A novel dynamic brain network in arousal for brain states and emotion analysis

Yunyuan Gao; Zhen Cao; Jia Liu; Jianhai Zhang; Yunyuan Gao; Zhen Cao; Jia Liu; Jianhai Zhang

doi:10.3934/mbe.2021368

Mathematical Biosciences and Engineering

2021, Volume 18, Issue 6: 7440-7463. doi: 10.3934/mbe.2021368

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Research article Special Issues

A novel dynamic brain network in arousal for brain states and emotion analysis

1.
College of Automation, Hangzhou Dianzi University, Hangzhou, China
2.
Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou, China
3.
Department of Industrial and Systems Engineering, Auburn University, Auburn, AL, United States
4.
School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China

Received: 08 June 2021 Accepted: 23 August 2021 Published: 31 August 2021

Background
Brain network can be well used in emotion analysis to analyze the brain state of subjects. A novel dynamic brain network in arousal is proposed to analyze brain states and emotion with Electroencephalography (EEG) signals.
New Method
Time factors is integrated to construct a dynamic brain network under high and low arousal conditions. The transfer entropy is adopted in the dynamic brain network. In order to ensure the authenticity of dynamics and connections, surrogate data are used for testing and analysis. Channel norm information features are proposed to optimize the data and evaluate the level of activity of the brain.
Results
The frontal lobe, temporal lobe, and parietal lobe provide the most information about emotion arousal. The corresponding stimulation state is not maintained at all times. The number of active brain networks under high arousal conditions is generally higher than those under low arousal conditions. More consecutive networks show high activity under high arousal conditions among these active brain networks. The results of the significance analysis of the features indicates that there is a significant difference between high and low arousal.
Comparison with Existing Method(s)
Compared with traditional methods, the method proposed in this paper can analyze the changes of subjects' brain state over time in more detail. The proposed features can be used to quantify the brain network for accurate analysis.
Conclusions
The proposed dynamic brain network bridges the research gaps in lacking time resolution and arousal conditions in emotion analysis. We can clearly get the dynamic changes of the overall and local details of the brain under high and low arousal conditions. Furthermore, the active segments and brain regions of the subjects were quantified and evaluated by channel norm information.This method can be used to realize the feature extraction and dynamic analysis of the arousal dimension of emotional EEG, further explore the emotional dimension model, and also play an auxiliary role in emotional analysis.
- EEG,
- dynamic brain network,
- arousal,
- emotion analysis,
- transfer entropy matrix
Citation: Yunyuan Gao, Zhen Cao, Jia Liu, Jianhai Zhang. A novel dynamic brain network in arousal for brain states and emotion analysis[J]. Mathematical Biosciences and Engineering, 2021, 18(6): 7440-7463. doi: 10.3934/mbe.2021368

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Abstract

Background

Brain network can be well used in emotion analysis to analyze the brain state of subjects. A novel dynamic brain network in arousal is proposed to analyze brain states and emotion with Electroencephalography (EEG) signals.

New Method

Time factors is integrated to construct a dynamic brain network under high and low arousal conditions. The transfer entropy is adopted in the dynamic brain network. In order to ensure the authenticity of dynamics and connections, surrogate data are used for testing and analysis. Channel norm information features are proposed to optimize the data and evaluate the level of activity of the brain.

Results

The frontal lobe, temporal lobe, and parietal lobe provide the most information about emotion arousal. The corresponding stimulation state is not maintained at all times. The number of active brain networks under high arousal conditions is generally higher than those under low arousal conditions. More consecutive networks show high activity under high arousal conditions among these active brain networks. The results of the significance analysis of the features indicates that there is a significant difference between high and low arousal.

Comparison with Existing Method(s)

Compared with traditional methods, the method proposed in this paper can analyze the changes of subjects' brain state over time in more detail. The proposed features can be used to quantify the brain network for accurate analysis.

Conclusions

The proposed dynamic brain network bridges the research gaps in lacking time resolution and arousal conditions in emotion analysis. We can clearly get the dynamic changes of the overall and local details of the brain under high and low arousal conditions. Furthermore, the active segments and brain regions of the subjects were quantified and evaluated by channel norm information.This method can be used to realize the feature extraction and dynamic analysis of the arousal dimension of emotional EEG, further explore the emotional dimension model, and also play an auxiliary role in emotional analysis.

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