Research on Mental Fatigue Detecting Method Based on Sleep Deprivation Models.
- Author:
Xiaolu WANG
;
Xiang GAO
;
Minpeng XU
;
Hongzhi QI
;
Xuemin WANG
;
Dong MING
;
Peng ZHOU
- Publication Type:Journal Article
- MeSH:
Electroencephalography;
Humans;
Mental Fatigue;
Models, Biological;
Sleep Deprivation
- From:
Journal of Biomedical Engineering
2015;32(3):497-502
- CountryChina
- Language:Chinese
-
Abstract:
Mental fatigue is an important factor of human health and safety. It is important to achieve dynamic mental fatigue detection by using electroencephalogram (EEG) signals for fatigue prevention and job performance improvement. We in our study induced subjects' mental fatigue with 30 h sleep deprivation (SD) in the experiment. We extracted EEG features, including relative power, power ratio, center of gravity frequency (CGF), and basic relative power ratio. Then we built mental fatigue prediction model by using regression analysis. And we conducted lead optimization for prediction model. Result showed that R2 of prediction model could reach to 0.932. After lead optimization, 4 leads were used to build prediction model, in which R' could reach to 0.811. It can meet the daily applicatioi accuracy of mental fatigue prediction.
