{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T05:16:06Z","timestamp":1778822166272,"version":"3.51.4"},"reference-count":79,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100012579","name":"Natural Science Foundation of Qinghai","doi-asserted-by":"publisher","award":["2022-ZJ-925"],"award-info":[{"award-number":["2022-ZJ-925"]}],"id":[{"id":"10.13039\/501100012579","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008081","name":"Southeast University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100008081","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62066039"],"award-info":[{"award-number":["62066039"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["D20035"],"award-info":[{"award-number":["D20035"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Applied Soft Computing"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1016\/j.asoc.2026.114855","type":"journal-article","created":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T08:08:19Z","timestamp":1771315699000},"page":"114855","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["MGGL: A multi-grained graph learning model for EEG emotion recognition"],"prefix":"10.1016","volume":"193","author":[{"given":"Mingjie","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heming","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.asoc.2026.114855_bib0005","series-title":"2015 11th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","first-page":"42","article-title":"Applying non linear approach for ECG denoising and waves localization","author":"Beya","year":"2015"},{"key":"10.1016\/j.asoc.2026.114855_bib0010","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.110060","article-title":"Sparse temporal aware capsule network for robust speech emotion recognition","volume":"144","author":"Zhang","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.asoc.2026.114855_bib0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.111241","article-title":"Driver multi-task emotion recognition network based on multi-modal facial video analysis","volume":"161","author":"Xiang","year":"2025","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.asoc.2026.114855_bib0020","series-title":"International Conference on Bio-Inspired Systems and Signal Processing","first-page":"156","article-title":"Electrocardiogram signal analysing-delineation and localization of ECG component","volume":"vol. 5","author":"Beya","year":"2016"},{"key":"10.1016\/j.asoc.2026.114855_bib0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.107012","article-title":"Evaluating the effectiveness of machine learning in identifying the optimal facial electromyography location for emotion detection","volume":"100","author":"Barigala","year":"2025","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.asoc.2026.114855_bib0030","series-title":"2024 IEEE 22nd International Conference on Industrial Informatics (INDIN)","first-page":"1","article-title":"Stacked transformer models for enhanced wind speed prediction in the red sea","author":"Hittawe","year":"2024"},{"issue":"6","key":"10.1016\/j.asoc.2026.114855_bib0035","doi-asserted-by":"crossref","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":"10.1016\/j.asoc.2026.114855_bib0040","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2024.112631","article-title":"CNN-transformer network for student learning effect prediction using EEG signals based on spatio-temporal feature fusion","volume":"170","author":"Xie","year":"2025","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.asoc.2026.114855_bib0045","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.128183","article-title":"A novel biologically plausible spiking convolutional capsule network with optimized batch normalization for EEG-based emotion recognition","volume":"288","author":"Chen","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.asoc.2026.114855_bib0050","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2025.130102","article-title":"FCAnet: a novel feature fusion approach to EEG emotion recognition based on cross-attention networks","volume":"638","author":"Li","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.asoc.2026.114855_bib0055","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1109\/TNSRE.2024.3355750","article-title":"Graph neural network-based EEG classification: a survey","volume":"32","author":"Klepl","year":"2024","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"10.1016\/j.asoc.2026.114855_bib0060","series-title":"Road Traffic Modeling and Management: Using Statistical Monitoring and Deep Learning","author":"Harrou","year":"2021"},{"issue":"10","key":"10.1016\/j.asoc.2026.114855_bib0065","doi-asserted-by":"crossref","first-page":"5917","DOI":"10.1109\/JBHI.2024.3416944","article-title":"Graph convolutional network with connectivity uncertainty for EEG-based emotion recognition","volume":"28","author":"Gao","year":"2024","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"4","key":"10.1016\/j.asoc.2026.114855_bib0070","doi-asserted-by":"crossref","first-page":"7130","DOI":"10.1109\/TNNLS.2024.3405663","article-title":"An efficient graph learning system for emotion recognition inspired by the cognitive prior graph of EEG brain network","volume":"36","author":"Li","year":"2024","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.asoc.2026.114855_bib0075","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.106177","article-title":"A unified GCNN model for predicting CYP450 inhibitors by using graph convolutional neural networks with attention mechanism","volume":"150","author":"Qiu","year":"2022","journal-title":"Comput. Biol. Med."},{"issue":"3","key":"10.1016\/j.asoc.2026.114855_bib0080","doi-asserted-by":"crossref","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."},{"issue":"8","key":"10.1016\/j.asoc.2026.114855_bib0085","doi-asserted-by":"crossref","first-page":"3841","DOI":"10.1109\/TKDE.2020.3033829","article-title":"K-core based temporal graph convolutional network for dynamic graphs","volume":"34","author":"Liu","year":"2020","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.asoc.2026.114855_bib0090","doi-asserted-by":"crossref","first-page":"76","DOI":"10.70470\/EDRAAK\/2025\/010","article-title":"Applications of geospatial AI in human geography and spatial networks: a literature review","volume":"2025","author":"Saleh","year":"2025","journal-title":"EDRAAK"},{"key":"10.1016\/j.asoc.2026.114855_bib0095","doi-asserted-by":"crossref","first-page":"42","DOI":"10.58496\/BJML\/2023\/008","article-title":"Overview of neural networks","volume":"2023","author":"Mijwel","year":"2023","journal-title":"Babyl. J. Mach. Learn."},{"key":"10.1016\/j.asoc.2026.114855_bib0100","doi-asserted-by":"crossref","first-page":"88","DOI":"10.58496\/BJM\/2025\/009","article-title":"Graph-theoretic characterizations of quasi-idempotents in full order-preserving transformation semigroup","volume":"2025","author":"Eze","year":"2025","journal-title":"Babyl. J. Math."},{"key":"10.1016\/j.asoc.2026.114855_bib0105","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112599","article-title":"MAS-DGAT-Net: a dynamic graph attention network with multibranch feature extraction and staged fusion for EEG emotion recognition","volume":"305","author":"Liu","year":"2024","journal-title":"Knowl.-Based Syst."},{"issue":"4","key":"10.1016\/j.asoc.2026.114855_bib0110","doi-asserted-by":"crossref","first-page":"2042","DOI":"10.1109\/TAFFC.2024.3394873","article-title":"Bridge graph attention based graph convolution network with multi-scale transformer for EEG emotion recognition","volume":"15","author":"Yan","year":"2024","journal-title":"IEEE Trans. Affect. Comput."},{"key":"10.1016\/j.asoc.2026.114855_bib0115","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.113096","article-title":"ALGGNet: an adaptive local-global-graph representation network for brain-computer interfaces","volume":"311","author":"Wang","year":"2025","journal-title":"Knowl.-Based Syst."},{"issue":"3","key":"10.1016\/j.asoc.2026.114855_bib0120","doi-asserted-by":"crossref","first-page":"2512","DOI":"10.1109\/TAFFC.2022.3170428","article-title":"GMSS: graph-based multi-task self-supervised learning for EEG emotion recognition","volume":"14","author":"Li","year":"2023","journal-title":"IEEE Trans. Affect. Comput."},{"key":"10.1016\/j.asoc.2026.114855_bib0125","series-title":"2024 IEEE International Symposium on Biomedical Imaging (ISBI)","first-page":"1","article-title":"A self-adaptive subgraph generation algorithm for EEG channel selection","author":"Zhao","year":"2024"},{"issue":"45","key":"10.1016\/j.asoc.2026.114855_bib0130","first-page":"1","article-title":"Multi-view self-supervised domain adaptation for EEG-based emotion recognition","volume":"126","author":"Zhang","year":"2025","journal-title":"IEEE Trans. Affect. Comput."},{"key":"10.1016\/j.asoc.2026.114855_bib0135","doi-asserted-by":"crossref","DOI":"10.1016\/j.jneumeth.2024.110358","article-title":"Emotion recognition based on EEG source signals and dynamic brain function network","volume":"415","author":"Sun","year":"2025","journal-title":"J. Neurosci. Methods"},{"issue":"4","key":"10.1016\/j.asoc.2026.114855_bib0140","doi-asserted-by":"crossref","first-page":"1494","DOI":"10.1109\/TCDS.2024.3370635","article-title":"Brain connectivity analysis for EEG-based face perception task","volume":"16","author":"Chakladar das","year":"2024","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"issue":"4","key":"10.1016\/j.asoc.2026.114855_bib0145","doi-asserted-by":"crossref","first-page":"1656","DOI":"10.1109\/TCDS.2023.3270170","article-title":"EEG-based emotion recognition using trainable adjacency relation driven graph convolutional network","volume":"15","author":"Li","year":"2023","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"issue":"12","key":"10.1016\/j.asoc.2026.114855_bib0150","doi-asserted-by":"crossref","first-page":"18565","DOI":"10.1109\/TNNLS.2023.3319315","article-title":"Hybrid network using dynamic graph convolution and temporal self-attention for EEG-based emotion recognition","volume":"35","author":"Cheng","year":"2023","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"11","key":"10.1016\/j.asoc.2026.114855_bib0155","doi-asserted-by":"crossref","first-page":"6486","DOI":"10.1109\/JBHI.2024.3443651","article-title":"GMAEEG: a self-supervised graph masked autoencoder for EEG representation learning","volume":"28","author":"Fu","year":"2024","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.asoc.2026.114855_bib0160","series-title":"ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","first-page":"1","article-title":"EEG correlation analysis-guided graph local enhanced feature learning for emotion recognition","author":"Li","year":"2025"},{"key":"10.1016\/j.asoc.2026.114855_bib0165","series-title":"ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","first-page":"12981","article-title":"Cross-subject EEG emotion recognition based on interconnected dynamic domain adaptation","author":"An","year":"2024"},{"issue":"1","key":"10.1016\/j.asoc.2026.114855_bib0170","first-page":"250","article-title":"Semi-supervised dual-stream self-attentive adversarial graph contrastive learning for cross-subject EEG-based emotion recognition","volume":"16","author":"Ye","year":"2024","journal-title":"IEEE Trans. Affect. Comput."},{"issue":"3","key":"10.1016\/j.asoc.2026.114855_bib0175","doi-asserted-by":"crossref","first-page":"1090","DOI":"10.1109\/TAFFC.2023.3319397","article-title":"Unsupervised time-aware sampling network with deep reinforcement learning for EEG-based emotion recognition","volume":"15","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Affect. Comput."},{"key":"10.1016\/j.asoc.2026.114855_bib0180","series-title":"2025 39th Annual AAAI Conference on Artificial Intelligence(AAAI)","article-title":"EEG-SCMM: soft contrastive masked modeling for cross-corpus EEG-based emotion recognition","author":"Liu","year":"2024"},{"issue":"10","key":"10.1016\/j.asoc.2026.114855_bib0185","doi-asserted-by":"crossref","first-page":"17664","DOI":"10.1109\/TNNLS.2025.3581991","article-title":"DMAE-EEG: a pretraining framework for EEG spatiotemporal representation learning","volume":"36","author":"Zhang","year":"2025","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.asoc.2026.114855_bib0190","doi-asserted-by":"crossref","first-page":"103","DOI":"10.70470\/KHWARIZMIA\/2023\/010","article-title":"Efficient hardware acceleration techniques for deep learning on edge devices: a comprehensive performance analysis","volume":"2023","author":"Burhanuddin","year":"2023","journal-title":"Khwarizmia"},{"key":"10.1016\/j.asoc.2026.114855_bib0195","doi-asserted-by":"crossref","first-page":"14","DOI":"10.58496\/MJBD\/2021\/003","article-title":"Parallel generalized hebbian algorithm for large scale data analytics","volume":"2021","author":"Yaseen","year":"2021","journal-title":"Mesopotam. J. Big Data"},{"issue":"7","key":"10.1016\/j.asoc.2026.114855_bib0200","doi-asserted-by":"crossref","first-page":"9773","DOI":"10.1109\/TNNLS.2023.3236635","article-title":"LGGNet: learning from local-global-graph representations for brain\u2013computer interface","volume":"35","author":"Ding","year":"2023","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.asoc.2026.114855_bib0205","doi-asserted-by":"crossref","first-page":"3245","DOI":"10.1109\/TNSRE.2023.3304660","article-title":"MSFR-GCN: a multi-scale feature reconstruction graph convolutional network for EEG emotion and cognition recognition","volume":"31","author":"Pan","year":"2023","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"issue":"5","key":"10.1016\/j.asoc.2026.114855_bib0210","doi-asserted-by":"crossref","first-page":"516","DOI":"10.3390\/brainsci14050516","article-title":"TSANN-TG: temporal\u2013spatial attention neural networks with task-specific graph for EEG emotion recognition","volume":"14","author":"Jiang","year":"2024","journal-title":"Brain Sci."},{"key":"10.1016\/j.asoc.2026.114855_bib0215","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2024.107060","article-title":"Emotion recognition using multi-scale EEG features through graph convolutional attention network","volume":"184","author":"Cao","year":"2025","journal-title":"Neural Netw."},{"issue":"3","key":"10.1016\/j.asoc.2026.114855_bib0220","doi-asserted-by":"crossref","first-page":"1716","DOI":"10.1109\/TAFFC.2025.3535542","article-title":"Multi-scale hyperbolic hontrastive learning for cross-subject EEG emotion recognition","volume":"16","author":"Chang","year":"2025","journal-title":"IEEE Trans. Affect. Comput."},{"key":"10.1016\/j.asoc.2026.114855_bib0225","doi-asserted-by":"crossref","DOI":"10.1109\/TIM.2025.3533618","article-title":"Contrastive learning of EEG representation of brain area for emotion recognition","volume":"74","author":"Dai","year":"2025","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.asoc.2026.114855_bib0230","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.127095","article-title":"A group decision making model with non-reciprocal fuzzy preference relations based on pearson correlation coefficient","volume":"276","author":"Mo","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.asoc.2026.114855_bib0235","doi-asserted-by":"crossref","first-page":"159","DOI":"10.58496\/MJCSC\/2025\/009","article-title":"ANILA: adaptive neuro-inspired learning algorithm for efficient machine learning, AI optimization, and healthcare enhancement","volume":"2025","author":"Khaleel","year":"2025","journal-title":"Mesopotam. J. Comput. Sci."},{"key":"10.1016\/j.asoc.2026.114855_bib0240","doi-asserted-by":"crossref","first-page":"44","DOI":"10.58496\/BJM\/2025\/006","article-title":"Existence and uniqueness theorem of multi-dimensional integro-differential equations with fractional differointegrations","volume":"2025","author":"Mohammed","year":"2025","journal-title":"Babyl. J. Math."},{"key":"10.1016\/j.asoc.2026.114855_bib0245","doi-asserted-by":"crossref","first-page":"78","DOI":"10.58496\/BJM\/2023\/015","article-title":"Reliability allocation in complex systems using fuzzy logic modules","volume":"2023","author":"Abed","year":"2023","journal-title":"Babyl. J. Math."},{"issue":"3","key":"10.1016\/j.asoc.2026.114855_bib0250","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1109\/TAMD.2015.2431497","article-title":"Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks","volume":"7","author":"Zheng","year":"2015","journal-title":"IEEE Trans. Auton. Ment. Dev."},{"issue":"3","key":"10.1016\/j.asoc.2026.114855_bib0255","doi-asserted-by":"crossref","first-page":"1110","DOI":"10.1109\/TCYB.2018.2797176","article-title":"EmotionMeter: a multimodal framework for recognizing human emotions","volume":"49","author":"Zheng","year":"2018","journal-title":"IEEE Trans. Cybern."},{"issue":"1","key":"10.1016\/j.asoc.2026.114855_bib0260","doi-asserted-by":"crossref","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":"2011","journal-title":"IEEE Trans. Affect. Comput."},{"key":"10.1016\/j.asoc.2026.114855_bib0265","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.nicl.2013.07.013","article-title":"Dopaminergic therapy in parkinson\u2019s disease decreases cortical beta band coherence in the resting state and increases cortical beta band power during executive control","volume":"3","author":"George","year":"2013","journal-title":"NeuroImage: Clinical"},{"issue":"1","key":"10.1016\/j.asoc.2026.114855_bib0270","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1007\/s13246-025-01521-5","article-title":"Significance of gender, brain region and EEG band complexity analysis for parkinson\u2019s disease classification using recurrence plots and machine learning algorithms","volume":"48","author":"Sasidharan","year":"2025","journal-title":"Phys. Eng. Sci. Med."},{"key":"10.1016\/j.asoc.2026.114855_bib0275","series-title":"2016 IEEE International Conference on Image Processing (ICIP)","first-page":"2485","article-title":"Fast earth mover\u2019s distance computation for catadioptric image sequences","author":"Tahri","year":"2016"},{"key":"10.1016\/j.asoc.2026.114855_bib0280","series-title":"2015 11th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","first-page":"155","article-title":"Automatic detection and tracking of animal sperm cells in microscopy images","author":"Beya","year":"2015"},{"key":"10.1016\/j.asoc.2026.114855_bib0285","series-title":"Neural Information Processing: 25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13\u201316, 2018, Proceedings, Part v 25","first-page":"403","article-title":"Cross-subject emotion recognition using deep adaptation networks","author":"Li","year":"2018"},{"key":"10.1016\/j.asoc.2026.114855_bib0290","first-page":"1","article-title":"Neuron perception inspired EEG emotion recognition with parallel contrastive learning","volume":"1","author":"Li","year":"2025","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"21","key":"10.1016\/j.asoc.2026.114855_bib0295","first-page":"1","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."},{"issue":"2","key":"10.1016\/j.asoc.2026.114855_bib0300","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1109\/TCSS.2024.3488201","article-title":"Ugan: uncertainty-guided graph augmentation network for EEG emotion recognition","volume":"12","author":"Chen","year":"2025","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"10.1016\/j.asoc.2026.114855_bib0305","first-page":"1","article-title":"Transit-EEG: a framework for cross-subject classification with subject specific adaptation","volume":"10","author":"Ahuja","year":"2025","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"issue":"1","key":"10.1016\/j.asoc.2026.114855_bib0310","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-025-98623-7","article-title":"SS-EMERGE: self-supervised enhancement for multidimension emotion recognition using GNNs for EEG","volume":"15","author":"Ahuja","year":"2025","journal-title":"Sci. Rep."},{"key":"10.1016\/j.asoc.2026.114855_bib0315","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2024.129315","article-title":"SEDA-EEG: a semi-supervised emotion recognition network with domain adaptation for cross-subject EEG analysis","volume":"622","author":"Tan","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.asoc.2026.114855_bib0320","first-page":"1011","article-title":"DISD-Net: a dynamic interactive network with self-distillation for cross-subject multi-modal emotion recognition","volume":"14","author":"Cheng","year":"2025","journal-title":"IEEE Trans. Multimedia"},{"issue":"36","key":"10.1016\/j.asoc.2026.114855_bib0325","first-page":"1440","article-title":"A multimodal-driven fusion data augmentation framework for emotion recognition","volume":"18","author":"Li","year":"2025","journal-title":"IEEE Trans. Artif. Intell."},{"key":"10.1016\/j.asoc.2026.114855_bib0330","doi-asserted-by":"crossref","unstructured":"Z. Zhou, L. Zhang, Q. Liu, G. Huang, Z. Yu, Z. Liang, Emotion agent: unsupervised deep reinforcement learning with distribution-prototype reward for continuous emotional EEG analysis, Neurocomputing 652 (2025) 130951.","DOI":"10.1016\/j.neucom.2025.130951"},{"key":"10.1016\/j.asoc.2026.114855_bib0335","first-page":"1","article-title":"Contrastive learning of EEG representation of brain area for emotion recognition","volume":"74","author":"Dai","year":"2025","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"11","key":"10.1016\/j.asoc.2026.114855_bib0340","doi-asserted-by":"crossref","first-page":"3005","DOI":"10.1109\/TAI.2025.3560593","article-title":"EEG emotion recognition based on an implicit emotion regulatory mechanism","volume":"6","author":"Li","year":"2025","journal-title":"IEEE Trans. Artif. Intell."},{"key":"10.1016\/j.asoc.2026.114855_bib0345","doi-asserted-by":"crossref","first-page":"9070","DOI":"10.1109\/TMM.2024.3385676","article-title":"PGCN: pyramidal graph convolutional network for EEG emotion recognition","volume":"26","author":"Jin","year":"2024","journal-title":"IEEE Trans. Multimedia"},{"issue":"6","key":"10.1016\/j.asoc.2026.114855_bib0350","doi-asserted-by":"crossref","first-page":"1915","DOI":"10.1109\/TCDS.2024.3391131","article-title":"Minimizing EEG human interference: a study of an adaptive EEG spatial feature extraction with deep convolutional neural networks","volume":"16","author":"Deng","year":"2024","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"10.1016\/j.asoc.2026.114855_bib0355","first-page":"1","article-title":"STRFLNet: spatio-temporal representation fusion learning network for EEG-based emotion recognition","volume":"1","author":"Hu","year":"2025","journal-title":"IEEE Trans. Affect. Comput."},{"key":"10.1016\/j.asoc.2026.114855_bib0360","first-page":"1","article-title":"MECAM: a novel multi-axis EEG channel attention model for emotion recognition","volume":"73","author":"Hou","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"3","key":"10.1016\/j.asoc.2026.114855_bib0365","doi-asserted-by":"crossref","first-page":"2466","DOI":"10.1109\/TAFFC.2025.3564272","article-title":"Exploiting the intrinsic neighborhood semantic structure for domain adaptation in EEG-based emotion recognition","volume":"16","author":"Yang","year":"2025","journal-title":"IEEE Trans. Affect. Comput."},{"key":"10.1016\/j.asoc.2026.114855_bib0370","series-title":"ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","first-page":"1871","article-title":"CEMOAE: a dynamic autoencoder with masked channel modeling for robust EEG-based emotion recognition","author":"Lan","year":"2024"},{"key":"10.1016\/j.asoc.2026.114855_bib0375","series-title":"2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","first-page":"1954","article-title":"Addressing temporal and auditory factors in meditative EEG with self-supervised learning","author":"Gou","year":"2024"},{"issue":"4","key":"10.1016\/j.asoc.2026.114855_bib0380","doi-asserted-by":"crossref","first-page":"1673","DOI":"10.1109\/TCDS.2022.3147839","article-title":"Residual GCB-Net: residual graph convolutional broad network on emotion recognition","volume":"15","author":"Li","year":"2024","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"issue":"12","key":"10.1016\/j.asoc.2026.114855_bib0385","doi-asserted-by":"crossref","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":"2024","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.asoc.2026.114855_bib0390","article-title":"Multi-source domain adaptation with spatio-temporal feature extractor for EEG emotion recognition","volume":"84","author":"Guo","year":"2024","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.asoc.2026.114855_bib0395","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.126028","article-title":"Domain adversarial learning with multiple adversarial tasks for EEG emotion recognition","volume":"266","author":"Ju","year":"2025","journal-title":"Expert Syst. Appl."}],"container-title":["Applied Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1568494626003030?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1568494626003030?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T05:05:11Z","timestamp":1778821511000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1568494626003030"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":79,"alternative-id":["S1568494626003030"],"URL":"https:\/\/doi.org\/10.1016\/j.asoc.2026.114855","relation":{},"ISSN":["1568-4946"],"issn-type":[{"value":"1568-4946","type":"print"}],"subject":[],"published":{"date-parts":[[2026,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"MGGL: A multi-grained graph learning model for EEG emotion recognition","name":"articletitle","label":"Article Title"},{"value":"Applied Soft Computing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.asoc.2026.114855","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"114855"}}