Characteristic Extraction of Fatigue Driver's EEG Signals Based on Wavelet Entropy
Characteristic Extraction of Fatigue Driver's EEG Signals Based on Wavelet Entropy
- Neue Suche nach: Zhang, Ning Ning
- Neue Suche nach: Zhang, Qiang
This study aims to develop a method to detect drivers fatigue using the EEG signals. Experiments have been designed to test the subjects under simulated driving and actual driving, and the fatigue drivers Electroencephalogram (EEG) signals were collected. Wavelet transform method was applied to de-noise the raw EEG data. The H, R (H=α/β; R= (α+θ)/β) wavelet entropy were calculated. The results show that the fatigue drivers H, R wavelet entropy decreased after rest (P<0.05). It is concluded that there are significant difference in brain function between fatigue states and recovered after rest. It is shown that H, R wavelet entropy is an effective eigenvalue to measure drivers fatigue.
Dokumentinformationen
Characteristic Extraction of Fatigue Driver's EEG Signals Based on Wavelet Entropy
Advanced Materials Research ; 779-780 ; 1019-1022
2013-09-04
4 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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