EEG-Based Driver Drowsiness Detection Using the Dynamic Time Dependency Method

  • Haolan ZhangEmail author
  • Qixin Zhao
  • Sanghyuk Lee
  • Margaret G. Dowens
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11976)


The increasing number of traffic accidents caused by drowsy driving has drawn much attention for detecting driver’s status and alarming drowsy driving. Existing research indicates that the changes in the physiological characteristics can reflect fatigue status, particularly brain activities. Nowadays, the research on brain science has made significant progress, such as the analysis of EEG signal to provide technical supports for real world applications. In this paper, we analyze drivers’ EEG data sets based on the self-adjusting Dynamic Time Dependency (DTD) method for detecting drowsy driving. The proposed model, i.e. SEGAPA, incorporates the time window moving method and cluster probability distribution for detecting drivers’ status. The preliminary experimental results indicates the efficiency of the proposed method.


EEG pattern recognition Drowsy driving detection Dynamic time dependency Brain informatics 



This work is partially supported by Zhejiang Natural Science Fund (LY19F030010), Zhejiang Philosophy and Social Sciences Fund (20NDJC216YB), Ningbo Innovation Team (No. 2016C11024), National Natural Science Fund of China (No. 61572022). Ningbo Natural Science Fund (No. 83, chief investigator Haolan Zhang, Research on non-invasive BIC technology based on dynamic networks and machine learning methods, 2019).


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Haolan Zhang
    • 1
    • 2
    Email author
  • Qixin Zhao
    • 1
  • Sanghyuk Lee
    • 3
  • Margaret G. Dowens
    • 4
  1. 1.The Center for SCDMNIT, Zhejiang UniversityNingboChina
  2. 2.Ningbo Research InstituteZhejiang UniversityHangzhouChina
  3. 3.Xi’an Jiaotong-Liverpool UniversitySuzhouChina
  4. 4.The University of NottinghamNingboChina

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