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该文综述了基于脑电(EEG)情绪识别模型的研究进展，旨在探讨EEG信号在情绪识别中的应用潜力，分析现有模型的优缺点，并展望未来的发展方向.首先介绍了EEG信号的特点及其处理流程，包括数据预处理、特征提取和分类器训练.随后，综述了离散情绪模型和连续情绪模型，并介绍了相关的EEG情绪数据集.接着，详细阐述了基于机器学习和深度学习的EEG情绪识别模型，包括K近邻算法、支持向量机、线性判别分析、卷积神经网络、循环神经网络、图卷积神经网络等.此外，还讨论了基于深度迁移学习的EEG情绪识别模型，以解决EEG信号的个体差异性问题.现有研究表明，基于EEG的情绪识别模型在情感计算、人机交互、心理健康管理等领域展现出广阔的应用前景.然而，EEG情绪识别模型仍面临数据规模和多样性不足、个体差异性影响模型性能、计算资源密集等挑战.未来研究应致力于构建大规模、标准化的EEG情绪数据集，开发轻量化模型以适应资源受限环境，并探索跨模态融合和多领域知识迁移等方法，以进一步提升EEG情绪识别模型的性能和实用性.
Abstract：This paper reviews the research progress on EEG-based emotion recognition models, aiming to explore the potential of EEG signals in emotion recognition, analyze the advantages and disadvantages of existing models, and outline future development directions.Firstly, the characteristics of EEG signals and their processing workflows, including data preprocessing, feature extraction, and classifier training, are introduced. Subsequently, discrete emotion models and continuous emotion models are reviewed, along with relevant EEG emotion datasets. Then, EEG emotion recognition models based on machine learning and deep learning are elaborated, including K-nearest neighbor algorithm, support vector machine, linear discriminant analysis, convolutional neural network, recurrent neural network, and graph convolutional neural network. Additionally, EEG emotion recognition models based on deep transfer learning are discussed to address the issue of individual differences in EEG signals.Existing studies demonstrate that EEG-based emotion recognition models show broad application prospects in affective computing, human-computer interaction, and mental health management. However, EEG emotion recognition models still face challenges such as insufficient data scale and diversity, individual differences affecting model performance, and computational resource intensiveness.Future research should focus on constructing large-scale, standardized EEG emotion datasets, developing lightweight models to adapt to resource-constrained environments, and exploring cross-modal fusion and multi-domain knowledge transfer methods to further improve the performance and practicality of EEG emotion recognition models.
[1] BARRETT L F,GROSS J,CHRISTENSEN T C,et al.Knowing what you’re feeling and knowing what to do about it:Mapping the relation between emotion differentiation and emotion regulation[J].Cognition & emotion,2001,15(6):713-724.
[2] CHOATE-SUMMERS M.Emotion regulation and psychopathology:A transdiagnostic approach to etiology and treatment[J].Cognitive behaviour therapy,2011,40(1):78-78.
[3] BLACK M H,CHEN N T M,IYER K K,et al.Mechanisms of facial emotion recognition in autism spectrum disorders:Insights from eye tracking and electroencephalography[J].Neuroscience & biobehavioral reviews,2017,80:488-515.
[4] ALI ETEMAD S,ARYA A.Classification and translation of style and affect in human motion using RBF neural networks[J].Neurocomputing,2014,129:585-595.
[5] ABBASCHIAN B J,SIERRA-SOSA D,ELMAGHRABY A.Deep learning techniques for speech emotion recognition,from databases to models[J].Sensors,2021,21(4):1249.
[6] AGRAFIOTI F,HATZINAKOS D,ANDERSON A K.ECG pattern analysis for emotion detection[J].IEEE transactions on affective computing,2012,3(1):102-115.
[7] PERDIZ J,PIRES G,NUNES U J.Emotional state detection based on EMG and EOG biosignals:A short survey[C]//2017 IEEE 5th Portuguese Meeting on Bioengineering (ENBENG).Coimbra,Portugal:IEEE,2017:1-4.
[8] ALARCAO S M,FONSECA M J.Emotions recognition using EEG signals:A survey[J].IEEE transactions on affective computing,2019,10(3):374-393.
[9] DAVIDSON R J.What does the prefrontal cortex “do” in affect:Perspectives on frontal EEG asymmetry research[J].Biological psychology,2004,67(1-2):219-234.
[10] 吕宝粮，张亚倩，郑伟龙.情感脑机接口研究综述[J].智能科学与技术学报，2021,3(1):36-48.
[11] CARMENA J M,LEBEDEV M A,CRIST R E,et al.Learning to control a brain-machine interface for reaching and grasping by primates[J].Plos biology,2003,1(2):e42.
[12] HOCHBERG L R,SERRUYA M D,FRIEHS G M,et al.Neuronal ensemble control of prosthetic devices by a human with tetraplegia[J].Nature,2006,442(7099):164-171.
[13] CHAUDHARY U,BIRBAUMER N,RAMOS-MURGUIALDAY A.Brain-computer interfaces for communication and rehabilitation[J].Nature reviews neurology,2016,12(9):513-525.
[14] CINCOTTI F,MATTIA D,ALOISE F,et al.Non-invasive brain-computer interface system:Towards its application as assistive technology[J].Brain research bulletin,2008,75(6):796-803.
[15] 杜晓燕，李颖洁，朱贻盛，等.脑电信号伪迹去除的研究进展[J].生物医学工程学杂志，2008(2):464-467,471.
[16] MAKEIG S,JUNG T P,BELL A J,et al.Blind separation of auditory event-related brain responses into independent components[J].Proceedings of the national academy of sciences,1997,94(20):10979-10984.
[17] MALLAT S G.A theory for multiresolution signal decomposition:the wavelet representation[J].IEEE transactions on pattern analysis and machine intelligence,1989,11(7):674-693.
[18] DUAN R N,ZHU J Y,LU B L.Differential entropy feature for EEG-based emotion classification[C]//2013 6th International IEEE/EMBS Conference on Neural Engineering (NER).San Diego,CA,USA:IEEE,2013:81-84.
[19] AL-NAFJAN A,HOSNY M,AL-WABIL A,et al.Classification of human emotions from electroencephalogram (EEG) signal using deep neural network[J].International journal of advanced computer science and applications,2017,8(9):419-425.
[20] EKMAN P.An argument for basic emotions[J].Cognition and emotion,1992,6(3/4):169-200.
[21] PLUTCHIK R.A psychoevolutionary theory of emotions[J].Social science information,1982,21(4/5):529-553.
[22] RUSSELL J A.A circumplex model of affect[J].Journal of personality and social psychology,1980,39(6):1161-1178.
[23] MEHRABIAN A.Pleasure-arousal-dominance:A general framework for describing and measuring individual differences in Temperament[J].Current psychology,1996,14(4):261-292.
[24] 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.
[25] ZHENG W L,LU B L.Investigating critical frequency bands and channels for EEG-Based emotion recognition with deep neural networks[J].IEEE transactions on autonomous mental development,2015,7(3):162-175.
[26] SOLEYMANI M,LICHTENAUER J,PUN T,et al.A multimodal database for affect recognition and implicit tagging[J].IEEE transactions on affective computing,2012,3(1):42-55.
[27] SUBRAMANIAN R,WACHE J,ABADI M K,et al.ASCERTAIN:Emotion and personality recognition using commercial sensors[J].IEEE transactions on affective computing,2018,9(2):147-160.
[28] ZHENG W L,LIU W,LU Y,et al.EmotionMeter:A multimodal framework for recognizing human emotions[J].IEEE transactions on cybernetics,2019,49(3):1110-1122.
[29] LIU W,QIU J L,ZHENG W L,et al.Comparing recognition performance and robustness of multimodal deep learning models for multimodal emotion recognition[J].IEEE transactions on cognitive and developmental systems,2022,14(2):715-729.
[30] JIANG W B,LIU X H,ZHENG W L,et al.SEED-VII:A multimodal dataset of six basic emotions with continuous labels for emotion recognition[J].IEEE transactions on affective computing,2024:1-16.
[31] LIU W,ZHENG W L,LI Z,et al.Identifying similarities and differences in emotion recognition with EEG and eye movements among Chinese,German,and French people[J].Journal of neural engineering,2022,19(2):026012.
[32] COVER T,HART P.Nearest neighbor pattern classification[J].IEEE transactions on information theory,1967,13(1):21-27.
[33] BASTOS-FILHO T F,FERREIRA A,ATENCIO A C,et al.Evaluation of feature extraction techniques in emotional state recognition[C]//2012 4th International Conference on Intelligent Human Computer Interaction (IHCI).Kharagpur,India:IEEE,2012:1-6.
[34] LI M,XU H,LIU X,et al.Emotion recognition from multichannel EEG signals using k-nearest neighbor classification[J].Technology and health care,2018,26:509-519.
[35] CORTES C,VAPNIK V.Support-vector networks[J].Machine learning,1995,20(3):273-297.
[36] JATUPAIBOON N,PAN-NGUM S,ISRASENA P.Real-time EEG-based happiness detection system[J].The scientific world journal,2013,2013(1):618649.
[37] JALILIFARD A,PIZZOLATO E B,ISLAM M K.Emotion classification using single-channel scalp-EEG recording[C]//2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).Orlando,FL,USA:IEEE,2016:845-849.
[38] FISHER R A.The use of multiple measurements in taxonomic problems[J].Annals of eugenics,1936,7(2):179-188.
[39] BHARDWAJ A,GUPTA A,JAIN P,et al.Classification of human emotions from EEG signals using SVM and LDA classifiers[C]//2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN).Noida,Delhi-NCR,India:IEEE,2015:180-185.
[40] PAUL S,MAZUMDER A,GHOSH P,et al.EEG based emotion recognition system using MFDFA as feature extractor[C]//2015 International Conference on Robotics,Automation,Control and Embedded Systems (RACE).Chennai,India:IEEE,2015:1-5.
[41] CHIEN Y.Pattern classification and scene analysis[J].IEEE transactions on automatic control,1974,19(4):462-463.
[42] OKTAVIA N Y,WIBAWA A D,PANE E S,et al.Human emotion classification based on EEG signals using na?ve bayes method[C]//2019 International Seminar on Application for Technology of Information and Communication (iSemantic).Semarang,Indonesia:IEEE,2019:319-324.
[43] DABAS H,SETHI C,DUA C,et al.Emotion classification using EEG signals[C]//Proceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence.Shenzhen,China:ACM,2018:380-384.
[44] JAFARI M,SHOEIBI A,KHODATARS M,et al.Emotion recognition in EEG signals using deep learning methods:A review[J].Computers in biology and medicine,2023,165:107450.
[45] LECUN Y,BENGIO Y,HINTON G.Deep learning[J].Nature,2015,521(7553):436-444.
[46] SCHIRRMEISTER R T,SPRINGENBERG J T,FIEDERER L D J,et al.Deep learning with convolutional neural networks for EEG decoding and visualization[J].Human brain mapping,2017,38(11):5391-5420.
[47] KWON Y H,SHIN S B,KIM S D.Electroencephalography based fusion two-dimensional (2D)-convolution neural networks (CNN) model for emotion recognition system[J].Sensors,2018,18(5):1383.
[48] YANG H,HAN J,MIN K.A multi-column CNN model for emotion recognition from EEG signals[J].Sensors,2019,19(21):4736.
[49] CHAO H,DONG L.Emotion recognition using three-dimensional feature and convolutional neural network from multichannel EEG signals[J].IEEE sensors journal,2021,21(2):2024-2034.
[50] ELMAN J L.Finding structure in time[J].Cognitive science,1990,14(2):179-211.
[51] LI X,SONG D,ZHANG P,et al.Emotion recognition from multi-channel EEG data through convolutional recurrent neural network[C]//2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).Shenzhen,China:IEEE,2016:352-359.
[52] ZHANG T,ZHENG W,CUI Z,et al.Spatial-temporal recurrent neural network for emotion recognition[J].IEEE transactions on cybernetics,2019,49(3):839-847.
[53] ALHAGRY S,ALY A,A.R.Emotion recognition based on EEG using LSTM recurrent neural network[J].International journal of advanced computer science and applications,2017,8(10):355-358.
[54] KIPF T N,WELLING M.Semi-supervised classification with graph convolutional networks[J].arXiv:1609.02907.
[55] GAO Y,FU X,OUYANG T,et al.EEG-GCN:Spatio-temporal and self-adaptive graph convolutional networks for single and multi-view EEG-based emotion recognition[J].IEEE signal processing letters,2022,29:1574-1578.
[56] DU G,SU J,ZHANG L,et al.A multi-dimensional graph convolution network for EEG emotion recognition[J].IEEE transactions on instrumentation and measurement,2022,71:1-11.
[57] QIU X,WANG S,WANG R,et al.A multi-head residual connection GCN for EEG emotion recognition[J].Computers in biology and medicine,2023,163:107126.
[58] VASWANI A,SHAZEER N,PARMAR N,et al.Attention is all you need[J].Advances in neural information processing systems,2017,30.
[59] WANG Z,WANG Y,HU C,et al.Transformers for EEG-based emotion recognition:A hierarchical spatial information learning model[J].IEEE sensors journal,2022,22(5):4359-4368.
[60] TAO W,LI C,SONG R,et al.EEG-based emotion recognition via channel-wise attention and self attention[J].IEEE transactions on affective computing,2023,14(1):382-393.
[61] ZHAO S,JIA G,YANG J,et al.Emotion recognition from multiple modalities:Fundamentals and methodologies[J].IEEE signal processing magazine,2021,38(6):59-73.
[62] ROSHDY A,KARAR A,KORK S A,et al.Advancements in EEG emotion recognition:Leveraging multi-modal database integration[J].Applied sciences,2024,14(6):2487.
[63] WANG Q,WANG M,YANG Y,et al.Multi-modal emotion recognition using EEG and speech signals[J].Computers in biology and medicine,2022,149:105907.
[64] PAN S J,YANG Q.A Survey on transfer learning[J].IEEE transactions on knowledge and data engineering,2010,22(10):1345-1359.
[65] GANIN Y,USTINOVA E,AJAKAN H,et al.Domain-adversarial training of neural networks[J].Domain adaptation in computer vision applications,2017:189-209.
[66] JIN Y M,LUO Y D,ZHENG W L,et al.EEG-based emotion recognition using domain adaptation network[C]//2017 International Conference on Orange Technologies (ICOT).Singapore,Singapore:IEEE,2017:222-225.
[67] DIMOND S J,FARRINGTON L,JOHNSON P.Differing emotional response from right and left hemispheres[J].Nature,1976,261(5562):690-692.
[68] DAVIDSON R J,EKMAN P,SARON C D,et al.Approach-withdrawal and cerebral asymmetry:Emotional expression and brain physiology[J].Journal of personality and social psychology,1990,58(2):330-341.
[69] DAVIDSON R J,PIZZAGALLI D,NITSCHKE J B,et al.Depression:Perspectives from affective neuroscience[J].Annual review of psychology,2002,53(1):545-574.
[70] LI Y,ZHENG W,ZONG Y,et al.A bi-hemisphere domain adversarial neural network model for EEG emotion recognition[J].IEEE transactions on affective computing,2021,12(2):494-504.
[71] LI Y,ZHENG W,WANG L,et al.From regional to global brain:A novel hierarchical spatial-temporal neural network model for EEG emotion recognition[J].IEEE transactions on affective computing,2022,13(2):568-578.
[72] DU X,MA C,ZHANG G,et al.An efficient LSTM network for emotion recognition from multichannel EEG signals[J].IEEE transactions on affective computing,2022,13(3):1528-1540.
[73] LUO Y,ZHANG S Y,ZHENG W L,et al.WGAN domain adaptation for EEG-based emotion recognition[J].Neural information processing,2018,11305:275-286.
[74] LI J,QIU S,DU C,et al.Domain adaptation for EEG emotion recognition based on latent representation similarity[J].IEEE transactions on cognitive and developmental systems,2020,12(2):344-353.
[75] CHEN H,JIN M,LI Z,et al.MS-MDA:Multisource marginal distribution adaptation for cross-subject and cross-session EEG emotion recognition[J].Frontiers in neuroscience,2021,15:778488.
[76] KULIS B.Metric learning:A survey[J].Foundations and trends in machine learning,2013,5(4):287-364.
[77] WANG Y,QIU S,ZHAO C,et al.EEG-based emotion recognition with prototype-based data representation[C]//2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).Berlin,Germany:IEEE,2019:684-689.
[78] SHEN F,PENG Y,DAI G,et al.Coupled projection transfer metric learning for cross-session emotion recognition from EEG[J].Systems,2022,10(2):47.
[79] ZHOU R,ZHANG Z,FU H,et al.PR-PL:A novel prototypical representation based pairwise learning framework for emotion recognition using EEG signals[J].IEEE transactions on affective computing,2023:1-14.
[80] ZHOU R,YE W,ZHANG Z,et al.EEGMatch:Learning with incomplete labels for semisupervised EEG-based cross-subject emotion recognition[J].IEEE transactions on neural networks and learning systems,2024:1-15.
[81] WANG F,WU S,ZHANG W,et al.Emotion recognition with convolutional neural network and EEG-based EFDMs[J].Neuropsychologia,2020,146:107506.
[82] BHOSALE S,CHAKRABORTY R,KOPPARAPU S K.Calibration free meta learning based approach for subject independent EEG emotion recognition[J].Biomedical signal processing and control,2022,72:103289.
[83] FUNG P,BERTERO D,WAN Y,et al.Towards empathetic human-robot interactions[J].Computational linguistics and intelligent text processing,2018,9624:173-193.
[84] ZHOU T H,LIANG W,LIU H,et al.EEG emotion recognition applied to the effect analysis of music on emotion changes in psychological healthcare[J].International journal of environmental research and public health,2022,20(1):378.
[85] GIRI M,BANSAL M,RAMESH A,et al.Enhancing safety in vehicles using emotion recognition with artificial intelligence[C]//2023 IEEE 8th International Conference for Convergence in Technology (I2CT).Lonavla,India:IEEE,2023:1-10.
[86] 钟铭恩，吴平东，彭军强，等.基于脑电信号的驾驶员情绪状态识别研究[J].中国安全科学学报，2011,21(9):64-69.
基本信息:
DOI：10.13715/j.issn.2096-644X.20250205.0001
中图分类号:R318;TN911.7
引用信息:
[1]周哲昊,孙托,吴灿标等.脑电情绪识别模型研究进展[J].湘潭大学学报(自然科学版),2025,47(03):35-53.DOI:10.13715/j.issn.2096-644X.20250205.0001.
基金信息:
国家自然科学基金面上项目(62176089); 湖南省自然科学基金优秀青年项目(2023JJ20024);湖南省自然科学基金医卫联合项目(2024JJ9551)