Cargando…
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...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9407608/ https://www.ncbi.nlm.nih.gov/pubmed/36010760 http://dx.doi.org/10.3390/e24081093 |
_version_ | 1784774405593759744 |
---|---|
author | Qin, Yingmei Hu, Ziyu Chen, Yi Liu, Jing Jiang, Lijie Che, Yanqiu Han, Chunxiao |
author_facet | Qin, Yingmei Hu, Ziyu Chen, Yi Liu, Jing Jiang, Lijie Che, Yanqiu Han, Chunxiao |
author_sort | Qin, Yingmei |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-9407608 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-94076082022-08-26 Directed Brain Network Analysis for Fatigue Driving Based on EEG Source Signals Qin, Yingmei Hu, Ziyu Chen, Yi Liu, Jing Jiang, Lijie Che, Yanqiu Han, Chunxiao Entropy (Basel) Article 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. MDPI 2022-08-09 /pmc/articles/PMC9407608/ /pubmed/36010760 http://dx.doi.org/10.3390/e24081093 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Qin, Yingmei Hu, Ziyu Chen, Yi Liu, Jing Jiang, Lijie Che, Yanqiu Han, Chunxiao Directed Brain Network Analysis for Fatigue Driving Based on EEG Source Signals |
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 | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9407608/ https://www.ncbi.nlm.nih.gov/pubmed/36010760 http://dx.doi.org/10.3390/e24081093 |
work_keys_str_mv | AT qinyingmei directedbrainnetworkanalysisforfatiguedrivingbasedoneegsourcesignals AT huziyu directedbrainnetworkanalysisforfatiguedrivingbasedoneegsourcesignals AT chenyi directedbrainnetworkanalysisforfatiguedrivingbasedoneegsourcesignals AT liujing directedbrainnetworkanalysisforfatiguedrivingbasedoneegsourcesignals AT jianglijie directedbrainnetworkanalysisforfatiguedrivingbasedoneegsourcesignals AT cheyanqiu directedbrainnetworkanalysisforfatiguedrivingbasedoneegsourcesignals AT hanchunxiao directedbrainnetworkanalysisforfatiguedrivingbasedoneegsourcesignals |