Investigating the Influence of Driving on Brain Connectivity Networks and Emotion Processing Mechanism Based on EEG Signals

Humans are frequently driving with different emotions, but how driving affects the human brain information processing mechanism in different emotional states is still unknown. In this study, we investigate the effects of driving on brain functional connectivity networks across different emotions based on electroencephalogram (EEG) signals. We utilize the phase lag index (PLI) to measure the degree of phase synchronization among electrode channels and visualize the functional connectivity networks. We also employ graph theory to analyze the functional connectivity networks. Our findings indicate that the strength of functional connectivity among brain regions under different emotions significantly increased after involving in driving, with more efficient and rapid information transmission in the functional connectivity networks. This enhancement is particularly evident in the frontal and parietal lobes, with 507 enhanced connections related to the frontal lobe and 300 related to the parietal lobe, accounting for 41.94% and 24.81% of the total enhanced connections, respectively. These results offer valuable insights into the understanding of how human drives, holding potential for the development of interventions and technologies to improve emotional regulation and driving safety technologies.

Authors

Institutions

Publication Details

Journal
IEEE Sensors Journal
Published
2024-09-27
DOI
https://doi.org/10.1109/jsen.2024.3466124
Citations
3
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
1.03

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Investigating the Influence of Driving on Brain Connectivity Networks and Emotion Processing Mechanism Based on EEG Signals

Ling Zheng, Guofa Li, Chuzhao Li, Feng Gao et al.
3 citations
IEEE Sensors Journal
EEG and Brain-Computer Interfaces
1.03
article

Investigating the Influence of Driving on Brain Connectivity Networks and Emotion Processing Mechanism Based on EEG Signals

Ling Zheng, Guofa Li, Chuzhao Li, Feng Gao, Long Zhang, Paul Green, Zhenning Li
article en
3 citations

Abstract

Humans are frequently driving with different emotions, but how driving affects the human brain information processing mechanism in different emotional states is still unknown. In this study, we investigate the effects of driving on brain functional connectivity networks across different emotions based on electroencephalogram (EEG) signals. We utilize the phase lag index (PLI) to measure the degree of phase synchronization among electrode channels and visualize the functional connectivity networks. We also employ graph theory to analyze the functional connectivity networks. Our findings indicate that the strength of functional connectivity among brain regions under different emotions significantly increased after involving in driving, with more efficient and rapid information transmission in the functional connectivity networks. This enhancement is particularly evident in the frontal and parietal lobes, with 507 enhanced connections related to the frontal lobe and 300 related to the parietal lobe, accounting for 41.94% and 24.81% of the total enhanced connections, respectively. These results offer valuable insights into the understanding of how human drives, holding potential for the development of interventions and technologies to improve emotional regulation and driving safety technologies.

IEEE Sensors JournalVol. 24(21)
Chongqing University (CN), Shenzhen University (CN), University of Macau (MO), University of Michigan (US), City University of Macau (MO)
National Natural Science Foundation of China, Natural Science Foundation of Chongqing
Openalex Percentile: Top 24%
EEG and Brain-Computer Interfaces
1.03
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.