Detection of Train Driver Fatigue and Distraction Based on Forehead EEG : A Time-Series Ensemble Learning Method

Chaojie FAN, Yong PENG, Shuangling PENG, Honghao ZHANG, Yuankai WU, Sam KWONG

Research output: Journal PublicationsJournal Article (refereed)peer-review

45 Citations (Scopus)


Train driver fatigue and distraction are the main reasons for railway accidents. One of the new technologies to monitor drivers is by using the EEG signals, which provides vital signs monitoring of fatigue and distraction. However, monitoring systems involving full-head scalp EEG are time-consuming and uncomfortable for the driver. The aim of this study was to evaluate the suitability of recently introduced forehead EEG for train driver fatigue and distraction detection. We first constructed a unique dataset with experienced train drivers driving in a simulated train driving environment. The EEG signals were collected from an EEG recording device placed on the driver's forehead, and numerous features including energy, entropy, rhythmic energy ratio and frontal asymmetry ratio were extracted from the EEG signals. Therefore, a time-series ensemble learning method was proposed to perform fatigue and distraction detection based on the extracted feature. The proposed method outperforms other popular machine learning algorithms including Support Vector Machine(SVM), K-Nearest Neighbor(KNN), Decision Tree(DT), and Long short-term memory(LSTM). The proposed method is stable and convenient to meet the real-time requirement of train driver monitoring.
Original languageEnglish
Pages (from-to)13559-13569
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number8
Early online date18 Nov 2021
Publication statusPublished - Aug 2022
Externally publishedYes


  • Fatigue and distraction
  • Forehead EEG
  • Time-series ensemble learning method
  • Train driver


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