Application of HHT to driving fatigue in EEG analysis.
- Author:
Jiaofen NAN
1
;
Lingmei AI
;
Jun SHEN
Author Information
1. College of Computer Science, Shaanxi Normal University, Xi'an 710062, China.
- Publication Type:Journal Article
- MeSH:
Automobile Driving;
psychology;
Electroencephalography;
methods;
Humans;
Mental Fatigue;
physiopathology;
prevention & control;
psychology;
Signal Processing, Computer-Assisted
- From:
Journal of Biomedical Engineering
2011;28(4):653-657
- CountryChina
- Language:Chinese
-
Abstract:
Based on the fact that the signals of electroencephalogram (EEG) possess non-linear and non-stationary properties, Hilbert-Huang Transform (HHT) was proposed for the EEG analysis of driving fatigue. Firstly, C4-lead EEG was selected, and the data of normal driving state and fatigue driving state was analyzed by HHT to explore the differences. Then O2-lead EEG was chosen for contrastive analysis of differences between the different leads. It was found through the analysis that the EEG signals had different Hilbert marginal spectrums for different states, and there were also some differences at the same state for the two leads. It can be certain that HHT can well distinguish different states of drivers as a novel approach for driving fatigue detection, and the selected lead may affect detectable results to some extent.
