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

Haolan Zhang; Qixin Zhao; Sanghyuk Lee; Margaret G. Dowens

doi:10.1007/978-3-030-37078-7_5

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

School of Education and English

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

4 Citations (Scopus)

Abstract

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 language	English
Title of host publication	Brain Informatics - 12th International Conference, BI 2019, Proceedings
Editors	Peipeng Liang, Vinod Goel, Chunlei Shan
Publisher	Springer
Pages	39-47
Number of pages	9
ISBN (Print)	9783030370770
DOIs	https://doi.org/10.1007/978-3-030-37078-7_5
Publication status	Published - 2019
Event	12th International Conference on Brain Informatics, BI 2019 - Haikou, China Duration: 13 Dec 2019 → 15 Dec 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11976 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	12th International Conference on Brain Informatics, BI 2019
Country/Territory	China
City	Haikou
Period	13/12/19 → 15/12/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Free Keywords

Brain informatics
Drowsy driving detection
Dynamic time dependency
EEG pattern recognition

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-37078-7_5

Cite this

Zhang, H., Zhao, Q., Lee, S., & Dowens, M. G. (2019). EEG-Based Driver Drowsiness Detection Using the Dynamic Time Dependency Method. In P. Liang, V. Goel, & C. Shan (Eds.), Brain Informatics - 12th International Conference, BI 2019, Proceedings (pp. 39-47). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11976 LNAI). Springer. https://doi.org/10.1007/978-3-030-37078-7_5

Zhang, Haolan ; Zhao, Qixin ; Lee, Sanghyuk et al. / EEG-Based Driver Drowsiness Detection Using the Dynamic Time Dependency Method. Brain Informatics - 12th International Conference, BI 2019, Proceedings. editor / Peipeng Liang ; Vinod Goel ; Chunlei Shan. Springer, 2019. pp. 39-47 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{9d90d0c66a524683b82d4547ec724d42,

title = "EEG-Based Driver Drowsiness Detection Using the Dynamic Time Dependency Method",

abstract = "The increasing number of traffic accidents caused by drowsy driving has drawn much attention for detecting driver{\textquoteright}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{\textquoteright} 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{\textquoteright} status. The preliminary experimental results indicates the efficiency of the proposed method.",

keywords = "Brain informatics, Drowsy driving detection, Dynamic time dependency, EEG pattern recognition",

author = "Haolan Zhang and Qixin Zhao and Sanghyuk Lee and Dowens, \{Margaret G.\}",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 12th International Conference on Brain Informatics, BI 2019 ; Conference date: 13-12-2019 Through 15-12-2019",

year = "2019",

doi = "10.1007/978-3-030-37078-7\_5",

language = "English",

isbn = "9783030370770",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "39--47",

editor = "Peipeng Liang and Vinod Goel and Chunlei Shan",

booktitle = "Brain Informatics - 12th International Conference, BI 2019, Proceedings",

}

Zhang, H, Zhao, Q, Lee, S & Dowens, MG 2019, EEG-Based Driver Drowsiness Detection Using the Dynamic Time Dependency Method. in P Liang, V Goel & C Shan (eds), Brain Informatics - 12th International Conference, BI 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11976 LNAI, Springer, pp. 39-47, 12th International Conference on Brain Informatics, BI 2019, Haikou, China, 13/12/19. https://doi.org/10.1007/978-3-030-37078-7_5

EEG-Based Driver Drowsiness Detection Using the Dynamic Time Dependency Method. / Zhang, Haolan; Zhao, Qixin; Lee, Sanghyuk et al.
Brain Informatics - 12th International Conference, BI 2019, Proceedings. ed. / Peipeng Liang; Vinod Goel; Chunlei Shan. Springer, 2019. p. 39-47 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11976 LNAI).

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - EEG-Based Driver Drowsiness Detection Using the Dynamic Time Dependency Method

AU - Zhang, Haolan

AU - Zhao, Qixin

AU - Lee, Sanghyuk

AU - Dowens, Margaret G.

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - Brain informatics

KW - Drowsy driving detection

KW - Dynamic time dependency

KW - EEG pattern recognition

UR - https://www.scopus.com/pages/publications/85078546925

U2 - 10.1007/978-3-030-37078-7_5

DO - 10.1007/978-3-030-37078-7_5

M3 - Conference contribution

AN - SCOPUS:85078546925

SN - 9783030370770

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 39

EP - 47

BT - Brain Informatics - 12th International Conference, BI 2019, Proceedings

A2 - Liang, Peipeng

A2 - Goel, Vinod

A2 - Shan, Chunlei

PB - Springer

T2 - 12th International Conference on Brain Informatics, BI 2019

Y2 - 13 December 2019 through 15 December 2019

ER -

Zhang H, Zhao Q, Lee S, Dowens MG. EEG-Based Driver Drowsiness Detection Using the Dynamic Time Dependency Method. In Liang P, Goel V, Shan C, editors, Brain Informatics - 12th International Conference, BI 2019, Proceedings. Springer. 2019. p. 39-47. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-37078-7_5

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

Abstract

Publication series

Conference

UN SDGs

Free Keywords

ASJC Scopus subject areas

Access to Document

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