Directed Brain Network Analysis for Fatigue Driving Based on EEG Source Signals
Fatigue driving is one of the major factors that leads to traffic accidents. Long-term monotonous driving can easily cause a decrease in the driver’s attention and vigilance, manifesting a fatigue effect. This paper proposes a means of revealing the effects of driving fatigue on the brain’s informat...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/24/8/1093 |
| _version_ | 1797445576722219008 |
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| author | Yingmei Qin Ziyu Hu Yi Chen Jing Liu Lijie Jiang Yanqiu Che Chunxiao Han |
| author_facet | Yingmei Qin Ziyu Hu Yi Chen Jing Liu Lijie Jiang Yanqiu Che Chunxiao Han |
| author_sort | Yingmei Qin |
| collection | DOAJ |
| description | Fatigue driving is one of the major factors that leads to traffic accidents. Long-term monotonous driving can easily cause a decrease in the driver’s attention and vigilance, manifesting a fatigue effect. This paper proposes a means of revealing the effects of driving fatigue on the brain’s information processing abilities, from the aspect of a directed brain network based on electroencephalogram (EEG) source signals. Based on current source density (CSD) data derived from EEG signals using source analysis, a directed brain network for fatigue driving was constructed by using a directed transfer function. As driving time increased, the average clustering coefficient as well as the average path length gradually increased; meanwhile, global efficiency gradually decreased for most rhythms, suggesting that deep driving fatigue enhances the brain’s local information integration abilities while weakening its global abilities. Furthermore, causal flow analysis showed electrodes with significant differences between the awake state and the driving fatigue state, which were mainly distributed in several areas of the anterior and posterior regions, especially under the theta rhythm. It was also found that the ability of the anterior regions to receive information from the posterior regions became significantly worse in the driving fatigue state. These findings may provide a theoretical basis for revealing the underlying neural mechanisms of driving fatigue. |
| first_indexed | 2024-03-09T13:28:49Z |
| format | Article |
| id | doaj.art-001e835cd66342a0b1c9e1783d680e2a |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-09T13:28:49Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-001e835cd66342a0b1c9e1783d680e2a2023-11-30T21:20:26ZengMDPI AGEntropy1099-43002022-08-01248109310.3390/e24081093Directed Brain Network Analysis for Fatigue Driving Based on EEG Source SignalsYingmei Qin0Ziyu Hu1Yi Chen2Jing Liu3Lijie Jiang4Yanqiu Che5Chunxiao Han6Tianjin Key Laboratory of Information Sensing & Intelligent Control, School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin 300222, ChinaTianjin Key Laboratory of Information Sensing & Intelligent Control, School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin 300222, ChinaTianjin Key Laboratory of Information Sensing & Intelligent Control, School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin 300222, ChinaTianjin Key Laboratory of Information Sensing & Intelligent Control, School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin 300222, ChinaTianjin Key Laboratory of Information Sensing & Intelligent Control, School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin 300222, ChinaTianjin Key Laboratory of Information Sensing & Intelligent Control, School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin 300222, ChinaTianjin Key Laboratory of Information Sensing & Intelligent Control, School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin 300222, ChinaFatigue driving is one of the major factors that leads to traffic accidents. Long-term monotonous driving can easily cause a decrease in the driver’s attention and vigilance, manifesting a fatigue effect. This paper proposes a means of revealing the effects of driving fatigue on the brain’s information processing abilities, from the aspect of a directed brain network based on electroencephalogram (EEG) source signals. Based on current source density (CSD) data derived from EEG signals using source analysis, a directed brain network for fatigue driving was constructed by using a directed transfer function. As driving time increased, the average clustering coefficient as well as the average path length gradually increased; meanwhile, global efficiency gradually decreased for most rhythms, suggesting that deep driving fatigue enhances the brain’s local information integration abilities while weakening its global abilities. Furthermore, causal flow analysis showed electrodes with significant differences between the awake state and the driving fatigue state, which were mainly distributed in several areas of the anterior and posterior regions, especially under the theta rhythm. It was also found that the ability of the anterior regions to receive information from the posterior regions became significantly worse in the driving fatigue state. These findings may provide a theoretical basis for revealing the underlying neural mechanisms of driving fatigue.https://www.mdpi.com/1099-4300/24/8/1093fatigue drivingEEGcurrent source densitydirected networkinformation integrationcausal flow |
| spellingShingle | Yingmei Qin Ziyu Hu Yi Chen Jing Liu Lijie Jiang Yanqiu Che Chunxiao Han Directed Brain Network Analysis for Fatigue Driving Based on EEG Source Signals Entropy fatigue driving EEG current source density directed network information integration causal flow |
| title | Directed Brain Network Analysis for Fatigue Driving Based on EEG Source Signals |
| title_full | Directed Brain Network Analysis for Fatigue Driving Based on EEG Source Signals |
| title_fullStr | Directed Brain Network Analysis for Fatigue Driving Based on EEG Source Signals |
| title_full_unstemmed | Directed Brain Network Analysis for Fatigue Driving Based on EEG Source Signals |
| title_short | Directed Brain Network Analysis for Fatigue Driving Based on EEG Source Signals |
| title_sort | directed brain network analysis for fatigue driving based on eeg source signals |
| topic | fatigue driving EEG current source density directed network information integration causal flow |
| url | https://www.mdpi.com/1099-4300/24/8/1093 |
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