EEG-Based Driver Drowsiness Detection Using the Dynamic Time Dependency Method
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.
KeywordsEEG 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).
- 1.National Cent. Stat. Analysis.: Drowsy driving 2015 - crash stats brief statistical summary. Report No. DOT HS 812 446, Washington, DC: National Highway Traffic Safety Administration (2017)Google Scholar
- 2.Huang, J., Zhang, L., Xu, J.: Research on EEG-based fatigue driving. Ergonomics 4, 36–40 (2015)Google Scholar
- 3.Wang, W.: Research on Driver Fatigue Detection System. Thesis. South China University of Technology (2010)Google Scholar
- 4.Akrout, B., Mahdi, W.: A blinking measurement method for driver drowsiness detection. In: Burduk, R., Jackowski, K., Kurzynski, M., Wozniak, M., Zolnierek, A., (eds.) Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013. Advances in Intelligent Systems and Computing, vol. 226. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-319-00969-8_64CrossRefGoogle Scholar
- 5.Zhang, H.L., Xue, Y., Zhang, B., Li, X., Lu, X.: EEG pattern recognition based on self-adjusting dynamic time dependency method. In: Proceedings of ICDS. Lecture Notes on Computer Science. Springer (2019)Google Scholar
- 6.Nie, D., Fu, Y., Zhou, J., Fang, Y., Xia, H.: Time series analysis based on enhanced NLCS. In: Proceedings of ICIS, pp. 292–295. IEEE Press (2010)Google Scholar
- 7.Zhao, Q.: Exploring Fatigue Driving Recognition and Early Warning Based on EEG Data Analysis. Thesis, Zhejiang University, NIT (2017)Google Scholar