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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

4 Scopus Citations
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  • Yong Peng
  • Shuangling Peng
  • Honghao Zhang
  • Yuankai Wu

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Original languageEnglish
Number of pages11
Journal / PublicationIEEE Transactions on Intelligent Transportation Systems
Online published18 Nov 2021
Publication statusOnline published - 18 Nov 2021


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.

Research Area(s)

  • Electroencephalography, Fatigue, fatigue and distraction, Feature extraction, Forehead, forehead EEG, Rail transportation, Task analysis, time-series ensemble learning method, Train driver, Vehicles

Citation Format(s)