基于脑电功能连接微状态的情绪状态解码

沈新科; 李奕超; 刘锦; 宋森; 张丹

doi:10.11959/j.issn.2096-6652.202105

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基于脑电功能连接微状态的情绪状态解码

专题：情感脑机接口 | 更新时间：2024-06-05

- 基于脑电功能连接微状态的情绪状态解码
- Emotional state decoding using EEG-based microstates of functional connectivity
- 智能科学与技术学报 2021年3卷第1期页码：49-58
- 作者机构：
  
  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
- 作者简介：
  
  [ "沈新科（1996- ），男，清华大学医学院生物医学工程系博士生，主要研究方向为情感计算、神经影像。" ]
  [ "李奕超（1997- ），男，清华大学医学院生物医学工程系博士生，主要研究方向为认知和计算神经科学。" ]
  [ "刘锦（1995- ），女，清华大学医学院生物医学工程系硕士生，主要研究方向为元学习、跨被试情感计算。" ]
  [ "宋森（1978- ），男，博士，清华大学医学院生物医学工程系副教授，主要研究方向为情感计算、情绪的神经回路、计算神经科学等。" ]
  [ "张丹（1983- ），男，博士，清华大学社会科学学院心理学系副教授，主要研究方向为脑机接口、情感计算、社会认知等。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(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;Tsinghua University Initiative Scientific Research Program(20197010009)
- DOI：10.11959/j.issn.2096-6652.202105
  中图分类号： R318
- 网络出版日期：2021-03，
  
  纸质出版日期：2021-03-15
- 稿件说明：
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沈新科, 李奕超, 刘锦, 等. 基于脑电功能连接微状态的情绪状态解码[J]. 智能科学与技术学报, 2021,3(1):49-58.

Xinke SHEN, Yichao LI, Jin LIU, et al. Emotional state decoding using EEG-based microstates of functional connectivity[J]. Chinese journal of intelligent science and technology, 2021, 3(1): 49-58.
沈新科, 李奕超, 刘锦, 等. 基于脑电功能连接微状态的情绪状态解码[J]. 智能科学与技术学报, 2021,3(1):49-58. DOI： 10.11959/j.issn.2096-6652.202105.

Xinke SHEN, Yichao LI, Jin LIU, et al. Emotional state decoding using EEG-based microstates of functional connectivity[J]. Chinese journal of intelligent science and technology, 2021, 3(1): 49-58. DOI： 10.11959/j.issn.2096-6652.202105.

摘要

基于脑电的情绪状态解码大多将个体情绪看作相对稳定的状态，将脑电频域能量、通道间脑电相关性等稳态指标作为解码中使用的特征。基于近年来网络神经科学在脑区间动态功能连接分析中的新发展，设计并实现了功能连接微状态方法，将不同情绪状态下脑区间的动态功能连接模式聚集为具有代表性的微状态，提取微状态的覆盖比例、转移概率等时间动态过程指标作为特征，用于情绪状态解码。基于经典的脑电情绪公开数据集DEAP，动态功能连接微状态新特征在情绪的效价和唤醒两个维度上实现了均方误差分别为3.87±0.28和3.25±0.30的回归预测效力，优于传统频带能量特征的均方误差4.07±0.30（p=0.005）和3.41±0.31（p=0.064）。实验结果展示了基于脑电功能连接微状态的情绪状态解码可行性，并为进一步深入理解情绪的神经机制提供了启发。

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.

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references

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