{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T16:19:53Z","timestamp":1775665193945,"version":"3.50.1"},"reference-count":65,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2019,11,8]],"date-time":"2019-11-08T00:00:00Z","timestamp":1573171200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Rapid and accurate detection of driver fatigue is of great significance to improve traffic safety. In the present work, we propose the man-machine response mode (MRM) to relieve driver fatigue caused by long-term driving. In this paper, the characteristics of the complex brain network, which can effectively reflect brain activity information, were used to detect the change of driving fatigue over time. Combined with the traditional eye movement characteristics and a subjective questionnaire (SQ), the changes in driving fatigue characteristics were comprehensively analyzed. The results show that driving fatigue can be effectively delayed using the MRM. Additionally, the response equipment is low in cost and practical, so it will be practical to use in actual driving situations in the future.<\/jats:p>","DOI":"10.3390\/s19224883","type":"journal-article","created":{"date-parts":[[2019,11,8]],"date-time":"2019-11-08T11:30:19Z","timestamp":1573212619000},"page":"4883","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Study on the Effect of Man-Machine Response Mode to Relieve Driving Fatigue Based on EEG and EOG"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7209-7997","authenticated-orcid":false,"given":"Fuwang","family":"Wang","sequence":"first","affiliation":[{"name":"School of Mechanic Engineering, Northeast Electric Power University, Jilin 132012, China"}]},{"given":"Qing","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Mechanic Engineering, Northeast Electric Power University, Jilin 132012, China"}]},{"given":"Rongrong","family":"Fu","sequence":"additional","affiliation":[{"name":"College of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.aap.2018.03.004","article-title":"Predicting performance and safety based on driver fatigue","volume":"126","author":"Mollicone","year":"2019","journal-title":"Accid. 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