基于自监督图网络的脑电情绪识别方法研究  

Study on EEG Emotion Recognition Method Based on Self-supervised Graph Network

作　　者：张嘉翔 潘敏 张瑞[1] ZHANG Jiaxiang;PAN Min;ZHANG Rui(Medical Big Data Research Center,Northwest University,Xi’an 710127,China)

机构地区：[1]西北大学医学大数据研究中心,西安710127

出　　处：《计算机科学》2025年第5期122-127,共6页Computer Science

基　　金：国家自然科学基金(12071369,62006189);陕西省自然科学基金(2021JQ-430,2023-JC-QN-0028);中国博士后科学基金(2022M722580)。

摘　　要：脑电情绪识别是指通过分析人类脑电信号来识别相应情绪状态的技术,其在医疗健康、人机交互等领域有着广泛的应用。目前,脑电情绪识别往往借助机器学习或深度学习方法对标签脑电数据进行充分训练从而能够辨别不同情绪状态。然而,以往方法严重依赖于大量标签数据,而数据标注耗时耗力,并且脑电信号的个体差异性导致传统方法表现不佳。同时,研究表明,脑电信号的空间结构信息能够反映不同情绪状态下蕴含的脑区相互作用,有助于提高情绪的辨识度。为此,提出了一种基于自监督图网络的脑电情绪识别方法。首先,使用减数分裂方法预处理脑电信号;其次,利用图卷积网络提取脑电信号的空间结构信息,并设计自监督辅助任务对图卷积网络进行训练;最后,在公开数据集SEED和SEED-IV上验证所提方法的可行性和有效性,其情绪识别准确率为95.16%和80.23%,优于现有方法。EEG emotion recognition refers to the technology of identifying human emotional states by analyzing electroencephalogram(EEG)signals,which has wide application prospects in some fields such as medical health,and human-computer interaction.Currently,EEG-based emotion recognition frequently relies on machine learning or deep learning techniques to thoroughly train labeled EEG data and differentiate various emotional states.However,such methods require a lot of data annotation,which is time-consuming and labor-intensive.Meanwhile,research shows that the spatial structure information of EEG signals can reflect the interaction of brain areas related to different emotional states,which can help identify emotional characteristics.To this end,this paper proposes an EEG emotion recognition method based on self-supervised graph network.First,the meiosis method is used to preprocess the EEG signal.Then,a graph convolutional network is used to extract spatial structure information from EEG signals,and the network is trained through self-supervised tasks.Finally,the feasibility and effectiveness of the proposed method have been validated through numerical experiments using the public datasets SEED and SEED-IV.Numerical results show that the accuracy of emotion recognition is 95.16%and 80.23%,which is superior to current methods.

关 键 词：脑电信号 情绪识别 减数分裂 自监督学习 图卷积网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]