面向情绪识别任务的EEG特征提取研究综述

doi:10.3969/j.issn.0258-8021.2025.04.009

Abstract
Figure/Table
References (0)
Related Citation (15)

Download: PDF (1167 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Emotion plays a crucial role in human-computer interaction because it makes psychological and physiological responses to the environment. Accurate emotion recognition is vital for applications in the fields of medicine, education, psychology, and military. Compared to non-physiological signals, such as facial expressions and movements, physiological signals are difficult to disguise. As a type of physiological signal, electroencephalogram (EEG) offers advantages in terms of collectionconvenience and recognition accuracy and thus is often used in the field of emotion recognition. This review summarized recent progresses made in the feature extraction for EEG-based emotion recognition, introduced conventional time-frequency features, spatial domain features, brain network features, shallow nonlinear and manifold features, as well as deep learning-based feature extraction methods. Furthermore, this review provided an outlook on future directions.

Key words： EEG emotion recognition feature extraction deep learning

Received: 11 November 2024

PACS:

R318

Corresponding Authors: ^*E-mail: yuecaitong@zzu.edu.cn

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Li Mengmeng
	Xue Wenbo
	Liu Yunyang
	He Yudie
	Yue Caitong
	Li Zhihui
	Shang Zhigang

Cite this article:

Li Mengmeng,Xue Wenbo,Liu Yunyang, et al. A Review of EEG Feature Extraction for Emotion Recognition Tasks[J]. Chinese Journal of Biomedical Engineering, 2025, 44(4): 465-477.

URL:

http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2025.04.009 OR http://cjbme.csbme.org/EN/Y2025/V44/I4/465

[1] 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): 34-48.
[2] 张杨松, 卓彦, 尧德中. 脑电磁成像进展及展望[J]. 中国科学:生命科学, 2020, 50(11): 1268-1284.
[3] 尧德中, 秦云. 从脑-机接口到脑-器交互[J]. 重庆邮电大学学报(自然科学版), 2024, 36(4): 738-746.
[4] Prabowo D, Nugroho H, Setiawan N, et al. A systematic literature review of emotion recognition using EEG signals[J]. Cognitive Systems Research, 2023, 82: 101152.
[5] 聂聃, 王晓韡, 段若男, 等. 基于脑电的情绪识别研究综述[J]. 中国生物医学工程学报, 2012, 31(4): 595-606.
[6] Wang Y, Zhang B, Di L. Research progress of EEG-based emotion recognition: a survey[J]. ACM Computing Surveys, 2024, 56(11): 288.
[7] 郭苗苗, 徐桂芝, 王磊, 等. 听觉脑-机接口技术实验范式的研究进展[J]. 中国生物医学工程学报, 2013, 32(5): 613-619.
[8] 郑玉甫, 许敏鹏, 明东. 脑-机接口操控效果差异及其预测研究综述[J]. 中国生物医学工程学报, 2018, 37(6): 749-755.
[9] Cui X, Wu Y, Wu J, et al. A review: music-emotion recognition and analysis based on EEG signals[J]. Frontiers in Neuroinformatics, 2022, 16: 997282.
[10] Liu H, Du L, Li L, et al. A review of research on feature extraction for emotion recognition based on EEG signals[C]//Proceedings of the 2022 5th International Conference on Artificial Intelligence and Big Data (ICAIBD). New York: IEEE, 2022: 416-421.
[11] 周如双, 赵慧琳, 林玮玥, 等. 基于深浅特征融合的深度卷积残差网络的脑电情绪识别模型[J]. 中国生物医学工程学报, 2021, 40(6): 641-652.
[12] Ekman P, Friesen W. Constants across cultures in the face and emotion[J]. Journal of Personality and Social Psychology, 1971, 17(2): 124-129.
[13] Ekman P. An argument for basic emotions[J]. Cognition & Emotion, 1992, 6(3-4): 169-200.
[14] Plutchik R. The nature of emotions: human emotions have deep evolutionary roots, a fact that may explain their complexity and provide tools for clinical practice[J]. American Scientist, 2001, 89(4): 344-350.
[15] Johnston V. Why We Feel: the Science of Human Emotions[M]. Cambridge: Perseus Publishing, 1999.
[16] Russell J. A circumplex model of affect[J]. Journal of Personality and Social Psychology, 1980, 39(6): 1161-1178.
[17] Mehrabian A. Basic Dimensions for a General Psychological Theory: Implications for Personality, Social, Environmental, and Developmental Studies[M]. Cambridge: Oelgeschlager, Gunn & Hain, 1980.
[18] Koelstra S, Muhl C, Soleymani M, et al. DEAP: a database for emotion analysis; using physiological signals[J]. IEEE Transactions on Affective Computing, 2011, 3(1): 18-31.
[19] Zheng W, Lu B. 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.
[20] Zheng W, Liu W, Lu Y, et al. EmotionMeter: a multimodal framework for recognizing human emotions[J]. IEEE Transactions on Cybernetics, 2018, 49(3): 1110-1122.
[21] Hamzah H, Abdalla K. EEG-based emotion recognition systems; comprehensive study[J]. Heliyon, 2024, 10(10): e31485.
[22] 乔锐, 田银. 视听工作记忆的相位同步与跨频耦合研究[J]. 重庆邮电大学学报(自然科学版), 2024, 36(3): 450-457.
[23] Petrantonakis P, Hadjileontiadis L. Emotion recognition from EEG using higher order crossings[J]. IEEE Transactions on Information Technology in Biomedicine, 2009, 14: 186-197.
[24] Lan Z, Sourina O, Wang L, et al. Real-time EEG-based emotion monitoring using stable features[J]. Visual Computer, 2016, 32(3): 347-358.
[25] Liang Z, Oba S, Ishii S. An unsupervised EEG decoding system for human emotion recognition[J]. Neural Networks, 2019, 116: 257-268.
[26] Kusumaningrum T, Faqih A, Kusumoputro B. Emotion recognition based on DEAP database using EEG time-frequency features and machine learning methods[C]//Proceedings of the 2019 International Conference on Science & Technology (ICoST 2019). Bristol: IOP Publishing, 2020: 012020.
[27] Tripathi S, Acharya S, Sharma R, et al. Using deep and convolutional neural networks for accurate emotion classification on DEAP data[C]//Proceedings of the 2017 AAAI Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2017: 4746-4752.
[28] Pillalamarri R, Shanmugam U. Advances in EEG-based multimodal emotion recognition: A comprehensive review[C]//Proceedings of the 2023 Innovations in Power and Advanced Computing Technologies (i-PACT). New York: IEEE, 2023: 1-7.
[29] Lin Y, Wang C, Jung T, et al. EEG-based emotion recognition in music listening[J]. IEEE Transactions on Biomedical Engineering, 2010, 57(7): 1798-1806.
[30] Yoon H, Chung S. EEG-based emotion estimation using Bayesian weighted-log-posterior function and perceptron convergence algorithm[J]. Computers in Biology and Medicine, 2013, 43(12): 2230-2237.
[31] Li G, Ouyang D, Yuan Y, et al. An EEG data processing approach for emotion recognition[J]. IEEE Sensors Journal, 2022, 22(11): 10751-10763.
[32] Bouazizi S, Benmohamed E, Ltifi H. Enhancing EEG-based emotion recognition using PSD-grouped deep echo state network[J]. Journal of Universal Computer Science, 2023, 29(10), 1116-1138.
[33] Gill R, Singh J. A discrete wavelet transform based feature extraction for EEG based emotion recognition system[C]//Proceedings of the National Conference on Advances in Applied Sciences and Mathematics (NCASM). Melville: AIP Publishing, 2022, 2357(1): 100024.
[34] Mehmood R, Lee H. EEG based emotion recognition from human brain using Hjorth parameters and SVM[J]. International Journal of Bio-Science and Bio-Technology, 2015, 7(3): 23-32.
[35] 陈萌, 李幼军, 刘岩. 脑电信号与个人情绪状态关联性分析研究 [J]. 计算机科学与探索, 2017, 11(5): 794-801.
[36] Chen T, Ju S, Yuan X, et al. Emotion recognition using empirical mode decomposition and approximation entropy[J]. Computers & Electrical Engineering, 2018, 72: 383-392.
[37] Mert A, Akan A. Emotion recognition from EEG signals by using multivariate empirical mode decomposition[J]. Pattern Analysis and Applications, 2018, 21: 81-89.
[38] Gao Q, Wang C, WANG Z, et al. EEG based emotion recognition using fusion feature extraction method[J]. Multimedia Tools and Applications, 2020, 79(37): 27057-27074.
[39] Zhang S, Zhang Q. A multidimensional feature extraction method based on MSTBN and EEMD-WPT for emotion recognition from EEG signals[C]//Proceedings of the 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). New York: IEEE, 2022: 2042-2048.
[40] Kamble K, Sengupta J. A comprehensive survey on emotion recognition based on electroencephalograph (EEG) signals[J]. Multimedia Tools and Applications, 2023, 82(18): 27269-27304.
[41] Conneau A, Essid S. Assessment of new spectral features for EEG-based emotion recognition[C]//Proceedings of the 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). New York: IEEE, 2014: 4698-4702.
[42] Chen J, Jiang D, Zhang Y. A common spatial pattern and wavelet packet decomposition combined method for EEG-based emotion recognition[J]. Journal of Advanced Computational Intelligence and Intelligent Informatics, 2019, 23(2): 274-281.
[43] Yan M, Lv Z, Sun W, et al. An improved common spatial pattern combined with channel-selection strategy for electroencephalography-based emotion recognition[J]. Medical Engineering & Physics, 2020, 83: 130-141.
[44] Li G, Yuan B, Ouyang D, et al. Emotion recognition based on selected EEG signals by common spatial pattern[J]. IEEE Sensors Journal, 2024, 24(6): 8414-8426.
[45] 尹顺杰, 陈凯, 薛开庆, 等. 基于典型相关分析的脑网络研究方法综述[J].中国生物医学工程学报, 2024, 43: 240-251.
[46] 张舒涵, 牟宇锋, 李存波, 等. 基于脑电网络的情绪识别研究进展[J]. 电子科技大学学报, 2024, 53(5): 771-784.
[47] Gao Y, Wang X, Potter T, et al. Single-trial EEG emotion recognition using Granger causality/transfer entropy analysis[J]. Journal of Neuroscience Methods, 2020, 346: 108904.
[48] Cui G, Li X, Touyama H. Emotion recognition based on group phase locking value using convolutional neural network[J]. Scientific Reports, 2023, 13(1): 3769.
[49] Wang Z, Chen Z, Zhang J. EEG emotion recognition based on PLV-rich-club dynamic brain function network[J]. Applied Intelligence, 2023, 53(14): 17327-17345.
[50] Wang Z, Li S, Zhang J, et al. Emotion recognition based on phase-locking value brain functional network and topological data analysis[J]. Neural Computing and Applications, 2024, 36(14): 7903-7922.
[51] Vempati R, Sharma L. A systematic review on automated human emotion recognition using electroencephalogram signals and artificial intelligence[J]. Results in Engineering, 2023, 18: 101027.
[52] Zheng W, Lu B. 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.
[53] Nawaz R, Cheah K, Nisar H, et al. Comparison of different feature extraction methods for EEG-based emotion recognition[J]. Biocybernetics and Biomedical Engineering, 2020, 40(3): 910-926.
[54] Patel P, Annavarapu R. EEG-based human emotion recognition using entropy as a feature extraction measure[J]. Brain Informatics, 2021, 8(1): 20.
[55] Guo W, Li Y, Liu M, et al. Functional connectivity-enhanced feature-grouped attention network for cross-subject EEG emotion recognition[J]. Knowledge-Based Systems, 2024, 283: 111199.
[56] Tibermacine I, Russo S, Tibermacine A, et al. Riemannian geometry-based EEG approaches: a literature review[J]. Signal Processing, 2024, 2407: 20250.
[57] Gao Y, Sun X, Meng M, et al. EEG emotion recognition based on enhanced SPD matrix and manifold dimensionality reduction[J]. Computers in Biology and Medicine, 2022, 146: 105606.
[58] Wu M, Hu S, Wei B, et al. A novel deep learning model based on the ICA and Riemannian manifold for EEG-based emotion recognition[J]. Journal of Neuroscience Methods, 2022, 378: 109642.
[59] Wang Y, He H. EEG emotion recognition based on manifold geomorphological features in Riemannian space[J]. IEEE Intelligent Systems, 2024, 39(4): 23-36.
[60] Samal P, Hashmi M. Role of machine learning and deep learning techniques in EEG-based BCI emotion recognition system: a review[J]. Artificial Intelligence Review, 2024, 57(3): 50.
[61] Ma W, Zheng Y, Li T, et al. A comprehensive review of deep learning in EEG-based emotion recognition: classifications, trends, and practical implications[J]. PeerJ Computer Science, 2024, 10: e2065.
[62] Yang H, Han J, Min K. A multi-column CNN model for emotion recognition from EEG signals[J]. Sensors, 2019, 19(21): 4736.
[63] Baradaran F, Farzan A, Danishvar S, et al. Customized 2D CNN model for the automatic emotion recognition based on EEG signals[J]. Electronics, 2023, 12(10): 2232.
[64] Li T, Fu B, Wu Z, et al. EEG-based emotion recognition using spatial-temporal-connective features via multi-scale CNN[J]. IEEE Access, 2023, 11: 41859-41867.
[65] 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.
[66] Sengür D, Siuly S. Efficient approach for EEG-based emotion recognition[J]. Electronics Letters, 2020, 56(25): 1361-1364.
[67] Li C, Bao Z, Li L, et al. Exploring temporal representations by leveraging attention-based bidirectional LSTM-RNNs for multi-modal emotion recognition[J]. Information Processing & Management, 2020, 57(3): 102185.
[68] Sakalle A, Tomar P, Bhardwaj H, et al. A LSTM based deep learning network for recognizing emotions using wireless brainwave driven system[J]. Expert Systems with Applications, 2021, 173: 114516.
[69] Panachakel J, Ranjana H, Sidharth S, et al. CSP-LSTM based emotion recognition from EEG signals[C]//Proceedings of the 2023 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE). New York: IEEE, 2023: 289-294.
[70] Wang X, Ren Y, Luo Z, et al. Deep learning-based EEG emotion recognition: Current trends and future perspectives[J]. Frontiers in Psychology, 2023, 14: 1126994.
[71] Liu J, Wu G, Luo Y, et al. EEG-based emotion classification using a deep neural network and sparse autoencoder[J]. Frontiers in Systems Neuroscience, 2020, 14: 43.
[72] Liang Z, Zhou R, Zhang L, et al. EEGFuseNet: hybrid unsupervised deep feature characterization and fusion for high-dimensional EEG with an application to emotion recognition[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2021, 29: 1913-1925.
[73] Iyer A, Das S, Teotia R, et al. CNN and LSTM based ensemble learning for human emotion recognition using EEG recordings[J]. Multimedia Tools and Applications, 2023, 82(4): 4883-4896.
[74] Wang T, Huang X, Xiao Z, et al. EEG emotion recognition based on differential entropy feature matrix through 2D-CNN-LSTM network[J]. EURASIP Journal on Advances in Signal Processing, 2024, 2024(1): 49.
[75] Guo W, Wang Y. Convolutional gated recurrent unit-driven multidimensional dynamic graph neural network for subject-independent emotion recognition[J]. Expert Systems with Applications, 2024, 238: 121889.
[76] Yao X, Li T, Ding P, et al. Emotion classification based on transformer and CNN for EEG spatial-temporal feature learning[J]. Brain Sciences, 2024, 14(3): 268.
[77] Hu F, Wang F, Bi J, et al. HASTF: A hybrid attention spatio-temporal feature fusion network for EEG emotion recognition[J]. Frontiers in Neuroscience, 2024, 18: 1479570.
[78] Fan C, Xie H, Tao J, et al. ICaps-ResLSTM: improved capsule network and residual LSTM for EEG emotion recognition[J]. Biomedical Signal Processing and Control, 2024, 87: 105422.