{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T17:13:13Z","timestamp":1764781993439,"version":"3.46.0"},"reference-count":89,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2025,8,28]],"date-time":"2025-08-28T00:00:00Z","timestamp":1756339200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,28]],"date-time":"2025-08-28T00:00:00Z","timestamp":1756339200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1007\/s11517-025-03430-x","type":"journal-article","created":{"date-parts":[[2025,8,28]],"date-time":"2025-08-28T09:36:40Z","timestamp":1756373800000},"page":"3873-3893","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["IF-MMCL: an individual focused network with multi-view and multi-modal contrastive learning for cross-subject emotion recognition"],"prefix":"10.1007","volume":"63","author":[{"given":"Qiaoli","family":"Zhou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiawen","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiang","family":"Du","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Ke","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,8,28]]},"reference":[{"key":"3430_CR1","doi-asserted-by":"crossref","unstructured":"Huang Y, Wen H, Qing L, Jin R, Xiao L (2021) Emotion recognition based on body and context fusion in the wild. In 2021 IEEE\/CVF international conference on computer vision workshops (ICCVW), pp 3602\u20133610","DOI":"10.1109\/ICCVW54120.2021.00403"},{"key":"3430_CR2","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1007\/s13042-021-01413-6","volume":"13","author":"C Zhou","year":"2021","unstructured":"Zhou C, Zhi R (2021) Learning deep representation for action unit detection with auxiliary facial attributes. Int J Mach Learn Cybern 13:407\u2013419","journal-title":"Int J Mach Learn Cybern"},{"issue":"7","key":"3430_CR3","doi-asserted-by":"publisher","first-page":"6232","DOI":"10.1109\/TCYB.2021.3050508","volume":"52","author":"T Zhang","year":"2022","unstructured":"Zhang T, Gong X, Chen CLP (2022) Bmt-net: broad multitask transformer network for sentiment analysis. IEEE Trans Cybern 52(7):6232\u20136243","journal-title":"IEEE Trans Cybern"},{"key":"3430_CR4","doi-asserted-by":"crossref","unstructured":"Yan R, Yan Y, Qiu D (2021) Emotion-enhanced classification based on fuzzy reasoning. Int J Mach Learn Cybern 13:839\u2013850","DOI":"10.1007\/s13042-021-01356-y"},{"key":"3430_CR5","doi-asserted-by":"publisher","first-page":"553","DOI":"10.1007\/s13042-019-00995-6","volume":"11","author":"G Khan","year":"2019","unstructured":"Khan G, Samyan S, Khan MUG, Shahid M, Wahla SQ (2019) A survey on analysis of human faces and facial expressions datasets. Int J Mach Learn Cybern 11:553\u2013571","journal-title":"Int J Mach Learn Cybern"},{"key":"3430_CR6","doi-asserted-by":"publisher","first-page":"779","DOI":"10.1007\/s13042-019-01024-2","volume":"11","author":"X Jin","year":"2019","unstructured":"Jin X, Sun W, Jin Z (2019) A discriminative deep association learning for facial expression recognition. Int J Mach Learn Cybern 11:779\u2013793","journal-title":"Int J Mach Learn Cybern"},{"key":"3430_CR7","unstructured":"Scherer KR, B\u00e4nziger T (2010) On the use of actor portrayals in research on emotional expression"},{"key":"3430_CR8","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1177\/1754073913512003","volume":"6","author":"RW Levenson","year":"2014","unstructured":"Levenson RW (2014) The autonomic nervous system and emotion. Emotion Rev 6:100\u2013112","journal-title":"Emotion Rev"},{"issue":"2","key":"3430_CR9","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1109\/TCSS.2022.3157522","volume":"9","author":"H Bin","year":"2022","unstructured":"Bin H, Shen J, Zhu L, Dong Q, Cai H, Qian K (2022) Fundamentals of computational psychophysiology: theory and methodology. IEEE Trans Comput Soc Syst 9(2):349\u2013355","journal-title":"IEEE Trans Comput Soc Syst"},{"issue":"3","key":"3430_CR10","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1109\/TAFFC.2018.2817622","volume":"11","author":"T Song","year":"2020","unstructured":"Song T, Zheng W, Song P, Cui Z (2020) EEG emotion recognition using dynamical graph convolutional neural networks. IEEE Trans Affect Comput 11(3):532\u2013541","journal-title":"IEEE Trans Affect Comput"},{"issue":"1","key":"3430_CR11","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1109\/TAFFC.2019.2937768","volume":"13","author":"T Zhang","year":"2022","unstructured":"Zhang T, Wang X, Xu X, Chen CLP (2022) Gcb-net: graph convolutional broad network and its application in emotion recognition. IEEE Trans Affect Comput 13(1):379\u2013388","journal-title":"IEEE Trans Affect Comput"},{"key":"3430_CR12","doi-asserted-by":"publisher","first-page":"1539","DOI":"10.3758\/s13428-016-0813-2","volume":"49","author":"A Dawel","year":"2017","unstructured":"Dawel A, Wright L, Irons JL, Dumbleton R, Palermo R, O\u2019Kearney R, McKone E (2017) Perceived emotion genuineness: normative ratings for popular facial expression stimuli and the development of perceived-as-genuine and perceived-as-fake sets. Behav Res Methods 49:1539\u20131562","journal-title":"Behav Res Methods"},{"key":"3430_CR13","doi-asserted-by":"publisher","first-page":"1288","DOI":"10.1109\/TNSRE.2022.3175464","volume":"30","author":"Y Peng","year":"2022","unstructured":"Peng Y, Jin F, Kong W, Nie F, Bao-Liang L, Cichocki A (2022) Ogssl: a semi-supervised classification model coupled with optimal graph learning for eeg emotion recognition. IEEE Trans Neural Syst Rehab Eng 30:1288\u20131297","journal-title":"IEEE Trans Neural Syst Rehab Eng"},{"issue":"3","key":"3430_CR14","doi-asserted-by":"publisher","first-page":"452","DOI":"10.1080\/02699931.2013.837378","volume":"28","author":"L Lambrecht","year":"2014","unstructured":"Lambrecht L, Kreifelts B, Wildgruber D (2014) Gender differences in emotion recognition: impact of sensory modality and emotional category. Cognit Emotion 28(3):452\u2013469","journal-title":"Cognit Emotion"},{"key":"3430_CR15","doi-asserted-by":"crossref","unstructured":"Tang D, Zeng J, Li M (2018) An end-to-end deep learning framework for speech emotion recognition of atypical individuals","DOI":"10.21437\/Interspeech.2018-2581"},{"key":"3430_CR16","doi-asserted-by":"crossref","unstructured":"Dresvyanskiy D, Ryumina E, Kaya H, Markitantov M, Karpov A, Minker W (2022) End-to-end modeling and transfer learning for audiovisual emotion recognition in-the-wild. Multimodal Technol Inter, 6(2)","DOI":"10.3390\/mti6020011"},{"issue":"2","key":"3430_CR17","doi-asserted-by":"publisher","first-page":"985","DOI":"10.1109\/TAFFC.2020.2981610","volume":"13","author":"R Harper","year":"2022","unstructured":"Harper R, Southern J (2022) A bayesian deep learning framework for end-to-end prediction of emotion from heartbeat. IEEE Trans Affect Comput 13(2):985\u2013991","journal-title":"IEEE Trans Affect Comput"},{"key":"3430_CR18","doi-asserted-by":"crossref","unstructured":"Kumar P, Jain S, Raman B, Roy PP, Iwamura M (2021) End-to-end triplet loss based emotion embedding system for speech emotion recognition. In: 2020 25th International conference on pattern recognition (ICPR), pp 8766\u20138773","DOI":"10.1109\/ICPR48806.2021.9413144"},{"issue":"8","key":"3430_CR19","doi-asserted-by":"publisher","first-page":"1301","DOI":"10.1109\/JSTSP.2017.2764438","volume":"11","author":"P Tzirakis","year":"2017","unstructured":"Tzirakis P, Trigeorgis G, Nicolaou MA, Schuller BW, Zafeiriou S (2017) End-to-end multimodal emotion recognition using deep neural networks. IEEE J Sel Top Signal Process 11(8):1301\u20131309","journal-title":"IEEE J Sel Top Signal Process"},{"key":"3430_CR20","doi-asserted-by":"crossref","unstructured":"Pang B, Peng Y, Gao J, Kong W (2024) Semi-supervised bipartite graph construction with active EEG sample selection for emotion recognition. Med Biol Eng Comput 1\u201320","DOI":"10.1007\/s11517-024-03094-z"},{"issue":"1","key":"3430_CR21","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1007\/s11517-022-02686-x","volume":"61","author":"X Guixun","year":"2023","unstructured":"Guixun X, Guo W, Wang Y (2023) Subject-independent EEG emotion recognition with hybrid spatio-temporal gru-conv architecture. Med Biol Eng Comput 61(1):61\u201373","journal-title":"Med Biol Eng Comput"},{"issue":"6","key":"3430_CR22","doi-asserted-by":"publisher","first-page":"1911","DOI":"10.1007\/s11517-024-03041-y","volume":"62","author":"H Tian","year":"2024","unstructured":"Tian H, Gong W, Li W, Qian Y (2024) Pastfnet: a paralleled attention spatio-temporal fusion network for micro-expression recognition. Med Biol Eng Comput 62(6):1911\u20131924","journal-title":"Med Biol Eng Comput"},{"key":"3430_CR23","doi-asserted-by":"crossref","unstructured":"Liu R, Zuo H, Lian Z, Schuller BW, Li H (2024) Contrastive learning based modality-invariant feature acquisition for robust multimodal emotion recognition with missing modalities. IEEE Trans Affect Comput 1\u201318","DOI":"10.1109\/TAFFC.2024.3378570"},{"key":"3430_CR24","doi-asserted-by":"crossref","unstructured":"Zhang J, Wang X, Zhang D, Lee D-J (2022) Semi-supervised group emotion recognition based on contrastive learning. Electronics 11(23)","DOI":"10.3390\/electronics11233990"},{"issue":"3","key":"3430_CR25","doi-asserted-by":"publisher","first-page":"2496","DOI":"10.1109\/TAFFC.2022.3164516","volume":"14","author":"X Shen","year":"2023","unstructured":"Shen X, Liu X, Xin H, Zhang D, Song S (2023) Contrastive learning of subject-invariant EEG representations for cross-subject emotion recognition. IEEE Trans Affect Comput 14(3):2496\u20132511","journal-title":"IEEE Trans Affect Comput"},{"issue":"2","key":"3430_CR26","doi-asserted-by":"publisher","first-page":"458","DOI":"10.1109\/TCSS.2022.3202249","volume":"10","author":"X Wang","year":"2023","unstructured":"Wang X, Zhang D, Tan H-Z, Lee D-J (2023) A self-fusion network based on contrastive learning for group emotion recognition. IEEE Trans Comput Soc Syst 10(2):458\u2013469","journal-title":"IEEE Trans Comput Soc Syst"},{"issue":"20","key":"3430_CR27","doi-asserted-by":"publisher","first-page":"19608","DOI":"10.1109\/JSEN.2022.3202209","volume":"22","author":"C Li","year":"2022","unstructured":"Li C, Xuejuan Lin Y, Liu RS, Cheng J, Chen X (2022) Eeg-based emotion recognition via efficient convolutional neural network and contrastive learning. IEEE Sens J 22(20):19608\u201319619","journal-title":"IEEE Sens J"},{"issue":"3","key":"3430_CR28","doi-asserted-by":"publisher","first-page":"2512","DOI":"10.1109\/TAFFC.2022.3170428","volume":"14","author":"Y Li","year":"2023","unstructured":"Li Y, Ji Chen F, Li BF, Hao W, Ji Y, Zhou Y, Niu Y, Shi G, Zheng W (2023) Gmss: graph-based multi-task self-supervised learning for EEG emotion recognition. IEEE Trans Affect Comput 14(3):2512\u20132525","journal-title":"IEEE Trans Affect Comput"},{"key":"3430_CR29","doi-asserted-by":"crossref","unstructured":"Ye W, Zhang Z, Teng F, Zhang M, Wang J, Ni D, Li F, Xu P, Liang Z (2024) Semi-supervised dual-stream self-attentive adversarial graph contrastive learning for cross-subject EEG-based emotion recognition. IEEE Trans Affect Comput 1\u201316","DOI":"10.1109\/TAFFC.2024.3433470"},{"key":"3430_CR30","doi-asserted-by":"crossref","unstructured":"Bousmalis K, Silberman N, Dohan D, Erhan D, Krishnan D (2017) Unsupervised pixel-level domain adaptation with generative adversarial networks. In 2017 IEEE conference on computer vision and pattern recognition (CVPR), pp 95\u2013104","DOI":"10.1109\/CVPR.2017.18"},{"key":"3430_CR31","unstructured":"Hoffman J, Tzeng E, Park T, Zhu J-Y, Isola P, Saenko K, Efros AA, Darrell T (2017) Cycada: cycle-consistent adversarial domain adaptation. ArXiv:1711.03213"},{"issue":"12","key":"3430_CR32","doi-asserted-by":"publisher","first-page":"5964","DOI":"10.1109\/JBHI.2022.3210158","volume":"26","author":"Z Li","year":"2022","unstructured":"Li Z, Zhu E, Jin M, Fan C, He H, Cai T, Li J (2022) Dynamic domain adaptation for class-aware cross-subject and cross-session EEG emotion recognition. IEEE J Biomed Health Inf 26(12):5964\u20135973","journal-title":"IEEE J Biomed Health Inf"},{"key":"3430_CR33","doi-asserted-by":"crossref","unstructured":"Chen H, Jin M, Li Z, Fan C, Li J, He H (2021) Ms-mda: multisource marginal distribution adaptation for cross-subject and cross-session eeg emotion recognition. Front Neurosci 15","DOI":"10.3389\/fnins.2021.778488"},{"key":"3430_CR34","doi-asserted-by":"crossref","unstructured":"Li H, Jin Y-M, Zheng W-L, Lu B-L (2018) Cross-subject emotion recognition using deep adaptation networks. In International conference on neural information processing","DOI":"10.1007\/978-3-030-04221-9_36"},{"key":"3430_CR35","doi-asserted-by":"crossref","unstructured":"Zhao L-M, Yan X, Lu B-L (2021) Plug-and-play domain adaptation for cross-subject eeg-based emotion recognition. In AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v35i1.16169"},{"key":"3430_CR36","doi-asserted-by":"crossref","unstructured":"Chen B, Chen CLP, Zhang T (2024) Gddn: graph domain disentanglement network for generalizable eeg emotion recognition. IEEE Trans Affect Comput 1\u201315","DOI":"10.1109\/TAFFC.2024.3371540"},{"key":"3430_CR37","unstructured":"Long M, Cao Y, Wang J, Jordan MI (2015) Learning transferable features with deep adaptation networks. ArXiv:1502.02791"},{"key":"3430_CR38","unstructured":"Long M, Zhu H, Wang J, Jordan MI (2016) Deep transfer learning with joint adaptation networks. ArXiv:1605.06636"},{"key":"3430_CR39","doi-asserted-by":"publisher","first-page":"2796","DOI":"10.1109\/TAFFC.2023.3259010","volume":"14","author":"S Li","year":"2023","unstructured":"Li S, Zhang T, Chen B, Chen CP (2023) Mia-net: multi-modal interactive attention network for multi-modal affective analysis. IEEE Trans Affect Comput 14:2796\u20132809","journal-title":"IEEE Trans Affect Comput"},{"key":"3430_CR40","unstructured":"Lu Y, Zheng W-L, Li B, Lu B-L (2015) Combining eye movements and eeg to enhance emotion recognition. In International joint conference on artificial intelligence"},{"key":"3430_CR41","doi-asserted-by":"publisher","first-page":"102129","DOI":"10.1016\/j.inffus.2023.102129","volume":"103","author":"J Tang","year":"2024","unstructured":"Tang J, Ma Z, Gan K, Zhang J, Yin Z (2024) Hierarchical multimodal-fusion of physiological signals for emotion recognition with scenario adaption and contrastive alignment. Inf Fusion 103:102129","journal-title":"Inf Fusion"},{"issue":"3","key":"3430_CR42","doi-asserted-by":"publisher","first-page":"671","DOI":"10.1007\/s11571-022-09851-w","volume":"17","author":"S Chen","year":"2023","unstructured":"Chen S, Tang J, Zhu L, Kong W (2023) A multi-stage dynamical fusion network for multimodal emotion recognition. Cognit Neurodyn 17(3):671\u2013680","journal-title":"Cognit Neurodyn"},{"key":"3430_CR43","doi-asserted-by":"crossref","unstructured":"Lan Y-T, Liu W, Lu B-L (2020) Multimodal emotion recognition using deep generalized canonical correlation analysis with an attention mechanism. In 2020 International joint conference on neural networks (IJCNN), pp 1\u20136","DOI":"10.1109\/IJCNN48605.2020.9207625"},{"issue":"2","key":"3430_CR44","doi-asserted-by":"publisher","first-page":"715","DOI":"10.1109\/TCDS.2021.3071170","volume":"14","author":"W Liu","year":"2022","unstructured":"Liu W, Qiu J-L, Zheng W-L, Bao-Liang L (2022) Comparing recognition performance and robustness of multimodal deep learning models for multimodal emotion recognition. IEEE Trans Cognit Dev Syst 14(2):715\u2013729","journal-title":"IEEE Trans Cognit Dev Syst"},{"key":"3430_CR45","doi-asserted-by":"crossref","unstructured":"Gong L, Chen W, Li M, Zhang T (2024) Emotion recognition from multiple physiological signals using intra- and inter-modality attention fusion network. Digit Signal Process 144:104278","DOI":"10.1016\/j.dsp.2023.104278"},{"key":"3430_CR46","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1016\/j.inffus.2019.06.019","volume":"53","author":"Y Jiang","year":"2020","unstructured":"Jiang Y, Li W, Hossain MS, Chen M, Alelaiwi A, Al-hammadi M (2020) A snapshot research and implementation of multimodal information fusion for data-driven emotion recognition. Inf Fusion 53:209\u2013221","journal-title":"Inf Fusion"},{"key":"3430_CR47","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.inffus.2020.01.011","volume":"59","author":"J Zhang","year":"2020","unstructured":"Zhang J, Yin Z, Chen P, Nichele S (2020) Emotion recognition using multi-modal data and machine learning techniques: a tutorial and review. Inf Fusion 59:103\u2013126","journal-title":"Inf Fusion"},{"issue":"2","key":"3430_CR48","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1109\/TPAMI.2018.2798607","volume":"41","author":"T Baltru\u0161aitis","year":"2019","unstructured":"Baltru\u0161aitis T, Ahuja C, Morency L-P (2019) Multimodal machine learning: a survey and taxonomy. IEEE Trans Pattern Anal Mach Intell 41(2):423\u2013443","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3430_CR49","doi-asserted-by":"publisher","first-page":"104741","DOI":"10.1016\/j.bspc.2023.104741","volume":"84","author":"J Quan","year":"2023","unstructured":"Quan J, Li Y, Wang L, He R, Yang S, Guo L (2023) Eeg-based cross-subject emotion recognition using multi-source domain transfer learning. Biomed Signal Process Control 84:104741","journal-title":"Biomed Signal Process Control"},{"key":"3430_CR50","doi-asserted-by":"crossref","unstructured":"Cao J, He X, Yang C, Chen S, Li Z, Wang Z (2022) Multi-source and multi-representation adaptation for cross-domain electroencephalography emotion recognition. Front Psychol 12","DOI":"10.3389\/fpsyg.2021.809459"},{"key":"3430_CR51","doi-asserted-by":"publisher","first-page":"08","DOI":"10.1007\/s00521-020-05670-4","volume":"33","author":"F Wang","year":"2021","unstructured":"Wang F, Zhang W, Zongfeng X, Ping J, Chu H (2021) A deep multi-source adaptation transfer network for cross-subject electroencephalogram emotion recognition. Neural Comput Appl 33:08","journal-title":"Neural Comput Appl"},{"key":"3430_CR52","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1109\/TNSRE.2022.3219418","volume":"31","author":"D Liu","year":"2023","unstructured":"Liu D, Zhang J, Hanrui W, Liu S, Long J (2023) Multi-source transfer learning for eeg classification based on domain adversarial neural network. IEEE Trans Neural Syst Rehab Eng 31:218\u2013228","journal-title":"IEEE Trans Neural Syst Rehab Eng"},{"key":"3430_CR53","first-page":"1","volume":"72","author":"Q She","year":"2023","unstructured":"She Q, Zhang C, Fang F, Ma Y, Zhang Y (2023) Multisource associate domain adaptation for cross-subject and cross-session eeg emotion recognition. IEEE Trans Instrum Meas 72:1\u201312","journal-title":"IEEE Trans Instrum Meas"},{"issue":"3","key":"3430_CR54","doi-asserted-by":"publisher","first-page":"1110","DOI":"10.1109\/TCYB.2018.2797176","volume":"49","author":"W-L Zheng","year":"2019","unstructured":"Zheng W-L, Liu W, Yifei L, Bao-Liang L, Cichocki A (2019) Emotionmeter: a multimodal framework for recognizing human emotions. IEEE Trans Cybernet 49(3):1110\u20131122","journal-title":"IEEE Trans Cybernet"},{"issue":"3","key":"3430_CR55","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1109\/TAMD.2015.2431497","volume":"7","author":"W-L Zheng","year":"2015","unstructured":"Zheng W-L, Bao-Liang L (2015) Investigating critical frequency bands and channels for eeg-based emotion recognition with deep neural networks. IEEE Trans Auton Mental Dev 7(3):162\u2013175","journal-title":"IEEE Trans Auton Mental Dev"},{"key":"3430_CR56","doi-asserted-by":"crossref","unstructured":"Duan R-N, Zhu J-Y, Lu B-L (2013) Differential entropy feature for eeg-based emotion classification. In 2013 6th International IEEE\/EMBS conference on neural engineering (NER), pp 81\u201384","DOI":"10.1109\/NER.2013.6695876"},{"issue":"6","key":"3430_CR57","first-page":"1442","volume":"24","author":"A Topic","year":"2021","unstructured":"Topic A, Russo M (2021) Emotion recognition based on eeg feature maps through deep learning network. Eng Sci Technol Int J 24(6):1442\u20131454","journal-title":"Eng Sci Technol Int J"},{"key":"3430_CR58","doi-asserted-by":"crossref","unstructured":"Jiwani N, Gupta K, Afreen N (2022) Automated seizure detection using theta band. In 2022 International conference on emerging smart computing and informatics (ESCI), pp 1\u20134","DOI":"10.1109\/ESCI53509.2022.9758331"},{"key":"3430_CR59","doi-asserted-by":"publisher","first-page":"102991","DOI":"10.1016\/j.bspc.2021.102991","volume":"70","author":"P Sarma","year":"2021","unstructured":"Sarma P, Barma S (2021) Emotion recognition by distinguishing appropriate eeg segments based on random matrix theory. Biomed Signal Process Control 70:102991","journal-title":"Biomed Signal Process Control"},{"key":"3430_CR60","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1007\/s00521-016-2445-y","volume":"29","author":"C Y\u00fccelba\u015f","year":"2016","unstructured":"Y\u00fccelba\u015f C, Y\u00fccelba\u015f \u015e, \u00d6z\u015fen S, Tezel G, K\u00fc\u00e7\u00e7\u00fckt\u00fcrk S, Yosunkaya \u015e (2016) Automatic detection of sleep spindles with the use of stft, emd and dwt methods. Neural Comput Appl 29:17\u201333","journal-title":"Neural Comput Appl"},{"key":"3430_CR61","unstructured":"Xu B, Wang N, Chen T, Li M(2015) Empirical evaluation of rectified activations in convolutional network. ArXiv:1505.00853"},{"key":"3430_CR62","doi-asserted-by":"crossref","unstructured":"Nair AA, Rangamani A, Nguyen LH, Bell MAL, Tran TD (2021) Spectral gap extrapolation and radio frequency interference suppression using 1d unets. In 2021 IEEE radar conference (RadarConf21), pp 1\u20136","DOI":"10.1109\/RadarConf2147009.2021.9455241"},{"key":"3430_CR63","unstructured":"Vaswani A, Shazeer NM, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. In Neural information processing systems"},{"key":"3430_CR64","doi-asserted-by":"crossref","unstructured":"Pipalia K, Bhadja R, Shukla M (2020) Comparative analysis of different transformer based architectures used in sentiment analysis. In 2020 9th International conference system modeling and advancement in research trends (SMART), pp 411\u2013415","DOI":"10.1109\/SMART50582.2020.9337081"},{"key":"3430_CR65","doi-asserted-by":"publisher","first-page":"102147","DOI":"10.1016\/j.inffus.2023.102147","volume":"103","author":"H Li","year":"2024","unstructured":"Li H, Xiao-Jun W (2024) Crossfuse: a novel cross attention mechanism based infrared and visible image fusion approach. Inf Fusion 103:102147","journal-title":"Inf Fusion"},{"issue":"2","key":"3430_CR66","doi-asserted-by":"publisher","first-page":"026039","DOI":"10.1088\/1741-2552\/ac63ec","volume":"19","author":"G Li","year":"2022","unstructured":"Li G, Chen N, Jin J (2022) Semi-supervised EEG emotion recognition model based on enhanced graph fusion and GCN. J Neural Eng 19(2):026039","journal-title":"J Neural Eng"},{"key":"3430_CR67","unstructured":"Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arxiv:1412.6980"},{"issue":"2","key":"3430_CR68","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1109\/TCDS.2020.2999337","volume":"13","author":"Y Li","year":"2021","unstructured":"Li Y, Wang L, Zheng W, Zong Y, Qi L, Cui Z, Zhang T, Song T (2021) A novel bi-hemispheric discrepancy model for eeg emotion recognition. IEEE Trans Cognit Dev Syst 13(2):354\u2013367","journal-title":"IEEE Trans Cognit Dev Syst"},{"key":"3430_CR69","doi-asserted-by":"publisher","first-page":"3245","DOI":"10.1109\/TNSRE.2023.3304660","volume":"31","author":"D Pan","year":"2023","unstructured":"Pan D, Zheng H, Feifan X, Ouyang Y, Jia Z, Wang C, Zeng H (2023) Msfr-gcn: a multi-scale feature reconstruction graph convolutional network for eeg emotion and cognition recognition. IEEE Trans Neural Syst Rehab Eng 31:3245\u20133254","journal-title":"IEEE Trans Neural Syst Rehab Eng"},{"key":"3430_CR70","doi-asserted-by":"crossref","unstructured":"Li J, Pan W, Huang H, Pan J, Wang F (2023) Stgate: spatial-temporal graph attention network with a transformer encoder for eeg-based emotion recognition. Front Human Neurosci 17","DOI":"10.3389\/fnhum.2023.1169949"},{"key":"3430_CR71","doi-asserted-by":"crossref","unstructured":"Li J, Li S, Pan J, Wang F (2021) Cross-subject eeg emotion recognition with self-organized graph neural network. Front Neurosci 15","DOI":"10.3389\/fnins.2021.611653"},{"key":"3430_CR72","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2023.3338676","volume":"73","author":"X Ning","year":"2024","unstructured":"Ning X, Wang J, Lin Y, Cai X, Chen H, Gou H, Li X, Jia Z (2024) Metaemotionnet: spatial\u2013spectral\u2013temporal-based attention 3-d dense network with meta-learning for eeg emotion recognition. IEEE Trans Instrumen Meas 73:1\u201313","journal-title":"IEEE Trans Instrumen Meas"},{"key":"3430_CR73","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1109\/JTEHM.2023.3320132","volume":"12","author":"W Li","year":"2024","unstructured":"Li W, Fang C, Zhu Z, Chen C, Song A (2024) Fractal spiking neural network scheme for EEG-based emotion recognition. IEEE J Trans Eng Health Med 12:106\u2013118","journal-title":"IEEE J Trans Eng Health Med"},{"key":"3430_CR74","doi-asserted-by":"publisher","first-page":"105875","DOI":"10.1016\/j.bspc.2023.105875","volume":"90","author":"S Bagherzadeh","year":"2024","unstructured":"Bagherzadeh S, Norouzi MR, Hampa SB, Ghasri A, Kouroshi PT, Hosseininasab S, Zadeh MAG, Nasrabadi AM (2024) A subject-independent portable emotion recognition system using synchrosqueezing wavelet transform maps of eeg signals and resnet-18. Biomed Signal Process Control 90:105875","journal-title":"Biomed Signal Process Control"},{"key":"3430_CR75","doi-asserted-by":"crossref","unstructured":"Guo W, Wang Y (2024) Convolutional gated recurrent unit-driven multidimensional dynamic graph neural network for subject-independent emotion recognition. Expert Syst Appl 238:121889","DOI":"10.1016\/j.eswa.2023.121889"},{"key":"3430_CR76","doi-asserted-by":"crossref","unstructured":"Katyal S, Ganesan RA (2023) Emotion recognition from eeg: self-attention and differentiable pooling improve sognn performance. In 2023 IEEE 20th India council international conference (INDICON), pp 281\u2013286","DOI":"10.1109\/INDICON59947.2023.10440863"},{"key":"3430_CR77","doi-asserted-by":"crossref","unstructured":"Liu R, Chao Y, Ma X, Sha X, Sun L, Li S, Chang S (2024) Ertnet: an interpretable transformer-based framework for eeg emotion recognition. Front Neurosci 18","DOI":"10.3389\/fnins.2024.1320645"},{"key":"3430_CR78","doi-asserted-by":"crossref","unstructured":"Delvigne V, Facchini A, Wannous H, Dutoit T, Ris L, Vandeborre J-P (2022) A saliency based feature fusion model for eeg emotion estimation. In 2022 44th Annual international conference of the IEEE engineering in medicine & biology society (EMBC), pp 3170\u20133174","DOI":"10.1109\/EMBC48229.2022.9871720"},{"key":"3430_CR79","doi-asserted-by":"crossref","unstructured":"Pan J, Liang R, He Z, Li J, Liang Y, Zhou X, He Y, Li Y (2024) St-scgnn: a spatio-temporal self-constructing graph neural network for cross-subject eeg-based emotion recognition and consciousness detection. IEEE J Biomed Health Inf 28(2):777\u2013788","DOI":"10.1109\/JBHI.2023.3335854"},{"key":"3430_CR80","doi-asserted-by":"publisher","first-page":"111199","DOI":"10.1016\/j.knosys.2023.111199","volume":"283","author":"W Guo","year":"2024","unstructured":"Guo W, Li Y, Liu M, Ma R, Wang Y (2024) Functional connectivity-enhanced feature-grouped attention network for cross-subject eeg emotion recognition. Knowl-Based Syst 283:111199","journal-title":"Knowl-Based Syst"},{"issue":"2","key":"3430_CR81","doi-asserted-by":"publisher","first-page":"026012","DOI":"10.1088\/1741-2552\/ac5c8d","volume":"19","author":"W Liu","year":"2022","unstructured":"Liu W, Zheng W-L, Li Z, Si-Yuan W, Gan L, Bao-Liang L (2022) Identifying similarities and differences in emotion recognition with EEG and eye movements among Chinese, German, and French people. J Neural Eng 19(2):026012","journal-title":"J Neural Eng"},{"key":"3430_CR82","doi-asserted-by":"publisher","first-page":"1213","DOI":"10.1007\/s13042-023-01964-w","volume":"15","author":"X Gong","year":"2023","unstructured":"Gong X, Dong Y, Zhang T (2023) Codf-net: coordinated-representation decision fusion network for emotion recognition with eeg and eye movement signals. Int J Mach Learn Cybern 15:1213\u20131226","journal-title":"Int J Mach Learn Cybern"},{"key":"3430_CR83","doi-asserted-by":"publisher","first-page":"1480","DOI":"10.1049\/cit2.12174","volume":"8","author":"X Zhang","year":"2023","unstructured":"Zhang X, Huang D, Li H, Zhang Y, Xia Y, Liu J (2023) Self-training maximum classifier discrepancy for eeg emotion recognition. CAAI Trans Intell Technol 8:1480\u20131491","journal-title":"CAAI Trans Intell Technol"},{"key":"3430_CR84","doi-asserted-by":"crossref","unstructured":"Gong M, Zhong W, Ye L, Zhang Q (2024) Misnet: multi-source information-shared eeg emotion recognition network with two-stream structure. Front Neurosci 18","DOI":"10.3389\/fnins.2024.1293962"},{"key":"3430_CR85","doi-asserted-by":"crossref","unstructured":"Li X, Chen CLP, Chen B, Zhang T (2024) Gusa: graph-based unsupervised subdomain adaptation for cross-subject eeg emotion recognition. IEEE Trans Affect Comput 1\u201312","DOI":"10.1109\/TAFFC.2024.3349770"},{"issue":"2","key":"3430_CR86","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1007\/s11517-023-02956-2","volume":"62","author":"L Zhu","year":"2024","unstructured":"Zhu L, Fei Y, Huang A, Ying N, Zhang J (2024) Instance-representation transfer method based on joint distribution and deep adaptation for eeg emotion recognition. Med Biol Eng Comput 62(2):479\u2013493","journal-title":"Med Biol Eng Comput"},{"key":"3430_CR87","doi-asserted-by":"publisher","first-page":"126262","DOI":"10.1016\/j.neucom.2023.126262","volume":"544","author":"F Yijin Zhou","year":"2023","unstructured":"Yijin Zhou F, Li YL, Ji Y, Shi G, Zheng W, Zhang L, Chen Y, Cheng R (2023) Progressive graph convolution network for eeg emotion recognition. Neurocomputing 544:126262","journal-title":"Neurocomputing"},{"key":"3430_CR88","doi-asserted-by":"publisher","first-page":"124001","DOI":"10.1016\/j.eswa.2024.124001","volume":"251","author":"M Zhu","year":"2024","unstructured":"Zhu M, Wu Q, Bai Z, Song Y, Gao Q (2024) Eeg-eye movement based subject dependence, cross-subject, and cross-session emotion recognition with multidimensional homogeneous encoding space alignment. Expert Syst Appl 251:124001","journal-title":"Expert Syst Appl"},{"key":"3430_CR89","doi-asserted-by":"crossref","unstructured":"Luo Y, Zhu L-Z, Lu B-L (2019) A GAN-Based Data Augmentation Method for Multimodal Emotion Recognition, pp 141\u2013150. 06","DOI":"10.1007\/978-3-030-22796-8_16"}],"container-title":["Medical &amp; Biological Engineering &amp; Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-025-03430-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-025-03430-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-025-03430-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T17:02:38Z","timestamp":1764781358000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-025-03430-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,28]]},"references-count":89,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["3430"],"URL":"https:\/\/doi.org\/10.1007\/s11517-025-03430-x","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"type":"print","value":"0140-0118"},{"type":"electronic","value":"1741-0444"}],"subject":[],"published":{"date-parts":[[2025,8,28]]},"assertion":[{"value":"11 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 August 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"We declare that there are no conflicts of interest related to this study. Specifically, we state the following:\n                      Financial Interests:\n                      None of the authors have received any financial support from companies, institutions, or individuals that could influence the outcome of this study. This includes, but is not limited to, research funding, consultancy fees, patent rights, or stock holdings.\n                      Personal Relationships:\n                      There are no personal relationships among the authors or between the authors and any other individuals or institutions that could compromise the integrity and independence of this research.\n                      Other Interests:\n                      None of the authors have any other activities or interests that could potentially bias the results of this study. We affirm that the results and conclusions presented in this study are solely based on scientific research and data analysis, unaffected by any external influences.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of Interest"}},{"value":"All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and\/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Research Involving Human Participants and\/or Animals"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed Consent"}}]}}