{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T02:40:57Z","timestamp":1777430457065,"version":"3.51.4"},"reference-count":52,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,7,29]],"date-time":"2022-07-29T00:00:00Z","timestamp":1659052800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["LY21F030005"],"award-info":[{"award-number":["LY21F030005"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["61971173"],"award-info":[{"award-number":["61971173"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["U20B2074"],"award-info":[{"award-number":["U20B2074"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["GK209907299001-008"],"award-info":[{"award-number":["GK209907299001-008"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["2017M620470"],"award-info":[{"award-number":["2017M620470"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["FZ2021KF16"],"award-info":[{"award-number":["FZ2021KF16"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["GD21202"],"award-info":[{"award-number":["GD21202"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["LY21F030005"],"award-info":[{"award-number":["LY21F030005"]}],"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":["61971173"],"award-info":[{"award-number":["61971173"]}],"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":["U20B2074"],"award-info":[{"award-number":["U20B2074"]}],"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":["GK209907299001-008"],"award-info":[{"award-number":["GK209907299001-008"]}],"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":["2017M620470"],"award-info":[{"award-number":["2017M620470"]}],"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":["FZ2021KF16"],"award-info":[{"award-number":["FZ2021KF16"]}],"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":["GD21202"],"award-info":[{"award-number":["GD21202"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fundamental Research Funds for the Provincial Universities of Zhejiang","award":["LY21F030005"],"award-info":[{"award-number":["LY21F030005"]}]},{"name":"Fundamental Research Funds for the Provincial Universities of Zhejiang","award":["61971173"],"award-info":[{"award-number":["61971173"]}]},{"name":"Fundamental Research Funds for the Provincial Universities of Zhejiang","award":["U20B2074"],"award-info":[{"award-number":["U20B2074"]}]},{"name":"Fundamental Research Funds for the Provincial Universities of Zhejiang","award":["GK209907299001-008"],"award-info":[{"award-number":["GK209907299001-008"]}]},{"name":"Fundamental Research Funds for the Provincial Universities of Zhejiang","award":["2017M620470"],"award-info":[{"award-number":["2017M620470"]}]},{"name":"Fundamental Research Funds for the Provincial Universities of Zhejiang","award":["FZ2021KF16"],"award-info":[{"award-number":["FZ2021KF16"]}]},{"name":"Fundamental Research Funds for the Provincial Universities of Zhejiang","award":["GD21202"],"award-info":[{"award-number":["GD21202"]}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["LY21F030005"],"award-info":[{"award-number":["LY21F030005"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["61971173"],"award-info":[{"award-number":["61971173"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["U20B2074"],"award-info":[{"award-number":["U20B2074"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["GK209907299001-008"],"award-info":[{"award-number":["GK209907299001-008"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2017M620470"],"award-info":[{"award-number":["2017M620470"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["FZ2021KF16"],"award-info":[{"award-number":["FZ2021KF16"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["GD21202"],"award-info":[{"award-number":["GD21202"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"CAAC Key Laboratory of Flight Techniques and Flight Safety","award":["LY21F030005"],"award-info":[{"award-number":["LY21F030005"]}]},{"name":"CAAC Key Laboratory of Flight Techniques and Flight Safety","award":["61971173"],"award-info":[{"award-number":["61971173"]}]},{"name":"CAAC Key Laboratory of Flight Techniques and Flight Safety","award":["U20B2074"],"award-info":[{"award-number":["U20B2074"]}]},{"name":"CAAC Key Laboratory of Flight Techniques and Flight Safety","award":["GK209907299001-008"],"award-info":[{"award-number":["GK209907299001-008"]}]},{"name":"CAAC Key Laboratory of Flight Techniques and Flight Safety","award":["2017M620470"],"award-info":[{"award-number":["2017M620470"]}]},{"name":"CAAC Key Laboratory of Flight Techniques and Flight Safety","award":["FZ2021KF16"],"award-info":[{"award-number":["FZ2021KF16"]}]},{"name":"CAAC Key Laboratory of Flight Techniques and Flight Safety","award":["GD21202"],"award-info":[{"award-number":["GD21202"]}]},{"name":"Guangxi Key Laboratory of Optoelectronic Information Processing, Guilin University of Electronic Technology","award":["LY21F030005"],"award-info":[{"award-number":["LY21F030005"]}]},{"name":"Guangxi Key Laboratory of Optoelectronic Information Processing, Guilin University of Electronic Technology","award":["61971173"],"award-info":[{"award-number":["61971173"]}]},{"name":"Guangxi Key Laboratory of Optoelectronic Information Processing, Guilin University of Electronic Technology","award":["U20B2074"],"award-info":[{"award-number":["U20B2074"]}]},{"name":"Guangxi Key Laboratory of Optoelectronic Information Processing, Guilin University of Electronic Technology","award":["GK209907299001-008"],"award-info":[{"award-number":["GK209907299001-008"]}]},{"name":"Guangxi Key Laboratory of Optoelectronic Information Processing, Guilin University of Electronic Technology","award":["2017M620470"],"award-info":[{"award-number":["2017M620470"]}]},{"name":"Guangxi Key Laboratory of Optoelectronic Information Processing, Guilin University of Electronic Technology","award":["FZ2021KF16"],"award-info":[{"award-number":["FZ2021KF16"]}]},{"name":"Guangxi Key Laboratory of Optoelectronic Information Processing, Guilin University of Electronic Technology","award":["GD21202"],"award-info":[{"award-number":["GD21202"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>Individual differences often appear in electroencephalography (EEG) data collected from different subjects due to its weak, nonstationary and low signal-to-noise ratio properties. This causes many machine learning methods to have poor generalization performance because the independent identically distributed assumption is no longer valid in cross-subject EEG data. To this end, transfer learning has been introduced to alleviate the data distribution difference between subjects. However, most of the existing methods have focused only on domain adaptation and failed to achieve effective collaboration with label estimation. In this paper, an EEG feature transfer method combined with semi-supervised regression and bipartite graph label propagation (TSRBG) is proposed to realize the unified joint optimization of EEG feature distribution alignment and semi-supervised joint label estimation. Through the cross-subject emotion recognition experiments on the SEED-IV data set, the results show that (1) TSRBG has significantly better recognition performance in comparison with the state-of-the-art models; (2) the EEG feature distribution differences between subjects are significantly minimized in the learned shared subspace, indicating the effectiveness of domain adaptation; (3) the key EEG frequency bands and channels for cross-subject EEG emotion recognition are achieved by investigating the learned subspace, which provides more insights into the study of EEG emotion activation patterns.<\/jats:p>","DOI":"10.3390\/systems10040111","type":"journal-article","created":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T23:49:27Z","timestamp":1659397767000},"page":"111","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Transfer EEG Emotion Recognition by Combining Semi-Supervised Regression with Bipartite Graph Label Propagation"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6967-5944","authenticated-orcid":false,"given":"Wenzheng","family":"Li","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1208-972X","authenticated-orcid":false,"given":"Yong","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China"},{"name":"Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,29]]},"reference":[{"key":"ref_1","unstructured":"Beldoch, M. (1964). Sensitivity to expression of emotional meaning in three modes of communication. The Communication of Emotional Meaning, McGraw-Hill."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"185","DOI":"10.2190\/DUGG-P24E-52WK-6CDG","article-title":"Emotional intelligence","volume":"9","author":"Salovey","year":"1990","journal-title":"Imagin. Cogn. Personal."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Chen, L., Wu, M., Pedrycz, W., and Hirota, K. (2020). Emotion Recognition and Understanding for Emotional Human-Robot Interaction Systems, Springer.","DOI":"10.1007\/978-3-030-61577-2"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Papero, D., Frost, R., Havstad, L., and Noone, R. (2018). Natural systems thinking and the human family. Systems, 6.","DOI":"10.3390\/systems6020019"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Li, W., Huan, W., Hou, B., Tian, Y., Zhang, Z., and Song, A. (2021). Can emotion be transferred?\u2014A review on transfer learning for EEG-Based Emotion Recognition. IEEE Trans. Cogn. Dev. Syst.","DOI":"10.1109\/TCDS.2021.3098842"},{"key":"ref_6","first-page":"12","article-title":"A survey of emotion recognition based on EEG","volume":"31","author":"Nie","year":"2012","journal-title":"Chin. J. Biomed. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ko, B.C. (2018). A brief review of facial emotion recognition based on visual information. Sensors, 18.","DOI":"10.3390\/s18020401"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.specom.2019.12.001","article-title":"Speech emotion recognition: Emotional models, databases, features, preprocessing methods, supporting modalities, and classifiers","volume":"116","year":"2020","journal-title":"Speech Commun."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2937","DOI":"10.1007\/s10115-020-01449-0","article-title":"A survey of state-of-the-art approaches for emotion recognition in text","volume":"62","author":"Alswaidan","year":"2020","journal-title":"Knowl. Inf. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"9609","DOI":"10.1109\/TIM.2020.3006611","article-title":"Adaptive tunable Q wavelet transform-based emotion identification","volume":"69","author":"Khare","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1109\/TAFFC.2017.2768030","article-title":"Emotion recognition based on high-resolution EEG recordings and reconstructed brain sources","volume":"11","author":"Becker","year":"2020","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2021.3123218","article-title":"Performance enhancement of P300 detection by multiscale-CNN","volume":"70","author":"Wang","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_13","first-page":"331","article-title":"Affective, natural interaction using EEG: Sensors, application and future directions","volume":"Volume 7297","author":"Maglogiannis","year":"2012","journal-title":"Lecture Notes in Computer Science, Proceedings of the Artificial Intelligence: Theories and Applications\u20147th Hellenic Conference on AI (SETN 2012), Lamia, Greece, 28\u201331 May 2012"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Marei, A., Yoon, S.A., Yoo, J.U., Richman, T., Noushad, N., Miller, K., and Shim, J. (2021). Designing feedback systems: Examining a feedback approach to facilitation in an online asynchronous professional development course for high school science teachers. Systems, 9.","DOI":"10.3390\/systems9010010"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1109\/TII.2018.2868431","article-title":"Brain network analysis of compressive sensed high-density EEG signals in AD and MCI subjects","volume":"15","author":"Mammone","year":"2018","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_16","unstructured":"Murugappan, M., Rizon, M., Nagarajan, R., Yaacob, S., Hazry, D., and Zunaidi, I. (2008, January 25\u201328). Time-frequency analysis of EEG signals for human emotion detection. Proceedings of the 4th Kuala Lumpur International Conference on Biomedical Engineering 2008, Kuala Lumpur, Malaysia."},{"key":"ref_17","first-page":"576","article-title":"Analysis of EEG based emotion detection of DEAP and SEED-IV databases using SVM","volume":"8","author":"Thejaswini","year":"2019","journal-title":"SSRN Electron. J."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"162","DOI":"10.3389\/fnins.2018.00162","article-title":"Exploring EEG features in cross-subject emotion recognition","volume":"12","author":"Li","year":"2018","journal-title":"Front. Neurosci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2250021","DOI":"10.1142\/S0129065722500216","article-title":"Deep learning methods for multi-channel EEG-based emotion recognition","volume":"32","author":"Atasever","year":"2022","journal-title":"Int. J. Neural Syst."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Lew, W.C.L., Wang, D., Shylouskaya, K., Zhang, Z., Lim, J.H., Ang, K.K., and Tan, A.H. (2020, January 20\u201324). EEG-based emotion recognition using spatial-temporal representation via Bi-GRU. Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada.","DOI":"10.1109\/EMBC44109.2020.9176682"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1109\/TCDS.2021.3079712","article-title":"Deep learning in EEG: Advance of the last ten-year critical period","volume":"14","author":"Gong","year":"2022","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1109\/TCDS.2019.2949306","article-title":"Domain adaptation for EEG emotion recognition based on latent representation similarity","volume":"12","author":"Li","year":"2020","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"ref_23","unstructured":"Gong, B., Shi, Y., Sha, F., and Grauman, K. (2012, January 16\u201321). Geodesic flow kernel for unsupervised domain adaptation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1109\/TCYB.2016.2633306","article-title":"Learning domain-invariant subspace using domain features and independence maximization","volume":"48","author":"Yan","year":"2018","journal-title":"IEEE Trans. Cybern."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.neucom.2021.02.048","article-title":"A novel transferability attention neural network model for EEG emotion recognition","volume":"447","author":"Li","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_26","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."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1109\/TAFFC.2017.2712143","article-title":"Identifying stable patterns over time for emotion recognition from EEG","volume":"10","author":"Zheng","year":"2017","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_28","first-page":"1769","article-title":"Physiological signals based affective computing: A systematic review","volume":"47","author":"Quan","year":"2021","journal-title":"Acta Autom. Sin."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2020\/8875426","article-title":"EEG-based emotion recognition: A state-of-the-art review of current trends and opportunities","volume":"2020","author":"Suhaimi","year":"2020","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_30","first-page":"36","article-title":"A survey of affective brain-computer interface","volume":"3","author":"Lu","year":"2021","journal-title":"Chin. J. Intell. Sci. Technol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1109\/TAI.2021.3054609","article-title":"A decade survey of transfer learning (2010\u20132020)","volume":"1","author":"Niu","year":"2020","journal-title":"IEEE Trans. Artif. Intell."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","article-title":"A comprehensive survey on transfer learning","volume":"109","author":"Zhuang","year":"2020","journal-title":"Proc. IEEE"},{"key":"ref_33","unstructured":"Zheng, W.L., and Lu, B.L. (2016, January 9\u201315). Personalizing EEG-based affective models with transfer learning. Proceedings of the 25th International Joint Conference on Artificial Intelligence, New York, NY, USA."},{"key":"ref_34","unstructured":"Zhou, R., Zhang, Z., Yang, X., Fu, H., Zhang, L., Li, L., Huang, G., Dong, Y., Li, F., and Liang, Z. (2022). A novel transfer learning framework with prototypical representation based pairwise learning for cross-subject cross-session EEG-based emotion recognition. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"103300","DOI":"10.1016\/j.bspc.2021.103300","article-title":"Deep learning-based classification of multichannel bio-signals using directedness transfer learning","volume":"72","author":"Bahador","year":"2022","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_36","doi-asserted-by":"crossref","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":"ref_37","doi-asserted-by":"crossref","unstructured":"Ding, Z., Li, S., Shao, M., and Fu, Y. (2018, January 8\u201314). Graph adaptive knowledge transfer for unsupervised domain adaptation. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01216-8_3"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1109\/TCDS.2018.2826840","article-title":"Domain adaptation techniques for EEG-based emotion recognition: A comparative study on two public datasets","volume":"11","author":"Lan","year":"2019","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Cui, J., Jin, X., Hu, H., Zhu, L., Ozawa, K., Pan, G., and Kong, W. (2021). Dynamic Distribution Alignment with Dual-Subspace Mapping For Cross-Subject Driver Mental State Detection. IEEE Trans. Cogn. Dev. Syst.","DOI":"10.1109\/TCDS.2021.3137530"},{"key":"ref_40","first-page":"1205","article-title":"Optimal kernel choice for large-scale two-sample tests","volume":"Volume 25","author":"Pereira","year":"2012","journal-title":"Curran Associates, Incorporated, Proceedings of the 26th Annual Conference on Neural Information Processing Systems (NIPS 2012)"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1002\/wics.101","article-title":"Principal component analysis","volume":"2","author":"Abdi","year":"2010","journal-title":"Wiley Interdiscip. Rev. Comput. Stat."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"820","DOI":"10.1145\/361573.361582","article-title":"Solution of the matrix equation AX + XB = C [F4]","volume":"15","author":"Bartels","year":"1972","journal-title":"Commun. ACM"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.ins.2021.04.058","article-title":"Fuzzy graph clustering","volume":"571","author":"Peng","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_44","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."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Long, M., Wang, J., Ding, G., Sun, J., and Yu, P.S. (2013, January 1\u20138). Transfer feature learning with joint distribution adaptation. Proceedings of the IEEE International Conference on Computer Vision, Sydney, NSW, Australia.","DOI":"10.1109\/ICCV.2013.274"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1109\/TAFFC.2018.2800046","article-title":"Feature selection based transfer subspace learning for speech emotion recognition","volume":"11","author":"Song","year":"2018","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_47","unstructured":"Nie, F., Wang, X., Deng, C., and Huang, H. (2017, January 4\u20139). Learning a structured optimal bipartite graph for co-clustering. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"532","DOI":"10.1109\/TAFFC.2018.2817622","article-title":"EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks","volume":"11","author":"Song","year":"2020","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_49","unstructured":"Zhou, Z. (2016). Machine Learning Beijing, Tsinghua University Press."},{"key":"ref_50","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Hinton","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Peng, Y., Qin, F., Kong, W., Ge, Y., Nie, F., and Cichocki, A. (2021). GFIL: A unified framework for the importance analysis of features, frequency bands and channels in EEG-based emotion recognition. IEEE Trans. Cogn. Dev. Syst.","DOI":"10.1109\/TCDS.2021.3082803"},{"key":"ref_52","unstructured":"Nie, F., Huang, H., Cai, X., and Ding, C. (2010, January 6\u20139). Efficient and robust feature selection via joint \u21132,1-norms minimization. Proceedings of the 23rd International Conference on Neural Information Processing Systems, Vancouver, BC, Canada."}],"container-title":["Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-8954\/10\/4\/111\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:59:38Z","timestamp":1760140778000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-8954\/10\/4\/111"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,29]]},"references-count":52,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["systems10040111"],"URL":"https:\/\/doi.org\/10.3390\/systems10040111","relation":{},"ISSN":["2079-8954"],"issn-type":[{"value":"2079-8954","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,29]]}}}