EEG-based driver fatigue detection using hybrid deep generic model

Phyo Phyo San, Sai Ho Ling, Rifai Chai, Yvonne Tran, Ashley Craig, Hung Nguyen

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

12 Citations (Scopus)

Abstract

Classification of electroencephalography (EEG)-based application is one of the important process for biomedical engineering. Driver fatigue is a major case of traffic accidents worldwide and considered as a significant problem in recent decades. In this paper, a hybrid deep generic model (DGM)-based support vector machine is proposed for accurate detection of driver fatigue. Traditionally, a probabilistic DGM with deep architecture is quite good at learning invariant features, but it is not always optimal for classification due to its trainable parameters are in the middle layer. Alternatively, Support Vector Machine (SVM) itself is unable to learn complicated invariance, but produces good decision surface when applied to well-behaved features. Consolidating unsupervised high-level feature extraction techniques, DGM and SVM classification makes the integrated framework stronger and enhance mutually in feature extraction and classification. The experimental results showed that the proposed DBN-based driver fatigue monitoring system achieves better testing accuracy of 73.29 % with 91.10 % sensitivity and 55.48 % specificity. In short, the proposed hybrid DGM-based SVM is an effective method for the detection of driver fatigue in EEG.

Original languageEnglish
Title of host publication2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
EditorsJ Patton, R Barbieri, J Ji, E Jabbari, S Dokos, R Mukkamala, D Guiraud, E Jovanov, Y Dhaher, D Panescu, M Vangils, B Wheeler, AP Dhawan
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages800-803
Number of pages4
Volume2016-October
ISBN (Electronic)9781457702204
DOIs
Publication statusPublished - 13 Oct 2016
Externally publishedYes
Event38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 - Orlando, United States
Duration: 16 Aug 201620 Aug 2016

Conference

Conference38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
CountryUnited States
CityOrlando
Period16/08/1620/08/16

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  • Cite this

    San, P. P., Ling, S. H., Chai, R., Tran, Y., Craig, A., & Nguyen, H. (2016). EEG-based driver fatigue detection using hybrid deep generic model. In J. Patton, R. Barbieri, J. Ji, E. Jabbari, S. Dokos, R. Mukkamala, D. Guiraud, E. Jovanov, Y. Dhaher, D. Panescu, M. Vangils, B. Wheeler, ... AP. Dhawan (Eds.), 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 (Vol. 2016-October, pp. 800-803). [7590822] Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/EMBC.2016.7590822