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

Author:

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

Publisher

Springer International Publishing

Reference7 articles.

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)

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3. Wang, W.: Research on Driver Fatigue Detection System. Thesis. South China University of Technology (2010)

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_64

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)

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