Directed Brain Network Analysis for Fatigue Driving Based on EEG Source Signals Article Swipe
Yingmei Qin
,
Ziyu Hu
,
Yi Chen
,
Jing Liu
,
Lijie Jiang
,
Yanqiu Che
,
Chunxiao Han
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/e24081093
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/e24081093
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.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/e24081093
- https://www.mdpi.com/1099-4300/24/8/1093/pdf?version=1660050633
- OA Status
- gold
- Cited By
- 11
- References
- 48
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4293778440
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4293778440Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/e24081093Digital Object Identifier
- Title
-
Directed Brain Network Analysis for Fatigue Driving Based on EEG Source SignalsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-08-09Full publication date if available
- Authors
-
Yingmei Qin, Ziyu Hu, Yi Chen, Jing Liu, Lijie Jiang, Yanqiu Che, Chunxiao HanList of authors in order
- Landing page
-
https://doi.org/10.3390/e24081093Publisher landing page
- PDF URL
-
https://www.mdpi.com/1099-4300/24/8/1093/pdf?version=1660050633Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1099-4300/24/8/1093/pdf?version=1660050633Direct OA link when available
- Concepts
-
Electroencephalography, Computer science, Vigilance (psychology), Rhythm, Clustering coefficient, Cluster analysis, Artificial intelligence, Neuroscience, Psychology, Medicine, Internal medicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
11Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 4, 2023: 3, 2022: 1Per-year citation counts (last 5 years)
- References (count)
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48Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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