{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T15:19:17Z","timestamp":1783005557179,"version":"3.54.5"},"reference-count":97,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T00:00:00Z","timestamp":1782950400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Comput. Neurosci."],"abstract":"<jats:p>Cross-subject emotion recognition based on electroencephalogram (EEG) signals faces significant challenges, mainly because EEG data are highly non-stationary and easily influenced by time, environment, and individual physiological states. Meanwhile, substantial inter-subject variability leads to obvious differences in signal patterns across different people, which makes it difficult for a single model to learn stable and transferable emotional features. As a result, these factors severely hinder model generalization and reduce recognition performance in real-world applications. Unlike previous reviews that categorize methods based on network architectures, this paper proposes a novel taxonomy grounded in the \u201cgeneralization hypothesis,\u201d synthesizing existing approaches into five major paradigms: statistical and adversarial distribution alignment, topological and structural modeling, advanced representation learning, generative modeling and style reconstruction, and multimodal complementary fusion. Our analysis reveals that the core conflict lies in the trade-off between alignment intensity and semantic integrity. Future research should integrate causal representation learning with source-free domain adaptation to realize truly plug-and-play affective brain\u2013computer interfaces (aBCIs).<\/jats:p>","DOI":"10.3389\/fncom.2026.1865513","type":"journal-article","created":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T15:04:51Z","timestamp":1783004691000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Cross-subject generalization for EEG emotion recognition: a review of methods, challenges, and future trends"],"prefix":"10.3389","volume":"20","author":[{"given":"Zhengping","family":"Li","sequence":"first","affiliation":[{"name":"College of Artificial Intelligence and Computer Science, North China University of Technology","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaofeng","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence and Computer Science, North China University of Technology","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuwen","family":"Hao","sequence":"additional","affiliation":[{"name":"Disaster Medicine Research Center","place":["Medical Innovation Center of PLA General Hospital, Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lijun","family":"Wang","sequence":"additional","affiliation":[{"name":"Hangzhou Institute of Technology, Xidian University","place":["Hangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoxue","family":"Li","sequence":"additional","affiliation":[{"name":"Hangzhou Institute of Technology, Xidian University","place":["Hangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Duo","sequence":"additional","affiliation":[{"name":"China Academy of Information and Communications Technology","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,2]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"923","DOI":"10.1109\/TCDS.2025.3529669","article-title":"TRANSIT-EEG\u2014a framework for cross-subject classification with subject specific adaptation","volume":"17","author":"Ahuja","year":"2025","journal-title":"IEEE Trans. Cogn. Dev. Syst"},{"key":"B2","doi-asserted-by":"publisher","first-page":"28295","DOI":"10.1038\/s41598-025-13289-5","article-title":"Cross-subject EEG signals-based emotion recognition using contrastive learning","volume":"15","author":"Alghamdi","year":"2025","journal-title":"Sci. Rep"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP48485.2024.10446957","article-title":"\u201cCross-subject EEG emotion recognition based on interconnected dynamic domain adaptation,\u201d","author":"An","year":"2024","journal-title":"ICASSP 2024"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC48229.2022.9871743","article-title":"\u201cExploiting multiple EEG data domains with adversarial learning,\u201d","author":"Bethge","year":"2022","journal-title":"2022 44th annual international conference of the IEEE engineering in medicine &biology society (EMBC)"},{"key":"B5","doi-asserted-by":"publisher","first-page":"1716","DOI":"10.1109\/TAFFC.2025.3535542","article-title":"Multi-scale hyperbolic contrastive learning for cross-subject EEG emotion recognition","volume":"16","author":"Chang","year":"2025","journal-title":"IEEE Trans. Affect. Comput"},{"key":"B6","doi-asserted-by":"publisher","first-page":"2025","DOI":"10.1109\/JBHI.2024.3360151","article-title":"Generative listener EEG for speech emotion recognition using generative adversarial networks with compressed sensing","volume":"28","author":"Chang","year":"2024","journal-title":"IEEE J. Biomed. Health Inform"},{"key":"B7","doi-asserted-by":"publisher","first-page":"1739","DOI":"10.1109\/TAFFC.2024.3371540","article-title":"GDDN: graph domain disentanglement network for generalizable EEG emotion recognition","volume":"15","author":"Chen","year":"2024","journal-title":"IEEE Trans. Affect. Comput"},{"key":"B8","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1109\/TETCI.2024.3406422","article-title":"Comprehensive multisource learning network for cross-subject multimodal emotion recognition","volume":"9","author":"Chen","year":"2025","journal-title":"IEEE Trans. Emer. Topics Comput. Intell"},{"key":"B9","doi-asserted-by":"publisher","first-page":"3498","DOI":"10.1109\/TNSRE.2025.3603190","article-title":"A progressive multi-domain adaptation network with reinforced self-constructed graphs for cross-subject EEG-based emotion and consciousness recognition","volume":"33","author":"Chen","year":"2025","journal-title":"IEEE Trans. Neural Syst. Rehabilit. Eng"},{"key":"B10","doi-asserted-by":"publisher","first-page":"106953","DOI":"10.1016\/j.bspc.2024.106953","article-title":"MSS-JDA: multi-source self-selected joint domain adaptation method based on cross-subject EEG emotion recognition","volume":"100","author":"Chen","year":"2025","journal-title":"Biomed. Signal Process. Control"},{"key":"B11","doi-asserted-by":"publisher","first-page":"1245","DOI":"10.1109\/TCDS.2025.3545666","article-title":"Cross-subject and cross-session EEG emotion recognition based on multisource structural deep clustering","volume":"17","author":"Chen","year":"2025","journal-title":"IEEE Trans. Cogn. Dev. Syst"},{"key":"B12","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1007\/s11571-025-10272-8","article-title":"Conditional probabilistic-based domain adaptation for cross-subject EEG-based emotion recognition","volume":"19","author":"Cheng","year":"2025","journal-title":"Cogn. Neurodyn"},{"key":"B13","doi-asserted-by":"publisher","first-page":"2034","DOI":"10.3390\/s20072034","article-title":"Investigating the use of pretrained convolutional neural network on cross-subject and cross-dataset EEG emotion recognition","volume":"20","author":"Cimtay","year":"2020","journal-title":"Sensors"},{"key":"B14","doi-asserted-by":"publisher","first-page":"1592070","DOI":"10.3389\/fnins.2025.1592070","article-title":"Domain adaptive deep possibilistic clustering for EEG-based emotion recognition","volume":"19","author":"Dan","year":"2025","journal-title":"Front. Neurosci"},{"key":"B15","doi-asserted-by":"publisher","first-page":"10381","DOI":"10.1109\/TNNLS.2025.3552603","article-title":"EmT: a novel transformer for generalized cross-subject EEG emotion recognition","volume":"36","author":"Ding","year":"2025","journal-title":"IEEE Trans. Neural Netw. Learn. Syst"},{"key":"B16","doi-asserted-by":"publisher","first-page":"110136","DOI":"10.1109\/ACCESS.2023.3322294","article-title":"Cross-subject channel selection using modified relief and simplified CNN-based deep learning for EEG-based emotion recognition","volume":"11","author":"Farokhah","year":"2023","journal-title":"IEEE Access"},{"key":"B17","doi-asserted-by":"publisher","first-page":"1234162","DOI":"10.3389\/fnins.2023.1234162","article-title":"A novel feature fusion network for multimodal emotion recognition from EEG and eye movement signals","volume":"17","author":"Fu","year":"2023","journal-title":"Front. Neurosci"},{"key":"B18","doi-asserted-by":"publisher","first-page":"1970","DOI":"10.1109\/TAFFC.2024.3392791","article-title":"CiABL: completeness-induced adaptative broad learning for cross-subject emotion recognition with EEG and eye movement signals","volume":"15","author":"Gong","year":"2024","journal-title":"IEEE Trans. Affect. Comput"},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.1109\/BIBM62325.2024.10822614","article-title":"\u201cEEG-based cross subject emotion recognition based on collaborative learning and dynamic distribution adaptation,\u201d","author":"Gu","year":"2024","journal-title":"2024 IEEE international conference on bioinformatics and biomedicine (BIBM)"},{"key":"B20","doi-asserted-by":"publisher","first-page":"111635","DOI":"10.1016\/j.asoc.2024.111635","article-title":"EEG emotion recognition based on the TimesNet fusion model","volume":"159","author":"Han","year":"2024","journal-title":"Appl. Soft Comput"},{"key":"B21","doi-asserted-by":"publisher","first-page":"1316","DOI":"10.1007\/s12559-022-10016-4","article-title":"Generator-based domain adaptation method with knowledge free for cross-subject EEG emotion recognition","volume":"14","author":"Huang","year":"2022","journal-title":"Cognit. Comput"},{"key":"B22","doi-asserted-by":"publisher","first-page":"2272","DOI":"10.1145\/3664647.3681579","article-title":"\u201cCorrelation-driven multi-modality graph decomposition for cross-subject emotion recognition,\u201d","author":"Huang","year":"2024","journal-title":"Proceedings of the 32nd ACM international conference on multimedia"},{"key":"B23","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1109\/MCI.2015.2501545","article-title":"Transfer learning in brain-computer interfaces","volume":"11","author":"Jayaram","year":"2016","journal-title":"IEEE Comput. Intell. Mag"},{"key":"B24","doi-asserted-by":"publisher","first-page":"109978","DOI":"10.1016\/j.jneumeth.2023.109978","article-title":"Joint domain symmetry and predictive balance for cross-dataset EEG emotion recognition","volume":"400","author":"Jiang","year":"2023","journal-title":"J. Neurosci. Methods"},{"key":"B25","doi-asserted-by":"publisher","first-page":"969","DOI":"10.1109\/TAFFC.2024.3485057","article-title":"SEED-VII: a multimodal dataset of six basic emotions with continuous labels for emotion recognition","volume":"16","author":"Jiang","year":"2025","journal-title":"IEEE Trans. Affect. Comput"},{"key":"B26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2023.3302938","article-title":"Cross-subject EEG-based emotion recognition via semisupervised multisource joint distribution adaptation","volume":"72","author":"Jim\u00e9nez-Guarneros","year":"2023","journal-title":"IEEE Trans. Instrum. Meas"},{"key":"B27","doi-asserted-by":"publisher","first-page":"1502","DOI":"10.1109\/TAFFC.2024.3357656","article-title":"CFDA-CSF: a multi-modal domain adaptation method for cross-subject emotion recognition","volume":"15","author":"Jim\u00e9nez-Guarneros","year":"2024","journal-title":"IEEE Trans. Affect. Comput"},{"key":"B28","doi-asserted-by":"publisher","first-page":"113238","DOI":"10.1016\/j.knosys.2025.113238","article-title":"Multi-modal supervised domain adaptation with a multi-level alignment strategy and consistent decision boundaries for cross-subject emotion recognition from EEG and eye movement signals","volume":"315","author":"Jim\u00e9nez-Guarneros","year":"2025","journal-title":"Knowl. Based Syst"},{"key":"B29","doi-asserted-by":"publisher","first-page":"2214","DOI":"10.1109\/TCSS.2024.3519300","article-title":"MMDA: a multimodal and multisource domain adaptation method for cross-subject emotion recognition from EEG and eye movement signals","volume":"12","author":"Jim\u00e9nez-Guarneros","year":"","journal-title":"IEEE Trans. Comput. Soc. Syst"},{"key":"B30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2025.3551924","article-title":"Multimodal semi-supervised domain adaptation using cross-modal learning and joint distribution alignment for cross-subject emotion recognition","volume":"74","author":"Jimnez-Guarneros","year":"","journal-title":"IEEE Trans. Instrum. Meas"},{"key":"B31","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1109\/JBHI.2017.2688239","article-title":"DREAMER: a database for emotion recognition through EEG and ECG signals from wireless low-cost off-the-shelf devices","volume":"22","author":"Katsigiannis","year":"2018","journal-title":"J. Biomed. Health Inform"},{"key":"B32","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1007\/s12193-009-0025-5","article-title":"Multimodal emotion recognition in speech-based interaction using facial expression, body gesture and acoustic analysis","volume":"3","author":"Kessous","year":"2010","journal-title":"J. Multim. User Interf"},{"key":"B33","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/T-AFFC.2011.15","article-title":"DEAP: a database for emotion analysis; using physiological signals","volume":"3","author":"Koelstra","year":"2012","journal-title":"IEEE Trans. Affect. Comput"},{"key":"B34","doi-asserted-by":"publisher","first-page":"14049","DOI":"10.1109\/TNNLS.2025.3546283","article-title":"Neuron perception inspired EEG emotion recognition with parallel contrastive learning","volume":"36","author":"Li","year":"2025","journal-title":"IEEE Trans. Neural Netw. Learn. Syst"},{"key":"B35","doi-asserted-by":"publisher","first-page":"105519","DOI":"10.1016\/j.compbiomed.2022.105519","article-title":"Cross-subject EEG emotion recognition combined with connectivity features and meta-transfer learning","volume":"145","author":"Li","year":"2022","journal-title":"Comput. Biol. Med"},{"key":"B36","doi-asserted-by":"publisher","first-page":"1169949","DOI":"10.3389\/fnhum.2023.1169949","article-title":"STGATE: spatial-temporal graph attention network with a transformer encoder for EEG-based emotion recognition","volume":"17","author":"Li","year":"2023","journal-title":"Front. Hum. Neurosci"},{"key":"B37","doi-asserted-by":"publisher","first-page":"3281","DOI":"10.1109\/TCYB.2019.2904052","article-title":"Multisource transfer learning for cross-subject EEG emotion recognition","volume":"50","author":"Li","year":"2020","journal-title":"IEEE Trans. Cybern"},{"key":"B38","doi-asserted-by":"publisher","first-page":"110756","DOI":"10.1016\/j.knosys.2023.110756","article-title":"MTLFuseNet: a novel emotion recognition model based on deep latent feature fusion of EEG signals and multi-task learning","volume":"276","author":"Li","year":"2023","journal-title":"Knowl.-Based Syst"},{"key":"B39","doi-asserted-by":"publisher","first-page":"19336","DOI":"10.1109\/JSEN.2024.3390799","article-title":"Dynamic stream selection network for subject-independent EEG-based emotion recognition","volume":"24","author":"Li","year":"2024","journal-title":"IEEE Sens. J"},{"key":"B40","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN54540.2023.10192009","article-title":"\u201cSpatial-temporal constraint learning for cross-subject EEG-based emotion recognition,\u201d","author":"Li","year":"","journal-title":"2023 international joint conference on neural networks (IJCNN)"},{"key":"B41","doi-asserted-by":"publisher","first-page":"5302","DOI":"10.1109\/JBHI.2023.3311338","article-title":"MS-FRAN: a novel multi-source domain adaptation method for EEG-based emotion recognition","volume":"27","author":"Li","year":"","journal-title":"IEEE J. Biomed. Health Inform"},{"key":"B42","doi-asserted-by":"publisher","first-page":"2474","DOI":"10.1109\/TETCI.2024.3449926","article-title":"Distillation-based domain generalization for cross-dataset EEG-based emotion recognition","volume":"9","author":"Li","year":"2025","journal-title":"IEEE Trans. Emerg. Topics Comput. Intell"},{"key":"B43","doi-asserted-by":"publisher","first-page":"1451","DOI":"10.1109\/TAFFC.2024.3349770","article-title":"Gusa: graph-based unsupervised subdomain adaptation for cross-subject EEG emotion recognition","volume":"15","author":"Li","year":"2024","journal-title":"IEEE Trans. Affect. Comput"},{"key":"B44","doi-asserted-by":"publisher","first-page":"87","DOI":"10.3389\/fnins.2020.00087","article-title":"Latent factor decoding of multi-channel EEG for emotion recognition through autoencoder-like neural networks","volume":"14","author":"Li","year":"2020","journal-title":"Front. Neurosci"},{"key":"B45","doi-asserted-by":"publisher","first-page":"016046","DOI":"10.1088\/1741-2552\/acb79e","article-title":"Emotion recognition using spatial-temporal EEG features through convolutional graph attention network","volume":"20","author":"Li","year":"2023","journal-title":"J. Neural Eng"},{"key":"B46","doi-asserted-by":"publisher","first-page":"5964","DOI":"10.1109\/JBHI.2022.3210158","article-title":"Dynamic domain adaptation for class-aware cross-subject and cross-session EEG emotion recognition","volume":"26","author":"Li","year":"2022","journal-title":"IEEE J. Biomed. Health Inform"},{"key":"B47","doi-asserted-by":"publisher","first-page":"1315","DOI":"10.1109\/TAFFC.2023.3336531","article-title":"Emotion recognition from few-channel EEG signals by integrating deep feature aggregation and transfer learning","volume":"15","author":"Liu","year":"2024","journal-title":"IEEE Trans. Affect. Comput"},{"key":"B48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3674975","article-title":"Self-supervised EEG representation learning for robust emotion recognition","volume":"105","author":"Liu","year":"2024","journal-title":"ACM Trans. Sensor Netw"},{"key":"B49","doi-asserted-by":"publisher","first-page":"1297","DOI":"10.1007\/s11760-025-04819-9","article-title":"MS-AGDA: a multi-source adaptive gating unsupervised domain adaptation for cross-subject and cross-session EEG-based emotion recognition","volume":"19","author":"Liu","year":"2025","journal-title":"Signal, Image Video Proc"},{"key":"B50","doi-asserted-by":"publisher","first-page":"1419","DOI":"10.1109\/TAFFC.2024.3514635","article-title":"Enhancing EEG-based cross-subject emotion recognition via adaptive source joint domain adaptation","volume":"16","author":"Liu","year":"2025","journal-title":"IEEE Trans. Affect. Comput"},{"key":"B51","doi-asserted-by":"publisher","first-page":"026012","DOI":"10.1088\/1741-2552\/ac5c8d","article-title":"Identifying similarities and differences in emotion recognition with EEG and eye movements among Chinese, German, and French people","volume":"19","author":"Liu","year":"2022","journal-title":"J. Neural Eng"},{"key":"B52","doi-asserted-by":"publisher","first-page":"3515","DOI":"10.1109\/BIBM62325.2024.10822354","article-title":"\u201cMoGE: mixture of graph experts for cross-subject emotion recognition via decomposing EEG,\u201d","author":"Liu","year":"2024","journal-title":"2024 IEEE international conference on bioinformatics and biomedicine (BIBM)"},{"key":"B53","doi-asserted-by":"publisher","first-page":"1280241","DOI":"10.3389\/fnhum.2023.1280241","article-title":"Hybrid transfer learning strategy for cross-subject EEG emotion recognition","volume":"17","author":"Lu","year":"2023","journal-title":"Front. Hum. Neurosci"},{"key":"B54","doi-asserted-by":"publisher","first-page":"1471634","DOI":"10.3389\/fnhum.2024.1471634","article-title":"Domain adaptation spatial feature perception neural network for cross-subject EEG emotion recognition","volume":"18","author":"Lu","year":"2024","journal-title":"Front. Hum. Neurosci"},{"key":"B55","doi-asserted-by":"publisher","first-page":"986","DOI":"10.3390\/e27090986","article-title":"Cross-subject EEG emotion recognition using SSA-EMS algorithm for feature extraction","volume":"27","author":"Lu","year":"2025","journal-title":"Entropy"},{"key":"B56","doi-asserted-by":"publisher","first-page":"2387","DOI":"10.3390\/s23052387","article-title":"Online learning for wearable EEG-based emotion classification","volume":"23","author":"Moontaha","year":"2023","journal-title":"Sensors"},{"key":"B57","doi-asserted-by":"publisher","first-page":"113958","DOI":"10.1016\/j.asoc.2025.113958","article-title":"A dual-branch self-supervised contrastive learning framework for emotion recognition based on time-frequency fusion","volume":"185","author":"Ouyang","year":"2025","journal-title":"Appl. Soft Comput"},{"key":"B58","doi-asserted-by":"publisher","first-page":"777","DOI":"10.1109\/JBHI.2023.3335854","article-title":"ST-SCGNN: a spatio-temporal self-constructing graph neural network for cross-subject EEG-based emotion recognition and consciousness detection","volume":"28","author":"Pan","year":"2024","journal-title":"IEEE J. Biomed. Health Inform"},{"key":"B59","doi-asserted-by":"publisher","first-page":"104741","DOI":"10.1016\/j.bspc.2023.104741","article-title":"EEG-based cross-subject emotion recognition using multi-source domain transfer learning","volume":"84","author":"Quan","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"B60","doi-asserted-by":"publisher","first-page":"406","DOI":"10.1007\/978-981-97-8499-8_28","article-title":"\u201cCoarse-to-fine domain adaptation for cross-subject EEG emotion recognition with contrastive learning,\u201d","author":"Ran","year":"2024","journal-title":"Pattern recognition and computer vision: 7th Chinese conference, PRCV 2024, Urumqi, China, October 18\u201320, 2024, proceedings, part XV"},{"key":"B61","doi-asserted-by":"publisher","first-page":"5069","DOI":"10.1109\/TIFS.2025.3570183","article-title":"An interpretable contrastive learning transformer for EEG-based person identification","volume":"20","author":"Shao","year":"2025","journal-title":"IEEE Trans. Inform. Foren. Secur"},{"key":"B62","doi-asserted-by":"publisher","first-page":"130254","DOI":"10.1016\/j.neucom.2025.130254","article-title":"Dual filtration subdomain adaptation network for cross-subject EEG emotion recognition","volume":"639","author":"She","year":"2025","journal-title":"Neurocomputing"},{"key":"B63","doi-asserted-by":"publisher","first-page":"106860","DOI":"10.1016\/j.compbiomed.2023.106860","article-title":"Cross-subject EEG emotion recognition using multi-source domain manifold feature selection","volume":"159","author":"She","year":"","journal-title":"Comput. Biol. Med"},{"key":"B64","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2023.3277985","article-title":"Multisource associate domain adaptation for cross-subject and cross-session EEG emotion recognition","volume":"72","author":"She","year":"","journal-title":"IEEE Trans. Instrum. Meas"},{"key":"B65","doi-asserted-by":"publisher","first-page":"131749","DOI":"10.1016\/j.neucom.2025.131749","article-title":"Dynamic sparse directed graph convolutional network with attention mechanisms for EEG emotion recognition","volume":"658","author":"Shen","year":"2025","journal-title":"Neurocomputing"},{"key":"B66","doi-asserted-by":"publisher","DOI":"10.1109\/ICBME61513.2023.10488580","article-title":"\u201cEnhanced subspace alignment with clustering and weighting for cross-subject multi-session EEG-based emotion recognition,\u201d","author":"Shirkarami","year":"2023","journal-title":"2023 30th national and 8th international Iranian conference on biomedical engineering (ICBME)"},{"key":"B67","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1007\/978-3-319-49409-8_35","article-title":"\u201cDeep CORAL: correlation alignment for deep domain adaptation,\u201d","author":"Sun","year":"2016","journal-title":"Computer vision ECCV 2016 workshops"},{"key":"B68","doi-asserted-by":"publisher","first-page":"1213099","DOI":"10.3389\/fnins.2023.1213099","article-title":"Local domain generalization with low-rank constraint for EEG-based emotion recognition","volume":"17","author":"Tao","year":"2023","journal-title":"Front. Neurosci"},{"key":"B69","doi-asserted-by":"publisher","first-page":"1167","DOI":"10.28991\/ESJ-2022-06-05-017","article-title":"A systematic review on emotion recognition system using physiological signals: data acquisition and methodology","volume":"6","author":"Tawsif","year":"2022","journal-title":"Emerg. Sci. J"},{"key":"B70","doi-asserted-by":"publisher","first-page":"1371","DOI":"10.1109\/TCE.2024.3414154","article-title":"Data generation for enhancing EEG-based emotion recognition: extracting time-invariant and subject-invariant components with contrastive learning","volume":"71","author":"Wan","year":"2025","journal-title":"IEEE Trans. Consumer Electr"},{"key":"B71","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i1.27819","article-title":"\u201cDMMR: cross-subject domain generalization for EEG-based emotion recognition via denoising mixed mutual reconstruction,\u201d","author":"Wang","year":"2024","journal-title":"Proceedings of the AAAI conference on artificial intelligence"},{"key":"B72","doi-asserted-by":"publisher","first-page":"035004","DOI":"10.1088\/1361-6579\/ad2eb6","article-title":"Cerebral asymmetry representation learning-based deep subdomain adaptation network for electroencephalogram-based emotion recognition","volume":"45","author":"Wang","year":"2024","journal-title":"Physiol. Meas"},{"key":"B73","article-title":"A lightweight domain adversarial neural network based on knowledge distillation for EEG-based cross-subject emotion recognition","author":"Wang","year":"2023","journal-title":"arXiv:2305.07446"},{"key":"B74","doi-asserted-by":"publisher","first-page":"015103","DOI":"10.1063\/5.0231511","article-title":"Dynamic domain adaptive EEG emotion recognition based on multi-source selection","volume":"96","author":"Wang","year":"2025","journal-title":"Rev. Sci. Instr"},{"key":"B75","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1093\/sleep\/16.2.128","article-title":"Attention, stress and negative emotion in persistent sleep-onset and sleep-maintenance insomnia","volume":"16","author":"Waters","year":"1993","journal-title":"Sleep"},{"key":"B76","doi-asserted-by":"publisher","first-page":"7899","DOI":"10.1038\/s41598-026-39315-8","article-title":"Video-dominant emotion recognition for portable EEG-based devices","volume":"16","author":"Wen","year":"2026","journal-title":"Sci. Rep"},{"key":"B77","doi-asserted-by":"publisher","first-page":"3153","DOI":"10.1007\/s11517-025-03384-0","article-title":"Auxiliary classifier adversarial networks with maximum subdomain discrepancy for EEG-based emotion recognition","volume":"63","author":"Xiao","year":"2025","journal-title":"Med. Biol. Eng. Comput"},{"key":"B78","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1007\/s11760-025-03876-4","article-title":"DDNet: a hybrid network based on deep adaptive multi-head attention and dynamic graph convolution for EEG emotion recognition","volume":"19","author":"Xu","year":"2025","journal-title":"Signal, Image Video Proc"},{"key":"B79","doi-asserted-by":"publisher","first-page":"8485","DOI":"10.1109\/ACCESS.2024.3349552","article-title":"MASTF-net: an EEG emotion recognition network based on multi-source domain adaptive method based on spatio-temporal image and frequency domain information","volume":"12","author":"Xu","year":"2024","journal-title":"IEEE Access"},{"key":"B80","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN55064.2022.9892816","article-title":"\u201cDeep spatio-temporal mutual learning for EEG emotion recognition,\u201d","author":"Ye","year":"2022","journal-title":"2022 international joint conference on neural networks (IJCNN)"},{"key":"B81","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1109\/TAFFC.2024.3433470","article-title":"Semi-supervised dual-stream self-attentive adversarial graph contrastive learning for cross-subject EEG-based emotion recognition","volume":"16","author":"Ye","year":"2025","journal-title":"IEEE Trans. Affect. Comput"},{"key":"B82","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1007\/978-3-030-92310-5_24","author":"Zhang","year":"2021","journal-title":"Neural Information Processing"},{"key":"B83","doi-asserted-by":"publisher","first-page":"110901","DOI":"10.1016\/j.brainresbull.2024.110901","article-title":"MGFKD: a semi-supervised multi-source domain adaptation algorithm for cross-subject EEG emotion recognition","volume":"208","author":"Zhang","year":"2024","journal-title":"Brain Res. Bull"},{"key":"B84","doi-asserted-by":"publisher","first-page":"107614","DOI":"10.1016\/j.neunet.2025.107614","article-title":"SASD-MCL: semi-supervised alignment self-distillation with mixed contrastive learning for cross-subject EEG emotion recognition","volume":"190","author":"Zhang","year":"2025","journal-title":"Neural Netw"},{"key":"B85","doi-asserted-by":"publisher","first-page":"046060","DOI":"10.1088\/1741-2552\/ad7060","article-title":"Emotion recognition of EEG signals based on contrastive learning graph convolutional model","volume":"21","author":"Zhang","year":"2024","journal-title":"J. Neural Eng"},{"key":"B86","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1109\/TAMD.2015.2431497","article-title":"investigating critical frequency bands channels for EEG-based emotion recognition with deep neural networks","volume":"7","author":"Zheng","year":"2015","journal-title":"IEEE Trans. Auton. Ment. Dev"},{"key":"B87","doi-asserted-by":"publisher","first-page":"1290","DOI":"10.1109\/TAFFC.2020.2994159","article-title":"EEG-based emotion recognition using regularized graph neural networks","volume":"13","author":"Zhong","year":"2022","journal-title":"IEEE Trans. Affect. Comput"},{"key":"B88","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2025.3553234","article-title":"Unsupervised domain adaptation with pseudo-label propagation for cross-domain EEG emotion recognition","volume":"74","author":"Zhong","year":"2025","journal-title":"IEEE Trans. Instrum. Meas"},{"key":"B89","doi-asserted-by":"publisher","first-page":"114318","DOI":"10.1016\/j.knosys.2025.114318","article-title":"ProDG: a proxy-domain-guiding strategy for multi-source-free domain adaptation in EEG emotion recognition","volume":"329","author":"Zhou","year":"2025","journal-title":"Knowl. Based Syst"},{"key":"B90","doi-asserted-by":"publisher","first-page":"12991","DOI":"10.1109\/TNNLS.2024.3493425","article-title":"EEGMatch: learning with incomplete labels for semisupervised EEG-based cross-subject emotion recognition","volume":"36","author":"Zhou","year":"2025","journal-title":"IEEE Trans. Neural Netw. Learn. Syst"},{"key":"B91","doi-asserted-by":"publisher","first-page":"2157","DOI":"10.1109\/TAFFC.2025.3555439","article-title":"Enhancing cross-dataset eeg emotion recognition: a novel approach with emotional EEG style transfer network","volume":"16","author":"Zhou","year":"2025","journal-title":"IEEE Trans. Affect. Comput"},{"key":"B92","doi-asserted-by":"publisher","first-page":"908","DOI":"10.1080\/10255842.2024.2417212","article-title":"Multi-source domain transfer network based on subdomain adaptation and minimum class confusion for EEG emotion recognition","volume":"29","author":"Zhu","year":"2026","journal-title":"Comput. Methods Biomech. Biomed. Engin"},{"key":"B93","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1007\/s11517-023-02956-2","article-title":"Instance-representation transfer method based on joint distribution and deep adaptation for EEG emotion recognition","volume":"62","author":"Zhu","year":"2024","journal-title":"Med. Biol. Eng. Comput"},{"key":"B94","doi-asserted-by":"publisher","first-page":"108372","DOI":"10.1016\/j.bspc.2025.108372","article-title":"A contrastive learning model of attentional enhancement for EEG cross-subject emotion recognition","volume":"112","author":"Zhu","year":"2026","journal-title":"Biomed. Signal Process. Control"},{"key":"B95","doi-asserted-by":"publisher","first-page":"2102","DOI":"10.1109\/TAFFC.2025.3554399","article-title":"Multi-modal cross-subject emotion feature alignment and recognition with EEG and eye movements","volume":"16","author":"Zhu","year":"2025","journal-title":"IEEE Trans. Affect. Comput"},{"key":"B96","doi-asserted-by":"publisher","first-page":"2129","DOI":"10.32604\/cmes.2023.028732","article-title":"Brain functional network generation using distribution-regularized adversarial graph autoencoder with transformer for dementia diagnosis","volume":"137","author":"Zuo","year":"2023","journal-title":"Comput. Model. Eng. Sci"},{"key":"B97","doi-asserted-by":"publisher","first-page":"109898","DOI":"10.1016\/j.compbiomed.2025.109898","article-title":"Brain imaging-to-graph generation using adversarial hierarchical diffusion models for MCI causality analysis","volume":"189","author":"Zuo","year":"2025","journal-title":"Comput. Biol. Med"}],"container-title":["Frontiers in Computational Neuroscience"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fncom.2026.1865513\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T15:04:52Z","timestamp":1783004692000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fncom.2026.1865513\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,2]]},"references-count":97,"alternative-id":["10.3389\/fncom.2026.1865513"],"URL":"https:\/\/doi.org\/10.3389\/fncom.2026.1865513","relation":{},"ISSN":["1662-5188"],"issn-type":[{"value":"1662-5188","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,2]]},"article-number":"1865513"}}