Fatigue driving detection method based on EEG analysis in low-voltage and hypoxia plateau environment
Driving fatigue is often an important cause of traffic accidents. Monitoring psychological parameters of driver to detect fatigue state is an effective approach to prevent traffic accident. In order to study driving fatigue state in low-voltage and hypoxia plateau environment, the research involved...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2020-12-01
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| Series: | International Journal of Transportation Science and Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2046043020300253 |
| _version_ | 1797702464695173120 |
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| author | Difei Jing Dong Liu Shuwei Zhang Zhongyin Guo |
| author_facet | Difei Jing Dong Liu Shuwei Zhang Zhongyin Guo |
| author_sort | Difei Jing |
| collection | DOAJ |
| description | Driving fatigue is often an important cause of traffic accidents. Monitoring psychological parameters of driver to detect fatigue state is an effective approach to prevent traffic accident. In order to study driving fatigue state in low-voltage and hypoxia plateau environment, the research involved the aspects of subjective monitoring and objective monitoring by driver's real-time electroencephalogram (EEG) signal which was obtained by field driving fatigue test. Nonlinear and linear methods were used to analyze EEG signal in awake, critical, and fatigue three typical states. The EEGLAB in the MATLAB toolbox was used in nonlinear method to analyze the power spectral density map of θ, α, β wave in three typical states. The new EEG eigenvalues were collected, and the EEG power characteristic values were calculated to evaluate the trend of EEG signal in linear method. Combined nonlinear and linear methods with subjective data analysis, the energy characteristic of (α + θ)/β and (α + β)/θ were recommended as the indicator to evaluate driving fatigue characteristics in low-voltage and hypoxia plateau environment. This study provided foundation of theory and examination for the design of driving fatigue warning device in low-voltage and hypoxia plateau environment, which is of great significance for the development of driver fatigue detection system. |
| first_indexed | 2024-03-12T04:50:24Z |
| format | Article |
| id | doaj.art-ee62fd05c8a14447930a2fb8cf64540c |
| institution | Directory Open Access Journal |
| issn | 2046-0430 |
| language | English |
| last_indexed | 2024-03-12T04:50:24Z |
| publishDate | 2020-12-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | International Journal of Transportation Science and Technology |
| spelling | doaj.art-ee62fd05c8a14447930a2fb8cf64540c2023-09-03T09:25:22ZengKeAi Communications Co., Ltd.International Journal of Transportation Science and Technology2046-04302020-12-0194366376Fatigue driving detection method based on EEG analysis in low-voltage and hypoxia plateau environmentDifei Jing0Dong Liu1Shuwei Zhang2Zhongyin Guo3Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji Univeristy, Shanghai 201804, ChinaCCCC Xi’an Road Construction Ma Chinery Co., LTD, Xi’an, ChinaKey Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji Univeristy, Shanghai 201804, ChinaKey Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji Univeristy, Shanghai 201804, China; Shandong Road Region Safety and Emergency Support Laboratory, Jinan, Shandong, China; Corresponding author.Driving fatigue is often an important cause of traffic accidents. Monitoring psychological parameters of driver to detect fatigue state is an effective approach to prevent traffic accident. In order to study driving fatigue state in low-voltage and hypoxia plateau environment, the research involved the aspects of subjective monitoring and objective monitoring by driver's real-time electroencephalogram (EEG) signal which was obtained by field driving fatigue test. Nonlinear and linear methods were used to analyze EEG signal in awake, critical, and fatigue three typical states. The EEGLAB in the MATLAB toolbox was used in nonlinear method to analyze the power spectral density map of θ, α, β wave in three typical states. The new EEG eigenvalues were collected, and the EEG power characteristic values were calculated to evaluate the trend of EEG signal in linear method. Combined nonlinear and linear methods with subjective data analysis, the energy characteristic of (α + θ)/β and (α + β)/θ were recommended as the indicator to evaluate driving fatigue characteristics in low-voltage and hypoxia plateau environment. This study provided foundation of theory and examination for the design of driving fatigue warning device in low-voltage and hypoxia plateau environment, which is of great significance for the development of driver fatigue detection system.http://www.sciencedirect.com/science/article/pii/S2046043020300253Driving fatigueElectroencephalogram (EEG)Plateau environment |
| spellingShingle | Difei Jing Dong Liu Shuwei Zhang Zhongyin Guo Fatigue driving detection method based on EEG analysis in low-voltage and hypoxia plateau environment International Journal of Transportation Science and Technology Driving fatigue Electroencephalogram (EEG) Plateau environment |
| title | Fatigue driving detection method based on EEG analysis in low-voltage and hypoxia plateau environment |
| title_full | Fatigue driving detection method based on EEG analysis in low-voltage and hypoxia plateau environment |
| title_fullStr | Fatigue driving detection method based on EEG analysis in low-voltage and hypoxia plateau environment |
| title_full_unstemmed | Fatigue driving detection method based on EEG analysis in low-voltage and hypoxia plateau environment |
| title_short | Fatigue driving detection method based on EEG analysis in low-voltage and hypoxia plateau environment |
| title_sort | fatigue driving detection method based on eeg analysis in low voltage and hypoxia plateau environment |
| topic | Driving fatigue Electroencephalogram (EEG) Plateau environment |
| url | http://www.sciencedirect.com/science/article/pii/S2046043020300253 |
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