Time-Dependent Adaptations of Brain Networks in Driving Fatigue
Driving with fatigue is a major contributor to traffic accidents and is closely linked to central nervous system functions. To investigate the evolution of brain dynamics during simulated driving under different EEG rhythms, we conducted an experiment in which participants performed a 1 h driving ta...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Engineering Proceedings |
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| Online Access: | https://www.mdpi.com/2673-4591/50/1/6 |
| _version_ | 1797241224142258176 |
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| author | Olympia Giannakopoulou Ioannis Kakkos Georgios N. Dimitrakopoulos Yu Sun George K. Matsopoulos Dimitrios D. Koutsouris |
| author_facet | Olympia Giannakopoulou Ioannis Kakkos Georgios N. Dimitrakopoulos Yu Sun George K. Matsopoulos Dimitrios D. Koutsouris |
| author_sort | Olympia Giannakopoulou |
| collection | DOAJ |
| description | Driving with fatigue is a major contributor to traffic accidents and is closely linked to central nervous system functions. To investigate the evolution of brain dynamics during simulated driving under different EEG rhythms, we conducted an experiment in which participants performed a 1 h driving task while their EEG signals were recorded. We used the complex network theory to analyze data derived from the driving stimulation and found that as fatigue deepened, small-world metrics, namely the path lengths, clustering coefficients, and measures of efficiency (global, local, nodal), showed alterations against the driving time. Additionally, a major correlation (corr = 0.98) was observed between the cluster coefficient with local efficiency in all frequency bands (theta, alpha, beta). Our findings suggest that driving fatigue can cause significant trends in brain network characteristics, such as path length (m = −103 to −93), (m = 98) for specific rhythms (beta, alpha, theta band, respectively) and their related brain functions, which could serve as objective indicators when evaluating the fatigue level and in the future, preventing driving fatigue and its consequences. |
| first_indexed | 2024-04-24T18:19:55Z |
| format | Article |
| id | doaj.art-268ea0b9b3a54306a39137cfb1e9e920 |
| institution | Directory Open Access Journal |
| issn | 2673-4591 |
| language | English |
| last_indexed | 2024-04-24T18:19:55Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj.art-268ea0b9b3a54306a39137cfb1e9e9202024-03-27T13:36:22ZengMDPI AGEngineering Proceedings2673-45912023-10-01501610.3390/engproc2023050006Time-Dependent Adaptations of Brain Networks in Driving FatigueOlympia Giannakopoulou0Ioannis Kakkos1Georgios N. Dimitrakopoulos2Yu Sun3George K. Matsopoulos4Dimitrios D. Koutsouris5Biomedical Engineering Laboratory, National Technical University of Athens, 15772 Athens, GreeceBiomedical Engineering Laboratory, National Technical University of Athens, 15772 Athens, GreeceDepartment of Informatics, Ionian University, 49100 Corfu, GreeceKey Laboratory for Biomedical Engineering of Ministry of Education of China, Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, ChinaBiomedical Engineering Laboratory, National Technical University of Athens, 15772 Athens, GreeceBiomedical Engineering Laboratory, National Technical University of Athens, 15772 Athens, GreeceDriving with fatigue is a major contributor to traffic accidents and is closely linked to central nervous system functions. To investigate the evolution of brain dynamics during simulated driving under different EEG rhythms, we conducted an experiment in which participants performed a 1 h driving task while their EEG signals were recorded. We used the complex network theory to analyze data derived from the driving stimulation and found that as fatigue deepened, small-world metrics, namely the path lengths, clustering coefficients, and measures of efficiency (global, local, nodal), showed alterations against the driving time. Additionally, a major correlation (corr = 0.98) was observed between the cluster coefficient with local efficiency in all frequency bands (theta, alpha, beta). Our findings suggest that driving fatigue can cause significant trends in brain network characteristics, such as path length (m = −103 to −93), (m = 98) for specific rhythms (beta, alpha, theta band, respectively) and their related brain functions, which could serve as objective indicators when evaluating the fatigue level and in the future, preventing driving fatigue and its consequences.https://www.mdpi.com/2673-4591/50/1/6EEGPLI networksdriving fatiguesmall-world metricsfunctional connectivity |
| spellingShingle | Olympia Giannakopoulou Ioannis Kakkos Georgios N. Dimitrakopoulos Yu Sun George K. Matsopoulos Dimitrios D. Koutsouris Time-Dependent Adaptations of Brain Networks in Driving Fatigue Engineering Proceedings EEG PLI networks driving fatigue small-world metrics functional connectivity |
| title | Time-Dependent Adaptations of Brain Networks in Driving Fatigue |
| title_full | Time-Dependent Adaptations of Brain Networks in Driving Fatigue |
| title_fullStr | Time-Dependent Adaptations of Brain Networks in Driving Fatigue |
| title_full_unstemmed | Time-Dependent Adaptations of Brain Networks in Driving Fatigue |
| title_short | Time-Dependent Adaptations of Brain Networks in Driving Fatigue |
| title_sort | time dependent adaptations of brain networks in driving fatigue |
| topic | EEG PLI networks driving fatigue small-world metrics functional connectivity |
| url | https://www.mdpi.com/2673-4591/50/1/6 |
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