Chongxing Shi;C. L. Philip Chen;Shuzhen Li;Tong Zhang
{"title":"基于脑电图的情感识别的功能连接模式学习","authors":"Chongxing Shi;C. L. Philip Chen;Shuzhen Li;Tong Zhang","doi":"10.1109/TCDS.2024.3470248","DOIUrl":null,"url":null,"abstract":"Neuroscience research reveals that different emotions are associated with different functional connectivity structures of brain regions. However, many existing electroencephalography (EEG)-based emotion recognition methods use these connectivity patterns broadly without distinguishing between specific emotions. Additionally, the nonstationarity of EEG signals often results in high variations across different periods, leading models to extract time-specific features instead of emotional features. This article proposes a functional connectivity patterns learning network (FCPL) for EEG-based emotion recognition to address these challenges. FCPL includes a coefficient branch, a graph construction module, and a period domain adversarial module. These components capture individual characteristics and specific emotional connectivity patterns and reduce period-related variations, respectively. FCPL achieves state-of-the-art results: 42.04%/28.81% for seven-class subject-dependent/independent experiments on the MPED dataset, 97.45%/89.88% for subject-dependent/independent experiments on the SEED dataset, and 95.98%/96.19% for valence/arousal subject-dependent experiments and 67.90%/65.60% for valence/arousal subject-independent experiments on the DREAMER dataset. This work advances the exploration of functional connectivity structures in EEG signals from coarse-grained emotion-related patterns to fine-grained emotional distinctions, promoting neuroscience, and EEG-based emotion recognition technologies.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"17 3","pages":"480-494"},"PeriodicalIF":4.9000,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Functional Connectivity Patterns Learning for EEG-Based Emotion Recognition\",\"authors\":\"Chongxing Shi;C. L. Philip Chen;Shuzhen Li;Tong Zhang\",\"doi\":\"10.1109/TCDS.2024.3470248\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Neuroscience research reveals that different emotions are associated with different functional connectivity structures of brain regions. However, many existing electroencephalography (EEG)-based emotion recognition methods use these connectivity patterns broadly without distinguishing between specific emotions. Additionally, the nonstationarity of EEG signals often results in high variations across different periods, leading models to extract time-specific features instead of emotional features. This article proposes a functional connectivity patterns learning network (FCPL) for EEG-based emotion recognition to address these challenges. FCPL includes a coefficient branch, a graph construction module, and a period domain adversarial module. These components capture individual characteristics and specific emotional connectivity patterns and reduce period-related variations, respectively. FCPL achieves state-of-the-art results: 42.04%/28.81% for seven-class subject-dependent/independent experiments on the MPED dataset, 97.45%/89.88% for subject-dependent/independent experiments on the SEED dataset, and 95.98%/96.19% for valence/arousal subject-dependent experiments and 67.90%/65.60% for valence/arousal subject-independent experiments on the DREAMER dataset. This work advances the exploration of functional connectivity structures in EEG signals from coarse-grained emotion-related patterns to fine-grained emotional distinctions, promoting neuroscience, and EEG-based emotion recognition technologies.\",\"PeriodicalId\":54300,\"journal\":{\"name\":\"IEEE Transactions on Cognitive and Developmental Systems\",\"volume\":\"17 3\",\"pages\":\"480-494\"},\"PeriodicalIF\":4.9000,\"publicationDate\":\"2024-09-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Cognitive and Developmental Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10700842/\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Cognitive and Developmental Systems","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10700842/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Functional Connectivity Patterns Learning for EEG-Based Emotion Recognition
Neuroscience research reveals that different emotions are associated with different functional connectivity structures of brain regions. However, many existing electroencephalography (EEG)-based emotion recognition methods use these connectivity patterns broadly without distinguishing between specific emotions. Additionally, the nonstationarity of EEG signals often results in high variations across different periods, leading models to extract time-specific features instead of emotional features. This article proposes a functional connectivity patterns learning network (FCPL) for EEG-based emotion recognition to address these challenges. FCPL includes a coefficient branch, a graph construction module, and a period domain adversarial module. These components capture individual characteristics and specific emotional connectivity patterns and reduce period-related variations, respectively. FCPL achieves state-of-the-art results: 42.04%/28.81% for seven-class subject-dependent/independent experiments on the MPED dataset, 97.45%/89.88% for subject-dependent/independent experiments on the SEED dataset, and 95.98%/96.19% for valence/arousal subject-dependent experiments and 67.90%/65.60% for valence/arousal subject-independent experiments on the DREAMER dataset. This work advances the exploration of functional connectivity structures in EEG signals from coarse-grained emotion-related patterns to fine-grained emotional distinctions, promoting neuroscience, and EEG-based emotion recognition technologies.
期刊介绍:
The IEEE Transactions on Cognitive and Developmental Systems (TCDS) focuses on advances in the study of development and cognition in natural (humans, animals) and artificial (robots, agents) systems. It welcomes contributions from multiple related disciplines including cognitive systems, cognitive robotics, developmental and epigenetic robotics, autonomous and evolutionary robotics, social structures, multi-agent and artificial life systems, computational neuroscience, and developmental psychology. Articles on theoretical, computational, application-oriented, and experimental studies as well as reviews in these areas are considered.