## 基于脑电功能连接微状态的情绪状态解码

1 清华大学医学院生物医学工程系，北京 100084

2 清华大学脑与智能实验室，北京 100084

3 清华大学社会科学学院心理学系，北京 100084

## Emotional state decoding using EEG-based microstates of functional connectivity

SHEN Xinke1,2, LI Yichao1,2, LIU Jin1,2, SONG Sen1,2, ZHANG Dan2,3

1 Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China

2 Tsinghua Laboratory of Brain and Intelligence, Beijing 100084, China

3 Department of Psychology, School of Social Sciences, Tsinghua University, Beijing 100084, China

 基金资助: 国家自然科学基金资助项目.  U1736220.  61977041教育部关键科学与技术创新项目清华大学自主科研计划资助项目.  20197010009

Revised: 2021-02-26   Online: 2021-03-15

 Fund supported: The National Natural Science Foundation of China.  U1736220.  61977041Tsinghua University Initiative Scientific Research Program.  20197010009

Abstract

Emotional state decoding based on electroencephalography (EEG) usually regards individual emotion as a relatively static state and uses spectral power or inter-channel correlations of EEG as features.Based on recent advancement of dynamic functional connectivity analysis in the area of network neuroscience, a method called microstates of functional connectivity was designed and implemented, which clustered the inter-regional functional connectivity patterns of the brain under different emotional states to obtain representative microstates, and the temporal statistics, such as coverage and transition probability were extracted as features for emotional state decoding.Based on a widely used publicly available EEG dataset DEAP, new features in microstates of dynamic functional connectivity analysis achieved regression mean squared errors of 3.87±0.28 and 3.25±0.30 on valence and arousal respectively, which were better than those using traditional spectral power features, 4.07±0.30 (p=0.005) and 3.41±0.31 (p=0.064).The results demonstrate the feasibility of emotional state decoding based on microstates of functional connectivity and provide deeper insight into understanding the neural mechanisms of emotion.

Keywords： dynamic functional connectivity ; microstate ; emotional state decoding ; electroencephalography

SHEN Xinke. Emotional state decoding using EEG-based microstates of functional connectivity. Chinese Journal of Intelligent Science and Technology[J], 2021, 3(1): 49-58 doi:10.11959/j.issn.2096-6652.202105

## 2 功能连接微状态方法

### 图1

$d\left(x,c\right)=1-\frac{\left(x-\stackrel{\to }{\overline{x}}\right)\left(c-\stackrel{\to }{\overline{c}}{\right)}^{\prime }}{\sqrt{\left(x-\stackrel{\to }{\overline{x}}\right)\left(x-\stackrel{\to }{\overline{x}}{\right)}^{\prime }}\sqrt{\left(c-\stackrel{\to }{\overline{c}}\right)\left(c-\stackrel{\to }{\overline{c}}{\right)}^{\prime }}} \left(1\right)$

## 3 回归与相关分析结果

### 图2

 被试编号 效价 唤醒 微状态特征 频带能量特征 微状态特征 频带能量特征 1 6.09 5.75 4.31 4.44 2 7.94 7.89 8.33 8.37 3 1.87 1.87 2.22 2.20 4 4.54 5.24 3.59 3.81 5 4.67 5.18 3.48 3.62 6 1.99 1.98 1.90 2.02 7 3.53 3.62 2.96 3.38 8 3.81 3.92 1.98 2.15 9 1.81 2.02 0.90 0.95 10 3.33 3.78 1.94 1.88 11 3.72 4.25 5.18 5.35 12 4.03 4.99 2.82 2.75 13 4.92 5.18 3.37 3.78 14 4.02 4.22 2.86 2.50 15 3.19 4.60 1.82 1.89 16 3.07 3.05 3.19 3.04 17 1.60 1.64 1.32 1.86 18 1.32 1.48 1.71 1.69 19 3.10 2.78 2.98 2.86 20 2.64 2.18 1.44 1.55 21 3.34 3.40 1.60 1.66 22 5.40 5.66 3.28 3.49 23 2.95 3.04 5.69 5.91 24 4.08 4.34 2.02 2.26 25 6.06 6.41 3.24 3.38 26 6.72 7.26 5.53 5.31 27 4.26 4.64 3.74 5.90 28 6.26 6.46 6.18 6.39 29 4.32 4.73 4.41 5.60 30 1.72 1.89 1.56 1.79 31 3.99 3.49 5.88 4.98 32 3.50 3.39 2.45 2.38 平均值 3.87 4.07 3.25 3.41 标准误 0.28 0.30 0.30 0.31

## 5 结束语

The authors have declared that no competing interests exist.

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