| 38 | 0 | 14 |
| 下载次数 | 被引频次 | 阅读次数 |
为了探究因效性脑网络中各脑区间信息流动的关系，提出一种基于社区间脑电信号的因果网络情绪识别方法。首先，对预处理后的脑电信号提取其时频域特征，利用基于部分定向相干（Partial Directed Coherence,PDC）方法构建因效性脑网络，并使用Infomap社区划分算法对脑网络进行社区划分。然后，以各社区间部分定向相干因果关系作为边特征，各社区加权平均微分熵作为点特征，构成图数据。最后，将图数据送入图卷积神经网络进行分类识别。实验结果表明，相比以往全通道因效性情绪识别方法，所提方法利用大脑局部之间的有向因效信息降低了计算复杂度，且保持了较高的情绪识别准确率。
Abstract：In order to delve into the relationships of information flow among various brain regions within causal brain networks,a causal network emotion recognition methodology grounded in electroencephalogram(EEG)signals across communities is proposed.Firstly,time-frequency domain features are extracted from the preprocessed EEG signals.The partial directed coherence(PDC)method is adopted to build the casual brain network,and the Infomap community detection algorithm is used to divide the communities of the brain network.Next,agraph representation of the brain network is formulated,in which the causal interactions,quantified by PDC values between different communities,serve as the edge features,while the node features are defined by the weighted average differential entropy computed for each respective community.Finally,this constructed graph data is fed into a graph convolutional neural network for the ultimate task of emotion classification and recognition.Experimental results demonstrate that compared with the conventional full-channel causal emotion recognition approaches,the proposed method decreases the computational complexity by leveraging the directed causal information between the brain sections,and successfully maintains a high level of emotion recognition accuracy.
[1] KHARE S K,BAJAJ V.Time-frequency representation and convolutional neural network-based emotion recognition[J].IEEE Transactions on Neural Networks and Learning Systems, 2021, 32(7):2901-2909.
[2] CHATURVEDI V,KAUR A B,VARSHNEY V,et al.Music mood and human emotion recognition based on physiological signals:A systematic review[J].Multimedia Systems,2022,28(1):21-44.
[3] LI Y,ZHENG W M,ZONG Y,et al.A bihemisphere domain adversarial neural network model for EEG emotion recognition[J].IEEE Transactions on Affective Computing,2021,12(2):494-504.
[4] HUANG X H,ZHAO G Y,HONG X P,et al.Spontaneous facial micro-expression analysis using spatiotemporal completed local quantized patterns[J].Neurocomputing,2016,175:564-578.
[5] HUANG X H,WANG S J,LIU X,et al.Discriminative spatiotemporal local binary pattern with revisited integral projection for spontaneous facial micro-expression recognition[J].IEEE Transactions on Affective Computing,2019,10(1):32-47.
[6] YAN J J,ZHENG W M,XIN M H,et al.Integrating facial expression and body gesture in videos for emotion recognition[J].IEICE Transactions on Information and Systems,2014,97(3):610-613.
[7]王忠民，赵玉鹏，郑镕林，等．脑电信号情绪识别研究综述[J]．计算机科学与探索，2022,16(4):760-774.WANG Z M,ZHAO Y P,ZHENG R L,et al.Survey of research on EEG signal emotion recognition[J].Journal of Frontiers of Computer Science and Technology,2022,16(4):760-774.(in Chinese)
[8] LI Y,SHAFIPOUR R,MATEOS G,et al.Mapping brain structural connectivities to functional networks via graph encoder-decoder with interpretable latent embeddings[C]//2019IEEE Global Conference on Signal and Information Processing.Ottawa:IEEE,2020:1-5.
[9]黄义华，童玥，衡霞，等．基于动态功能性脑网络的情感分析[J]．计算机技术与发展，2022,32(2):20-25.HUANG Y H,TONG Y,HENG X,et al.Emotional analysis based on dynamic functional brain network[J].Computer Technology and Development,2022,32(2):20-25.(in Chinese)
[10]CHEN D W,WU F,WANG Z,et al.EEG-based emotion recognition with brain network using independent components analysis and granger causality[C]//2013International Conference on Computer Medical Applications.Sousse:IEEE,2013:1-6.
[11]杨剑，陈书燊，皇甫浩然，等．静息态脑电信号动态功能连接分析[J]．物理学报，2015,64(5):382-391.YANG J,CHEN S S,HUANGFU H R,et al.Dynamic functional connectivity of electroencephalogram in the resting state[J].Acta Physica Sinica,2015,64(5):382-391.(in Chinese)
[12]王忠民，冯璁，贺炎，等．基于脑电信号因效性脑网络的情感状态分析[J]．西安邮电大学学报，2020,25(2):35-40.WANG Z M,FENG C,HE Y,et al.Analysis of emotion states based on electroencephalogram signal effective brain network[J].Journal of Xi’an University of Posts and Telecommunications,2020,25(2):35-40.(in Chinese)
[13]端祥宇，袁冠，孟凡荣．动态社区发现方法研究综述[J]．计算机科学与探索，2021,15(4):612-630.DUAN X Y,YUAN G,MENG F R.Dynamic community detection:A survey[J].Journal of Frontiers of Computer Science and Technology,2021,15(4):612-630.(in Chinese)
[14]CHEN D W,WU F,WANG Z,et al.EEG-based emotion recognition with brain network using independent components analysis and granger causality[C]//2013International Conference on Computer Medical Applications.Sousse:IEEE,2013:1-6.
[15]GUO J L,FANG F,WANG W,et al.EEG emotion recognition based on granger causality and CapsNet neural network[C]//2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems.Nanjing:IEEE,2019:47-52.
[16]刘威辰．基于社区检测算法的ADHD结构脑网络模块研究[D]．太原：太原理工大学，2022:27-32.LIU W C.Research on ADHD structural brain network module based on community detection algorithm[D].Taiyuan:Taiyuan University of Technology,2022:27-32.(in Chinese)
[17]侯殿妮．基于权重随机块模型的ADHD功能脑网络社区结构的研究[D]．太原：太原理工大学，2024:24-29.HOU D N.Research on community structure of ADHD functional brain network based on weighted random block model[D].Taiyuan:Taiyuan University of Technology,2024:24-29.(in Chinese)
[18]谢娟英，张建宇．图卷积神经网络综述[J]．陕西师范大学学报（自然科学版），2024,52(2):89-101.XIE J Y,ZHANG J Y.The review of the graph convolutional neural networks[J].Journal of Shaanxi Normal University(Natural Science Edition),2024,52(2):89-101.(in Chinese)
[19]张成，汤璇，杨冬平，等．基于深度学习的脑电信号特征检测方法[J]．电子设计工程，2024,32(15):156-160.ZHANG C,TANG X,YANG D P,et al.Method for EEG signal feature detection based on deep learning[J].Electronic Design Engineering,2024,32(15):156-160.(in Chinese)
[20]KOELSTRA S,MUHL C,SOLEYMANI M,et al.DEAP:A database for emotion analysis;using physiological signals[J].IEEE Transactions on Affective Computing,2012,3(1):18-31.
[21]邓欣，高峰星，米建勋，等．基于稀疏表示的脑电（EEG）情感分类[J]．计算机应用研究，2019,36(3):801-806.DENG X,GAO F X,MI J X,et al.Classifying emotional EEG using sparse representation method[J].Application Research of Computers,2019,36(3):801-806.(in Chinese)
[22]CANDRA H,YUWONO M,CHAI R,et al.Investigation of window size in classification of EEG-emotion signal with wavelet entropy and support vector machine[C]//2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.Milan:IEEE,2015:7250-7253.
[23]GUPTA R,UR REHMAN LAGHARI K,FALK T H.Relevance vector classifier decision fusion and EEG graph-theoretic features for automatic affective state characterization[J]. Neurocomputing,2016,174:875-884.
[24]GAO Q,YANG Y,KANG Q J,et al.EEG-based emotion recognition with feature fusion networks[J].International Journal of Machine Learning and Cybernetics,2022,13(2):421-429.
[25]YU X,LI Z,ZANG Z,et al.Real-time EEG-based emotion recognition[J].Sensors,2023,23(18):7853.
基本信息:
DOI：10.13682/j.issn.2095-6533.2025.06.007
中图分类号:R318;TN911.7
引用信息:
[1]王忠民,雷欢.基于社区间脑电信号的因果网络情绪识别方法[J].西安邮电大学学报,2025,30(06):59-67.DOI:10.13682/j.issn.2095-6533.2025.06.007.
基金信息:
国家自然科学基金项目（61373116）; 陕西省自然科学基础研究计划重点项目（2024JC-ZDXM-37）