Classification of driver fatigue in an electroencephalography-based countermeasure system with source separation module

Rifai Chai; Ganesh R. Naik; Yvonne Tran; Sai Ho Ling; Ashley Craig; Hung T. Nguyen

doi:10.1109/EMBC.2015.7318412

Classification of driver fatigue in an electroencephalography-based countermeasure system with source separation module

Rifai Chai, Ganesh R. Naik, Yvonne Tran, Sai Ho Ling, Ashley Craig, Hung T. Nguyen

Western Sydney University

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

9 Citations (Scopus)

Abstract

![CDATA[An electroencephalography (EEG)-based counter measure device could be used for fatigue detection during driving. This paper explores the classification of fatigue and alert states using power spectral density (PSD) as a feature extractor and fuzzy swarm based-artificial neural network (ANN) as a classifier. An independent component analysis of entropy rate bound minimization (ICA-ERBM) is investigated as a novel source separation technique for fatigue classification using EEG analysis. A comparison of the classification accuracy of source separator versus no source separator is presented. Classification performance based on 43 participants without the inclusion of the source separator resulted in an overall sensitivity of 71.67%, a specificity of 75.63% and an accuracy of 73.65%. However, these results were improved after the inclusion of a source separator module, resulting in an overall sensitivity of 78.16%, a specificity of 79.60% and an accuracy of 78.88% (p < 0.05).]]

Original language	English
Title of host publication	Biomedical Engineering: A Bridge to Improve the Quality of Health Care and the Quality of Life: Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2015), Milano, Italy, 25-29 August 2015
Publisher	IEEE
Pages	514-517
Number of pages	4
ISBN (Print)	9781424492718
DOIs	https://doi.org/10.1109/EMBC.2015.7318412
Publication status	Published - 2015
Event	IEEE Engineering in Medicine and Biology Society. Annual Conference - Duration: 25 Aug 2015 → …

Publication series

Name
ISSN (Print)	1557-170X

Conference

Conference	IEEE Engineering in Medicine and Biology Society. Annual Conference
Period	25/08/15 → …

Keywords

artificial neural network
electroencephalography
fatigue
independent component analysis

UN SDGs

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

Access to Document

10.1109/EMBC.2015.7318412

Cite this

Chai, R., Naik, G. R., Tran, Y., Ling, S. H., Craig, A., & Nguyen, H. T. (2015). Classification of driver fatigue in an electroencephalography-based countermeasure system with source separation module. In Biomedical Engineering: A Bridge to Improve the Quality of Health Care and the Quality of Life: Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2015), Milano, Italy, 25-29 August 2015 (pp. 514-517). IEEE. https://doi.org/10.1109/EMBC.2015.7318412

Chai, Rifai ; Naik, Ganesh R. ; Tran, Yvonne et al. / Classification of driver fatigue in an electroencephalography-based countermeasure system with source separation module. Biomedical Engineering: A Bridge to Improve the Quality of Health Care and the Quality of Life: Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2015), Milano, Italy, 25-29 August 2015. IEEE, 2015. pp. 514-517

@inproceedings{db1f22abbb5f4fa0bc6ae6445a4dc850,

title = "Classification of driver fatigue in an electroencephalography-based countermeasure system with source separation module",

abstract = "![CDATA[An electroencephalography (EEG)-based counter measure device could be used for fatigue detection during driving. This paper explores the classification of fatigue and alert states using power spectral density (PSD) as a feature extractor and fuzzy swarm based-artificial neural network (ANN) as a classifier. An independent component analysis of entropy rate bound minimization (ICA-ERBM) is investigated as a novel source separation technique for fatigue classification using EEG analysis. A comparison of the classification accuracy of source separator versus no source separator is presented. Classification performance based on 43 participants without the inclusion of the source separator resulted in an overall sensitivity of 71.67%, a specificity of 75.63% and an accuracy of 73.65%. However, these results were improved after the inclusion of a source separator module, resulting in an overall sensitivity of 78.16%, a specificity of 79.60% and an accuracy of 78.88% (p < 0.05).]]",

keywords = "artificial neural network, electroencephalography, fatigue, independent component analysis",

author = "Rifai Chai and Naik, {Ganesh R.} and Yvonne Tran and Ling, {Sai Ho} and Ashley Craig and Nguyen, {Hung T.}",

year = "2015",

doi = "10.1109/EMBC.2015.7318412",

language = "English",

isbn = "9781424492718",

publisher = "IEEE",

pages = "514--517",

booktitle = "Biomedical Engineering: A Bridge to Improve the Quality of Health Care and the Quality of Life: Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2015), Milano, Italy, 25-29 August 2015",

note = "IEEE Engineering in Medicine and Biology Society. Annual Conference ; Conference date: 25-08-2015",

}

Chai, R, Naik, GR, Tran, Y, Ling, SH, Craig, A & Nguyen, HT 2015, Classification of driver fatigue in an electroencephalography-based countermeasure system with source separation module. in Biomedical Engineering: A Bridge to Improve the Quality of Health Care and the Quality of Life: Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2015), Milano, Italy, 25-29 August 2015. IEEE, pp. 514-517, IEEE Engineering in Medicine and Biology Society. Annual Conference, 25/08/15. https://doi.org/10.1109/EMBC.2015.7318412

Classification of driver fatigue in an electroencephalography-based countermeasure system with source separation module. / Chai, Rifai; Naik, Ganesh R.; Tran, Yvonne et al.
Biomedical Engineering: A Bridge to Improve the Quality of Health Care and the Quality of Life: Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2015), Milano, Italy, 25-29 August 2015. IEEE, 2015. p. 514-517.

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

TY - GEN

T1 - Classification of driver fatigue in an electroencephalography-based countermeasure system with source separation module

AU - Chai, Rifai

AU - Naik, Ganesh R.

AU - Tran, Yvonne

AU - Ling, Sai Ho

AU - Craig, Ashley

AU - Nguyen, Hung T.

PY - 2015

Y1 - 2015

N2 - ![CDATA[An electroencephalography (EEG)-based counter measure device could be used for fatigue detection during driving. This paper explores the classification of fatigue and alert states using power spectral density (PSD) as a feature extractor and fuzzy swarm based-artificial neural network (ANN) as a classifier. An independent component analysis of entropy rate bound minimization (ICA-ERBM) is investigated as a novel source separation technique for fatigue classification using EEG analysis. A comparison of the classification accuracy of source separator versus no source separator is presented. Classification performance based on 43 participants without the inclusion of the source separator resulted in an overall sensitivity of 71.67%, a specificity of 75.63% and an accuracy of 73.65%. However, these results were improved after the inclusion of a source separator module, resulting in an overall sensitivity of 78.16%, a specificity of 79.60% and an accuracy of 78.88% (p < 0.05).]]

AB - ![CDATA[An electroencephalography (EEG)-based counter measure device could be used for fatigue detection during driving. This paper explores the classification of fatigue and alert states using power spectral density (PSD) as a feature extractor and fuzzy swarm based-artificial neural network (ANN) as a classifier. An independent component analysis of entropy rate bound minimization (ICA-ERBM) is investigated as a novel source separation technique for fatigue classification using EEG analysis. A comparison of the classification accuracy of source separator versus no source separator is presented. Classification performance based on 43 participants without the inclusion of the source separator resulted in an overall sensitivity of 71.67%, a specificity of 75.63% and an accuracy of 73.65%. However, these results were improved after the inclusion of a source separator module, resulting in an overall sensitivity of 78.16%, a specificity of 79.60% and an accuracy of 78.88% (p < 0.05).]]

KW - artificial neural network

KW - electroencephalography

KW - fatigue

KW - independent component analysis

UR - http://handle.westernsydney.edu.au:8081/1959.7/uws:42735

U2 - 10.1109/EMBC.2015.7318412

DO - 10.1109/EMBC.2015.7318412

M3 - Conference Paper

SN - 9781424492718

SP - 514

EP - 517

BT - Biomedical Engineering: A Bridge to Improve the Quality of Health Care and the Quality of Life: Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2015), Milano, Italy, 25-29 August 2015

PB - IEEE

T2 - IEEE Engineering in Medicine and Biology Society. Annual Conference

Y2 - 25 August 2015

ER -

Chai R, Naik GR, Tran Y, Ling SH, Craig A, Nguyen HT. Classification of driver fatigue in an electroencephalography-based countermeasure system with source separation module. In Biomedical Engineering: A Bridge to Improve the Quality of Health Care and the Quality of Life: Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2015), Milano, Italy, 25-29 August 2015. IEEE. 2015. p. 514-517 doi: 10.1109/EMBC.2015.7318412

Classification of driver fatigue in an electroencephalography-based countermeasure system with source separation module

Abstract

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Keywords

UN SDGs

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