Driver Fatigue Eeg Fuzzy Entropy Feature Analysis Based on Sliding Window

DOI

10.2991/meici-17.2017.100How to use a DOI?

Keywords

EEG(Electroencephalogram); Fatigue Detection; Sliding Window; Fuzzy Entropy

Abstract

For the non-stationary signal, entropy is a good method of analysis, now has been successfully applied to the study of driver fatigue detection feature, now for the driver fatigue detection method is based on the piecewise independent samples, it is difficult to describe the gradual process of fatigue, based on fuzzy entropy as feature extraction method. In 1 sampling periods for the window, using 1/10 cycle steps, the collected EEG signals were analyzed by continuous, sliding window fuzzy features describe the gradual process of driver fatigue.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Zhendong Mu
PY  - 2017/10
DA  - 2017/10
TI  - Driver Fatigue Eeg Fuzzy Entropy Feature Analysis Based on Sliding Window
BT  - Proceedings of the 7th International Conference on Management, Education, Information and Control (MEICI 2017)
PB  - Atlantis Press
SP  - 523
EP  - 526
SN  - 1951-6851
UR  - https://doi.org/10.2991/meici-17.2017.100
DO  - 10.2991/meici-17.2017.100
ID  - Mu2017/10
ER  -