{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T17:56:16Z","timestamp":1777571776322,"version":"3.51.4"},"reference-count":45,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2017,3,1]],"date-time":"2017-03-01T00:00:00Z","timestamp":1488326400000},"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>The vigilance of the driver is important for railway safety, despite not being included in the safety management system (SMS) for high-speed train safety. In this paper, a novel fatigue detection system for high-speed train safety based on monitoring train driver vigilance using a wireless wearable electroencephalograph (EEG) is presented. This system is designed to detect whether the driver is drowsiness. The proposed system consists of three main parts: (1) a wireless wearable EEG collection; (2) train driver vigilance detection; and (3) early warning device for train driver. In the first part, an 8-channel wireless wearable brain-computer interface (BCI) device acquires the locomotive driver\u2019s brain EEG signal comfortably under high-speed train-driving conditions. The recorded data are transmitted to a personal computer (PC) via Bluetooth. In the second step, a support vector machine (SVM) classification algorithm is implemented to determine the vigilance level using the Fast Fourier transform (FFT) to extract the EEG power spectrum density (PSD). In addition, an early warning device begins to work if fatigue is detected. The simulation and test results demonstrate the feasibility of the proposed fatigue detection system for high-speed train safety.<\/jats:p>","DOI":"10.3390\/s17030486","type":"journal-article","created":{"date-parts":[[2017,3,2]],"date-time":"2017-03-02T10:08:28Z","timestamp":1488449308000},"page":"486","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":136,"title":["Design of a Fatigue Detection System for High-Speed Trains Based on Driver Vigilance Using a Wireless Wearable EEG"],"prefix":"10.3390","volume":"17","author":[{"given":"Xiaoliang","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Information Science and Technical, Southwest Jiaotong University, Chengdu 610031, China"}]},{"given":"Jiali","family":"Li","sequence":"additional","affiliation":[{"name":"School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, China"}]},{"given":"Yugang","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2641-2049","authenticated-orcid":false,"given":"Zutao","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China"}]},{"given":"Zhuojun","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, China"}]},{"given":"Dianyuan","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Information Science and Technical, Southwest Jiaotong University, Chengdu 610031, China"}]},{"given":"Xiang","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Information Science and Technical, Southwest Jiaotong University, Chengdu 610031, China"}]},{"given":"Miankuan","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Information Science and Technical, Southwest Jiaotong University, Chengdu 610031, China"}]},{"given":"Waleed","family":"Salman","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China"}]},{"given":"Guangdi","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China"}]},{"given":"Chunbai","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, IA 50011, USA"}]}],"member":"1968","published-online":{"date-parts":[[2017,3,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.ssci.2015.02.017","article-title":"Applying systems thinking approach to accident analysis in China: Case study of \u201c7.23\u201d Yong-Tai-Wen High-Speed train accident","volume":"76","author":"Fan","year":"2016","journal-title":"Saf. 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