文献类型：期刊文献

中文题名：基于脑电功能连接微状态序列递归分析的情绪解码模型

英文题名：Emotion Decoding Model Based on Recurrent Analysis of EEG Functional Connectivity Microstate Sequence

作者：李光强[1];陈宁[1];林家骏[1]

机构：[1]华东理工大学信息科学与工程学院,上海200237

年份：2023

卷号：49

期号：1

起止页码：108

中文期刊名：华东理工大学学报（自然科学版）

外文期刊名：Journal of East China University of Science and Technology

收录：Scopus;北大核心:【北大核心2020】；CSCD:【CSCD_E2023_2024】；

基金：国家自然科学基金面上项目(61771196)。

语种：中文

中文关键词：脑功能连接;微状态;递归分析;脑电图;情绪解码

外文关键词：brain functional connection;microstate;recurrence analysis;electroencephalogram;emotion decoding

摘要：脑电微状态代表准稳定的全局神经元活动,被认为是大脑动力学的构建模块,而基于微状态概率统计的特征不能很好表征脑电的动态变化特性。针对该问题提出了基于脑电微状态序列递归分析的情绪解码模型。该模型通过聚类从脑电功能连接模式中提取出具有代表性的微状态典型模式,将原始脑电信号映射为微状态时间序列,构建递归图表征脑电动力学特性,并利用卷积神经网络对递归图实现情绪解码。在公开的脑电情绪数据集(DEAP)上的实验结果表明该模型实现了比传统微状态方法更好的情绪解码效果。
Electroencephalogram(EEG) functional connectivity microstates represent quasi-stable global neuronal activity and are considered as the building blocks of brain dynamics. Therefore, the microstate sequence analysis is a promising method for understanding the brain dynamics behind various emotional states. Recent studies have shown that EEG microstates sequence is non-Markov and non-stationary, which also reflects the importance of temporal dynamics between different emotional states. However, the characteristics based on microstate probability statistics cannot well represent the dynamic change of EEG signals. These inspire us to use recurrence analysis to model time series of microstates and capture non-obvious correlations in time series. To this end, we propose an emotion decoding model based on recurrence analysis of EEG functional connectivity microstate sequences. Firstly, the microstate of each frame signal is established by using the time domain correlation between channels, and the typical microstate patterns are obtained through clustering. Then, the original EEG signals are mapped onto microstate time series according to typical microstate patterns, and the time series are analyzed recursively to construct recursive charts for characterizing the EEG dynamic characteristics. Furthermore, convolutional neural networks(CNNs) are used to predict the regression of emotions based on the valence or arousal value. Finally, it is shown via experimental results on DEAP dataset that the regression effect of the mean square error(MSE) of the model in two dimensions of valence and arousal is 3.45 and 2.79, respectively, which is better than the MSE(3.87 and 3.25) of the traditional statistical method.