Analysis of rhythm features of EEG for driving fatigue.
- Author:
Li WANG
1
;
Lingmei AI
;
Siwang WANG
;
Wanzhi LWO
;
Wanzhi LUO
Author Information
1. School of Computer Science, Shaanxi Normal University, Xi'an 710062, China.
- Publication Type:Journal Article
- MeSH:
Automobile Driving;
Brain Waves;
physiology;
Electroencephalography;
Fatigue;
physiopathology;
Humans;
Monitoring, Physiologic;
Signal Processing, Computer-Assisted
- From:
Journal of Biomedical Engineering
2012;29(4):629-633
- CountryChina
- Language:Chinese
-
Abstract:
With extracting separately delta, theta, alpha and beta rhythms of electroencephalogram (EEG), we studied the characters of EEG for fatigued drivers by analyzing relative power spectrum, power spectral entropy and brain electrical activity mapping. The experimental results showed that with the average relative power spectrum in delta and theta rhythms of EEG increasing, the average relative power spectrum in alpha and beta rhythms decreased, while the average relative power spectrum in delta, theta and alpha rhythms increased in deep fatigue. The average power spectral entropy of EEG decreases with the increasing fatigue level. The average relative power spectrum and the average power spectral entropy of EEG could be expected to serve as the index for detecting fatigue level of drivers.
