans Biomed Eng 58(1):121\u2013131","journal-title":"IEEE Trans Biomed Eng"},{"key":"6038_CR23","doi-asserted-by":"publisher","DOI":"10.3389\/fninf.2020.00001","author":"J LaRocco","year":"2020","unstructured":"LaRocco J, Le MD, Paeng DG (2020) A systemic review of available low-cost EEG headsets used for drowsiness detection. Front Neuroinform. https:\/\/doi.org\/10.3389\/fninf.2020.00001","journal-title":"Front Neuroinform"},{"issue":"1","key":"6038_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41597-019-0027-4","volume":"6","author":"Z Cao","year":"2019","unstructured":"Cao Z, Chuang CH, King JK, Lin CT (2019) Multi-channel EEG recordings during a sustained-attention driving task. Sci Data 6(1):1\u20138","journal-title":"Sci Data"},{"issue":"5","key":"6038_CR25","doi-asserted-by":"publisher","first-page":"775","DOI":"10.3390\/electronics9050775","volume":"9","author":"Y Ma","year":"2020","unstructured":"Ma Y, Zhang S, Qi D et al (2020) Driving drowsiness detection with EEG using a modified hierarchical extreme learning machine algorithm with particle swarm optimization: a pilot study. Electronics 9(5):775","journal-title":"Electronics"},{"issue":"4","key":"6038_CR26","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1109\/TBCAS.2010.2046415","volume":"4","author":"CT Lin","year":"2010","unstructured":"Lin CT, Chang CJ, Lin BS et al (2010) A real-time wireless brain\u2013computer interface system for drowsiness detection. IEEE Trans Biomed Circuits Syst 4(4):214\u2013222","journal-title":"IEEE Trans Biomed Circuits Syst"},{"key":"6038_CR27","doi-asserted-by":"publisher","first-page":"103","DOI":"10.3389\/fnins.2017.00103","volume":"11","author":"R Chai","year":"2017","unstructured":"Chai R, Ling SH, San PP et al (2017) Improving EEG-based driver fatigue classification using sparse-deep belief networks. Front Neurosci 11:103","journal-title":"Front Neurosci"},{"issue":"1","key":"6038_CR28","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.ssci.2008.01.007","volume":"47","author":"MVM Yeo","year":"2009","unstructured":"Yeo MVM, Li X, Shen K et al (2009) Can SVM be used for automatic EEG detection of drowsiness during car driving? Saf Sci 47(1):115\u2013124","journal-title":"Saf Sci"},{"key":"6038_CR29","doi-asserted-by":"publisher","DOI":"10.1109\/TCBB.2021.3052811","author":"X Gu","year":"2021","unstructured":"Gu X et al (2021) EEG-based brain-computer interfaces (BCIs): a survey of recent studies on signal sensing technologies and computational intelligence approaches and their applications. IEEE\/ACM Trans Comput Biol Bioinf. https:\/\/doi.org\/10.1109\/TCBB.2021.3052811","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"issue":"9","key":"6038_CR30","doi-asserted-by":"publisher","first-page":"2755","DOI":"10.1109\/TNNLS.2018.2886414","volume":"30","author":"Z Gao","year":"2019","unstructured":"Gao Z et al (2019) EEG-based spatio-temporal convolutional neural network for driver fatigue evaluation. IEEE Trans Neural Netw Learn Syst 30(9):2755\u20132763. https:\/\/doi.org\/10.1109\/TNNLS.2018.2886414","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"6","key":"6038_CR31","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1007\/s11571-018-9496-y","volume":"12","author":"H Zeng","year":"2018","unstructured":"Zeng H, Yang C, Dai G et al (2018) EEG classification of driver mental states by deep learning. Cogn Neurodyn 12(6):597\u2013606","journal-title":"Cogn Neurodyn"},{"issue":"1","key":"6038_CR32","first-page":"37","volume":"4","author":"Y Punsawad","year":"2011","unstructured":"Punsawad Y, Aempedchr S, Wongsawat Y, Panichkun M (2011) Weighted-frequency index for EEG-based mental fatigue alarm system. Int J Appl 4(1):37","journal-title":"Int J Appl"},{"issue":"12","key":"6038_CR33","doi-asserted-by":"publisher","first-page":"4477","DOI":"10.3390\/s18124477","volume":"18","author":"M Ogino","year":"2018","unstructured":"Ogino M, Mitsukura Y (2018) Portable drowsiness detection through use of a prefrontal single-channel electroencephalogram. Sensors 18(12):4477","journal-title":"Sensors"},{"issue":"5","key":"6038_CR34","doi-asserted-by":"publisher","first-page":"330","DOI":"10.1006\/cbmr.2000.1549","volume":"33","author":"HJ Park","year":"2000","unstructured":"Park HJ, Oh JS, Jeong DU, Park KS (2000) Automated sleep stage scoring using hybrid rule-and case-based reasoning. Comput Biomed Res 33(5):330\u2013349","journal-title":"Comput Biomed Res"},{"issue":"3","key":"6038_CR35","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1093\/biomet\/81.3.425","volume":"81","author":"DL Donoho","year":"1994","unstructured":"Donoho DL, Johnstone IM (1994) Ideal spatial adaptation by wavelet shrinkage. Biometrika 81(3):425\u2013455","journal-title":"Biometrika"},{"key":"6038_CR36","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1016\/j.image.2016.05.018","volume":"47","author":"M Hajinoroozi","year":"2016","unstructured":"Hajinoroozi M, Mao Z, Jung TP et al (2016) EEG-based prediction of driver\u2019s cognitive performance by deep convolutional neural network. Signal Process Image Commun 47:549\u2013555","journal-title":"Signal Process Image Commun"},{"key":"6038_CR37","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y et al (2015) Going deeper with convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"6038_CR38","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint"},{"key":"6038_CR39","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems (NIPS), pp 1097\u20131105"},{"key":"6038_CR40","unstructured":"Driver Fatigue Monitor MR688, https:\/\/caredrive.dyq.cn\/, accessed November 2021"},{"issue":"3","key":"6038_CR41","doi-asserted-by":"publisher","first-page":"486","DOI":"10.3390\/s17030486","volume":"17","author":"XL Zhang","year":"2017","unstructured":"Zhang XL, Li JL, Liu YG et al (2017) Design of a fatigue detection system for high-speed trains based on driver vigilance using a wireless wearable EEG. Sensors 17(3):486","journal-title":"Sensors"},{"issue":"12","key":"6038_CR42","doi-asserted-by":"publisher","first-page":"7169","DOI":"10.1109\/JSEN.2015.2473679","volume":"15","author":"G Li","year":"2015","unstructured":"Li G, Lee BL, Chung WY (2015) Smartwatch-based wearable EEG system for driver drowsiness detection. IEEE Sens J 15(12):7169\u20137180","journal-title":"IEEE Sens J"},{"issue":"3","key":"6038_CR43","doi-asserted-by":"publisher","first-page":"715","DOI":"10.1109\/JBHI.2016.2532354","volume":"21","author":"R Chai","year":"2016","unstructured":"Chai R, Naik GR, Nguyen TN et al (2016) Driver fatigue classification with independent component by entropy rate bound minimization analysis in an EEG-based system. IEEE J Biomed Health Inform 21(3):715\u2013724","journal-title":"IEEE J Biomed Health Inform"},{"key":"6038_CR44","doi-asserted-by":"publisher","DOI":"10.1155\/2013\/297587","author":"MK Wali","year":"2013","unstructured":"Wali MK, Murugappan M, Ahmmad B (2013) Wavelet packet transform based driver distraction level classification using EEG. Math Probl Eng. https:\/\/doi.org\/10.1155\/2013\/297587","journal-title":"Math Probl Eng"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06038-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-06038-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06038-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T09:13:32Z","timestamp":1744190012000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-06038-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,4]]},"references-count":44,"journal-issue":{"issue":"20","published-print":{"date-parts":[[2021,10]]}},"alternative-id":["6038"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-06038-y","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,4]]},"assertion":[{"value":"7 November 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 April 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 May 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}