Open AccessVigilance detection method for high‐speed rail using wireless wearable EEG collection technology based on low‐rank matrix decomposition
Author(s) -
Zhou Xiang,
Yao Di,
Zhu Miankuan,
Zhang Xiaoliang,
Qi Lingfei,
Pan Hongye,
Zhu Xin,
Wang Yuan,
Zhang Zutao
Publication year - 2018
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2017.0239
Subject(s) - vigilance (psychology) , wearable computer , electroencephalography , computer science , wireless , warning system , alarm , real time computing , artificial intelligence , simulation , speech recognition , engineering , embedded system , telecommunications , psychology , neuroscience , psychiatry , aerospace engineering
With the development of rail transit, driver vigilance is increasingly important in railway safety. A vigilance detection method based on high‐speed rail (HSR) is presented in this study. The proposed method includes three main parts: (i) a wireless wearable electroencephalography (EEG) collection module; (ii) HSR driver's vigilance detection module; and (iii) an early warning module. Drivers’ vigilance is monitored using eight EEG channels. A low‐rank matrix decomposition (also called robust principal component analysis) algorithm is used to classify EEG signals which are collected through wireless wearable EEG collection technology. The warning module will sound an alarm and the early warning begins to message the train control centre if the driver is judged as fatigue. The method was tested on driving EEG data from ten different drivers and reached 99.4% correct classification in a 9 s time window. The feasibility of the proposed vigilance‐detecting method for HSR safety is demonstrated through simulation and test results.