{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:14:27Z","timestamp":1750220067530,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":21,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,1,14]],"date-time":"2022-01-14T00:00:00Z","timestamp":1642118400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62103301"],"award-info":[{"award-number":["62103301"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Tianjin","award":["18JCYBJC88200"],"award-info":[{"award-number":["18JCYBJC88200"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,1,14]]},"DOI":"10.1145\/3517077.3517099","type":"proceedings-article","created":{"date-parts":[[2022,5,22]],"date-time":"2022-05-22T22:15:21Z","timestamp":1653257721000},"page":"137-142","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Fatigue Driving Vigilance Detection Using Convolutional Neural Networks and Scalp EEG Signals"],"prefix":"10.1145","author":[{"given":"Yue","family":"Fang","sequence":"first","affiliation":[{"name":"Tianjin Key Laboratory of Information Sensing&Intelligent Control,School of Automation and Electrical Engineering, Tianjin University of Technology and Education, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunxiao","family":"Han","sequence":"additional","affiliation":[{"name":"Tianjin Key Laboratory of Information Sensing&Intelligent Control,School of Automation and Electrical Engineering, Tianjin University of Technology and Education, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Liu","sequence":"additional","affiliation":[{"name":"Tianjin Key Laboratory of Information Sensing&Intelligent Control,School of Automation and Electrical Engineering, Tianjin University of Technology and Education, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fengjuan","family":"Guo","sequence":"additional","affiliation":[{"name":"Tianjin Key Laboratory of Information Sensing&Intelligent Control,School of Automation and Electrical Engineering, Tianjin University of Technology and Education, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingmei","family":"Qin","sequence":"additional","affiliation":[{"name":"Tianjin Key Laboratory of Information Sensing&Intelligent Control,School of Automation and Electrical Engineering, Tianjin University of Technology and Education, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanqiu","family":"Che","sequence":"additional","affiliation":[{"name":"Tianjin Key Laboratory of Information Sensing&Intelligent Control,School of Automation and Electrical Engineering, Tianjin University of Technology and Education, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,5,22]]},"reference":[{"key":"e_1_3_2_1_1_1","first-page":"3","article-title":"Fatigue, Sleep Restriction and Driving Performance","volume":"37","author":"Pierre Philip","year":"2005","unstructured":"Philip Pierre , Sagaspe Pastricia , and Moore Nicholas . 2005 . Fatigue, Sleep Restriction and Driving Performance . Accident Analysis & Prevention 37 , 3 (May 2005), 473-478. https:\/\/doi.org\/10.1016\/j.aap.2004.07.007 10.1016\/j.aap.2004.07.007 Philip Pierre, Sagaspe Pastricia, and Moore Nicholas. 2005. Fatigue, Sleep Restriction and Driving Performance. Accident Analysis & Prevention 37, 3 (May 2005), 473-478. https:\/\/doi.org\/10.1016\/j.aap.2004.07.007","journal-title":"Accident Analysis & Prevention"},{"key":"e_1_3_2_1_2_1","first-page":"6","article-title":"The hazards and prevention of driving while sleepy","volume":"7","author":"Brower K","year":"2003","unstructured":"Brower K J. 2003 . The hazards and prevention of driving while sleepy . Sleep Medicine Reviews 7 , 6 (December 2003). 507-521. https:\/\/doi.org\/10.1016\/S1087-0792(03)90004-9 10.1016\/S1087-0792(03)90004-9 Brower K J.2003. The hazards and prevention of driving while sleepy. Sleep Medicine Reviews 7, 6 (December 2003). 507-521. https:\/\/doi.org\/10.1016\/S1087-0792(03)90004-9","journal-title":"Sleep Medicine Reviews"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.biopsycho.2009.12.011"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1080\/07420520802107031"},{"key":"e_1_3_2_1_5_1","first-page":"6","article-title":"EEG Analysis for Cognitive Failure Detection in Driving Using Type-2 Fuzzy Classifiers","volume":"1","author":"Atulya","year":"2017","unstructured":"Anuradha, Saha, Amit, Konar, Atulya K, and Nagar. 2017 . EEG Analysis for Cognitive Failure Detection in Driving Using Type-2 Fuzzy Classifiers . IEEE Transactions on Emerging Topics in Computational Intelligence 1 , 6 (December 2017), 437-453. https:\/\/doi.org\/ 10.1109\/TETCI.2017.2750761\u00a0 10.1109\/TETCI.2017.2750761 Anuradha, Saha, Amit, Konar, Atulya K, and Nagar. 2017. EEG Analysis for Cognitive Failure Detection in Driving Using Type-2 Fuzzy Classifiers. IEEE Transactions on Emerging Topics in Computational Intelligence 1, 6 (December 2017), 437-453. https:\/\/doi.org\/ 10.1109\/TETCI.2017.2750761\u00a0","journal-title":"IEEE Transactions on Emerging Topics in Computational Intelligence"},{"key":"e_1_3_2_1_6_1","first-page":"3","article-title":"Ultra\u2010long\u2010term subcutaneous home monitoring of epilepsy\u2014490days of EEG from nine patients","volume":"60","author":"Duun\u2010Henriksen Weisdorf S , J","year":"2019","unstructured":"Weisdorf S , J Duun\u2010Henriksen , and Kjeldsen M J. 2019 . Ultra\u2010long\u2010term subcutaneous home monitoring of epilepsy\u2014490days of EEG from nine patients . Epilepsia 60 , 3 (September 2019). https:\/\/doi.org\/10.1111\/epi.16360 10.1111\/epi.16360 Weisdorf S , J Duun\u2010Henriksen, and Kjeldsen M J. 2019. Ultra\u2010long\u2010term subcutaneous home monitoring of epilepsy\u2014490days of EEG from nine patients. Epilepsia 60, 3 (September 2019). https:\/\/doi.org\/10.1111\/epi.16360","journal-title":"Epilepsia"},{"key":"e_1_3_2_1_7_1","volume-title":"Unsupervised Feature Learning for EEG-based Emotion Recognition. 2017 International Conference on Cyberworlds (CW). IEEE Computer Society 1, 3 (September 2017","author":"Olga Sourina","year":"2017","unstructured":"Lan, Zirui, Sourina Olga , Wang Lipo , Scherer Reinhold , and Muller-Putz. 2017 . Unsupervised Feature Learning for EEG-based Emotion Recognition. 2017 International Conference on Cyberworlds (CW). IEEE Computer Society 1, 3 (September 2017 ), 182\u2013185. https:\/\/doi.org\/10.1109\/CW.2017.19 10.1109\/CW.2017.19 Lan, Zirui, Sourina Olga, Wang Lipo, Scherer Reinhold, and Muller-Putz. 2017. Unsupervised Feature Learning for EEG-based Emotion Recognition. 2017 International Conference on Cyberworlds (CW). IEEE Computer Society 1, 3 (September 2017), 182\u2013185. https:\/\/doi.org\/10.1109\/CW.2017.19"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2528162"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/APWC-on-CSE.2016.017"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jocs.2017.11.011"},{"key":"e_1_3_2_1_11_1","volume-title":"Gradient-based learning applied to document recognition","author":"Abdelhamid Ossama","year":"2013","unstructured":"Abdelhamid Ossama , Deng Li , Yu Dong , Jiang Hui . 2013. Gradient-based learning applied to document recognition . IEEE 86,11( November 2013 ) 2278-2323. https:\/\/doi.org\/10.1109\/5.726791 10.1109\/5.726791 Abdelhamid Ossama , Deng Li , Yu Dong, Jiang Hui. 2013. Gradient-based learning applied to document recognition. IEEE 86,11(November 2013) 2278-2323. https:\/\/doi.org\/10.1109\/5.726791"},{"key":"e_1_3_2_1_12_1","first-page":"28","article-title":"A Fast Face Detection Method via Convolutional Neural Network","volume":"395","author":"Jin Zheng","year":"2020","unstructured":"Guo, Guanjun, Wang, Hanzi, and Zheng Jin . 2020 . A Fast Face Detection Method via Convolutional Neural Network . Neurocomputing 395 , 28 (June 2020), 128-137. https:\/\/doi.org\/10.1016\/j.neucom.2018.02.110 10.1016\/j.neucom.2018.02.110 Guo, Guanjun, Wang, Hanzi, and Zheng Jin. 2020. A Fast Face Detection Method via Convolutional Neural Network. Neurocomputing 395, 28 (June 2020), 128-137. https:\/\/doi.org\/10.1016\/j.neucom.2018.02.110","journal-title":"Neurocomputing"},{"key":"e_1_3_2_1_13_1","unstructured":"Murugan P. 2018. Implementation of deep convolutional neural network in multi-class categorical image classification. Computer Vision and Pattern Recognition 15 pages.  Murugan P. 2018. Implementation of deep convolutional neural network in multi-class categorical image classification. Computer Vision and Pattern Recognition 15 pages."},{"key":"e_1_3_2_1_14_1","volume-title":"INet: Convolutional Networks for Biomedical Image Segmentation","author":"Weng W","year":"2021","unstructured":"Weng W , Zhu X. 2021. INet: Convolutional Networks for Biomedical Image Segmentation . IEEE Access 9, ( January 2021 ), 16591\u201316603. https:\/\/doi.org\/10.1109\/access.2021.3053408 10.1109\/access.2021.3053408 Weng W , Zhu X. 2021. INet: Convolutional Networks for Biomedical Image Segmentation. IEEE Access 9, (January 2021), 16591\u201316603. https:\/\/doi.org\/10.1109\/access.2021.3053408"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2017.7950485"},{"key":"e_1_3_2_1_16_1","volume-title":"2016 Difference representation learning using stacked restricted Boltzmann machines for change detection in SAR images. Soft Computing 20, 12(September","author":"Liu Jia","year":"2016","unstructured":"Jia Liu , Maoguo Gong , Jiaojiao Zhao , Hao Li . 2016 Difference representation learning using stacked restricted Boltzmann machines for change detection in SAR images. Soft Computing 20, 12(September 2016 ), 4645-4657. https:\/\/doi.org\/10.1109\/ISBI.2017.7950485 10.1109\/ISBI.2017.7950485 Jia Liu, Maoguo Gong, Jiaojiao Zhao, Hao Li. 2016 Difference representation learning using stacked restricted Boltzmann machines for change detection in SAR images. Soft Computing 20, 12(September 2016), 4645-4657. https:\/\/doi.org\/10.1109\/ISBI.2017.7950485"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2014.131"},{"key":"e_1_3_2_1_18_1","volume-title":"Deep learning with convolutional neural networks for EEG decoding and visualization. Human Brain Mapping 38, 5(July","author":"Schirrmeiste RT","year":"2017","unstructured":"RT Schirrmeiste , JT Springenberg , LDJ Fiedere , M Glasstetter , K Eggensperger . 2017. Deep learning with convolutional neural networks for EEG decoding and visualization. Human Brain Mapping 38, 5(July 2017 ),5391-420. https:\/\/doi.org\/10.1002\/hbm.23730 10.1002\/hbm.23730 RT Schirrmeiste, JT Springenberg, LDJ Fiedere, M Glasstetter, K Eggensperger. 2017. Deep learning with convolutional neural networks for EEG decoding and visualization. Human Brain Mapping 38, 5(July 2017),5391-420. https:\/\/doi.org\/10.1002\/hbm.23730"},{"key":"e_1_3_2_1_19_1","volume-title":"ImageNet Classification with Deep Convolutional Neural Networks. Advances in neural information processing systems 60, 6(June","author":"Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton G. 2012. ImageNet Classification with Deep Convolutional Neural Networks. Advances in neural information processing systems 60, 6(June 2017 ), 84-90. https:\/\/doi.org\/10.1145\/3065386. 10.1145\/3065386 Krizhevsky A, Sutskever I, Hinton G. 2012. ImageNet Classification with Deep Convolutional Neural Networks. Advances in neural information processing systems 60, 6(June 2017), 84-90. https:\/\/doi.org\/10.1145\/3065386."},{"key":"e_1_3_2_1_20_1","volume-title":"Ribeiro","author":"Branco Paula","year":"2016","unstructured":"Paula Branco , Luis Torgo , and Rita P . Ribeiro . 2016 . A survey of predictive modeling on imbalanced domains. ACM Comput. Surv 49, 2, Article 31 (August 2016), 50 pages. http:\/\/dx.doi.org\/10.1145\/2907070 10.1145\/2907070 Paula Branco, Luis Torgo, and Rita P. Ribeiro. 2016. A survey of predictive modeling on imbalanced domains. ACM Comput. Surv 49, 2, Article 31 (August 2016), 50 pages. http:\/\/dx.doi.org\/10.1145\/2907070"},{"key":"e_1_3_2_1_21_1","first-page":"2","article-title":"Ensembling crowdsourced seizure prediction algorithms using long\u2010term human intracranial EEG","volume":"61","author":"Chip Reuben","year":"2020","unstructured":"Reuben Chip , Karoly Philippa , Freestone Dean R., and Temko Andriy; 2020 . Ensembling crowdsourced seizure prediction algorithms using long\u2010term human intracranial EEG . Epilepsia 61 , 2 (February 2020),7-12. https:\/\/doi.org\/10.1111\/epi.16418 10.1111\/epi.16418 Reuben Chip, Karoly Philippa, Freestone Dean R., and Temko Andriy; 2020. Ensembling crowdsourced seizure prediction algorithms using long\u2010term human intracranial EEG. Epilepsia 61, 2 (February 2020),7-12. https:\/\/doi.org\/10.1111\/epi.16418","journal-title":"Epilepsia"}],"event":{"name":"ICMIP 2022: 2022 7th International Conference on Multimedia and Image Processing","acronym":"ICMIP 2022","location":"Tianjin China"},"container-title":["2022 7th International Conference on Multimedia and Image Processing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3517077.3517099","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3517077.3517099","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:09:01Z","timestamp":1750183741000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3517077.3517099"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,14]]},"references-count":21,"alternative-id":["10.1145\/3517077.3517099","10.1145\/3517077"],"URL":"https:\/\/doi.org\/10.1145\/3517077.3517099","relation":{},"subject":[],"published":{"date-parts":[[2022,1,14]]},"assertion":[{"value":"2022-05-22","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}