{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T21:49:22Z","timestamp":1778622562350,"version":"3.51.4"},"reference-count":42,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2020,12,17]],"date-time":"2020-12-17T00:00:00Z","timestamp":1608163200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62076083"],"award-info":[{"award-number":["62076083"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key R&amp;D Program of China","award":["2017YFE0118200,2017YFE0116800"],"award-info":[{"award-number":["2017YFE0118200,2017YFE0116800"]}]},{"name":"Fundamental Research Funds for the Provincial 308 Universities of Zhejiang","award":["GK209907299001-008"],"award-info":[{"award-number":["GK209907299001-008"]}]},{"name":"Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province","award":["2020E10010"],"award-info":[{"award-number":["2020E10010"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Electroencephalogram (EEG) is an effective indicator for the detection of driver fatigue. Due to the significant differences in EEG signals across subjects, and difficulty in collecting sufficient EEG samples for analysis during driving, detecting fatigue across subjects through using EEG signals remains a challenge. EasyTL is a kind of transfer-learning model, which has demonstrated better performance in the field of image recognition, but not yet been applied in cross-subject EEG-based applications. In this paper, we propose an improved EasyTL-based classifier, the InstanceEasyTL, to perform EEG-based analysis for cross-subject fatigue mental-state detection. Experimental results show that InstanceEasyTL not only requires less EEG data, but also obtains better performance in accuracy and robustness than EasyTL, as well as existing machine-learning models such as Support Vector Machine (SVM), Transfer Component Analysis (TCA), Geodesic Flow Kernel (GFK), and Domain-adversarial Neural Networks (DANN), etc.<\/jats:p>","DOI":"10.3390\/s20247251","type":"journal-article","created":{"date-parts":[[2020,12,17]],"date-time":"2020-12-17T10:42:47Z","timestamp":1608201767000},"page":"7251","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["InstanceEasyTL: An Improved Transfer-Learning Method for EEG-Based Cross-Subject Fatigue Detection"],"prefix":"10.3390","volume":"20","author":[{"given":"Hong","family":"Zeng","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China"},{"name":"Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaming","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6718-0980","authenticated-orcid":false,"given":"Wael","family":"Zakaria","sequence":"additional","affiliation":[{"name":"Department of Mathematics-Computer Science, Faculty of Science, Ain Shams University, Abbassia, Cairo 11435, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4962-176X","authenticated-orcid":false,"given":"Fabio","family":"Babiloni","sequence":"additional","affiliation":[{"name":"Industrial NeuroScience Lab, University of Rome \u201cLa Sapienza\u201d, 00161 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8560-5671","authenticated-orcid":false,"given":"Borghini","family":"Gianluca","sequence":"additional","affiliation":[{"name":"Industrial NeuroScience Lab, University of Rome \u201cLa Sapienza\u201d, 00161 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3830-5534","authenticated-orcid":false,"given":"Xiufeng","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0113-6968","authenticated-orcid":false,"given":"Wanzeng","family":"Kong","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China"},{"name":"Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Connor, J.L. 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