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

Haolan Zhang*, Qixin Zhao, Sanghyuk Lee, Margaret G. Dowens

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationBrain Informatics - 12th International Conference, BI 2019, Proceedings
EditorsPeipeng Liang, Vinod Goel, Chunlei Shan
Number of pages9
ISBN (Print)9783030370770
Publication statusPublished - 2019
Event12th International Conference on Brain Informatics, BI 2019 - Haikou, China
Duration: 13 Dec 201915 Dec 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11976 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference12th International Conference on Brain Informatics, BI 2019


  • Brain informatics
  • Drowsy driving detection
  • Dynamic time dependency
  • EEG pattern recognition

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