EEG microstate functional network analysis of different emotional false memories

doi:10.3785/j.issn.1008-973X.2025.01.005

Journal of ZheJiang University (Engineering Science)

2025, Vol. 59

Issue (1): 49-61 DOI: 10.3785/j.issn.1008-973X.2025.01.005

EEG microstate functional network analysis of different emotional false memories

Yixuan LI1(

),Ying LI1,2,*(

),Qian XIAO1,Lingyue WANG1,2,Ning YIN1,2,Shuo YANG1,2

1. Hebei Key Laboratory of Bioelectromagnetics and Neural Engineering, School of Health Science and Biomedical Engineering, Hebei University of Technology, Tianjin 300130, China
2. Tianjin Key Laboratory of Bioelectricity and Intelligent Health, School of Health Science and Biomedical Engineering, Hebei University of Technology, Tianjin 300130, China

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Abstract

The research on the influence of emotions on false memory helps explore the memory-processing mechanisms of the brain. The EEG signals of false memories under different emotion states were collected. The microstate analysis was used to obtain the template maps for each emotion group named from microstate 1 to microstate 5, the time segmentation of the four stages of memory recognition (early processing, familiar processing, episodic recall processing and post-extraction processing) for the emotion groups were divided according to the microstate fitting results, and the phase-locked brain functional networks were constructed in microstates with significant difference in time coverage. The results analyzed of EEG signals from both the temporal perspective and the spatial perspective show that the brain processing patterns of the emotion groups begin to appear different from the episode recall processing stage. The positive group remains in the active microstates 3 and 5 of the prefrontal region and has strong brain function, the negative group remains in microstate 1 and has poor brain function, and the neutral group remains in the active microstates 3 and 4 of the central region. The positive group spends more time and mental resources on plot association and reasoning, while the negative group stays depressed for a longer time, and the neutral group devotes more time and mental resources to information integration.

Key words： electroencephalogram (EEG) emotions false memory microstate brain functional network

Received: 16 November 2023 Published: 18 January 2025

CLC:

R 318

Fund: 国家自然科学基金资助项目（51707055, 51877067）.

Corresponding Authors: Ying LI E-mail: 2821563467@qq.com;yli@hebut.edu.cn

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Cite this article:

Yixuan LI,Ying LI,Qian XIAO,Lingyue WANG,Ning YIN,Shuo YANG. EEG microstate functional network analysis of different emotional false memories. Journal of ZheJiang University (Engineering Science), 2025, 59(1): 49-61.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2025.01.005 OR https://www.zjujournals.com/eng/Y2025/V59/I1/49

不同情绪错误记忆的脑电微状态功能网络分析

研究情绪对错误记忆的影响，有助于探究大脑的记忆加工机制. 采集不同情绪状态下错误记忆脑电信号，由微状态分析得到各情绪组模板图（微状态1~5），根据微状态拟合结果划分各情绪组记忆再认4个阶段（早期加工、熟悉性加工、情节性回想加工和后提取加工）的时间段，在时间覆盖率有显著差异的微状态内构建相位锁值脑功能网络. 从时间、空间2个角度分析脑电信号，结果表明各情绪组的大脑加工模式从情节回想加工阶段出现不同. 积极组在前额区活跃的微状态3、5中持续停留且脑功能性强；消极组在微状态1中持续停留且脑功能性差；中性组在中央区活跃的微状态3、4中持续停留. 积极组的时间和脑力资源多用于情节联想和推理，消极组的大脑处于低迷状态的时间长，中性组的时间和脑力资源多用于信息整合.

关键词： 脑电图（EEG）, 情绪, 错误记忆, 微状态, 脑功能网络

Fig.1 Flow chart of Deese-Roediger-McDermott paradigm experiment

Fig.2 Relationship plot of clustering effect of false memory signals and number of clusters

Fig.3 Topographic map of microstate template

Fig.4 Microstate fitting results of false memory EEG signals in each emotion group

Fig.5 Duration percentage statistics results for microstates

Fig.6 Schematic diagram of brain region division

Fig.7 Correlation matrices of three microstates for different emotion groups

Fig.8 Global efficiency of different emotion groups at different thresholds

Fig.9 Binary matrices of three microstates for different emotion groups

Fig.10 Brain network diagrams of three microstates for different emotion groups

Tab.1 Degree distribution of each emotion group in different microstates

Tab.2 Top 10 brain network key nodes for different microstates and different emotion groups

Tab.3 One-way analysis of variance post-hoc comparison of nodal degree

Tab.4 One-way analysis of variance post-hoc comparison of local efficiency

Tab.5 One-way analysis of variance post-hoc comparison of global efficiency


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