Possibilistic Clustering-Promoting Semi-Supervised Learning for EEG-Based Emotion Recognition
The purpose of the latest brain computer interface is to perform accurate emotion recognition through the customization of their recognizers to each subject. In the field of machine learning, graph-based semi-supervised learning (GSSL) has attracted more and more attention due to its intuitive and g...
Saved in:
| Published in | Frontiers in neuroscience Vol. 15; p. 690044 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Lausanne
Frontiers Research Foundation
23.06.2021
Frontiers Media S.A |
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | The purpose of the latest brain computer interface is to perform accurate emotion recognition through the customization of their recognizers to each subject. In the field of machine learning, graph-based semi-supervised learning (GSSL) has attracted more and more attention due to its intuitive and good learning performance for emotion recognition. However, the existing GSSL methods are sensitive or not robust enough to noise or outlier electroencephalogram (EEG)-based data since each individual subject may present noise or outlier EEG patterns in the same scenario. To address the problem, in this paper, we invent a Possibilistic Clustering-Promoting semi-supervised learning method for EEG-based Emotion Recognition. Specifically, it constrains each instance to have the same label membership value with its local weighted mean to improve the reliability of the recognition method. In addition, a regularization term about fuzzy entropy is introduced into the objective function, and the generalization ability of membership function is enhanced by increasing the amount of sample discrimination information, which improves the robustness of the method to noise and the outlier. A large number of experimental results on the three real datasets (i.e., DEAP, SEED, and SEED-IV) show that the proposed method improves the reliability and robustness of the EEG-based emotion recognition. |
|---|---|
| AbstractList | The purpose of the latest brain computer interface is to perform accurate emotion recognition through the customization of their recognizers to each subject. In the field of machine learning, graph-based semi-supervised learning (GSSL) has attracted more and more attention due to its intuitive and good learning performance for emotion recognition. However, the existing GSSL methods are sensitive or not robust enough to noise or outlier electroencephalogram (EEG)-based data since each individual subject may present noise or outlier EEG patterns in the same scenario. To address the problem, in this paper, we invent a Possibilistic Clustering-Promoting semi-supervised learning method for EEG-based Emotion Recognition. Specifically, it constrains each instance to have the same label membership value with its local weighted mean to improve the reliability of the recognition method. In addition, a regularization term about fuzzy entropy is introduced into the objective function, and the generalization ability of membership function is enhanced by increasing the amount of sample discrimination information, which improves the robustness of the method to noise and the outlier. A large number of experimental results on the three real datasets (i.e., DEAP, SEED, and SEED-IV) show that the proposed method improves the reliability and robustness of the EEG-based emotion recognition.The purpose of the latest brain computer interface is to perform accurate emotion recognition through the customization of their recognizers to each subject. In the field of machine learning, graph-based semi-supervised learning (GSSL) has attracted more and more attention due to its intuitive and good learning performance for emotion recognition. However, the existing GSSL methods are sensitive or not robust enough to noise or outlier electroencephalogram (EEG)-based data since each individual subject may present noise or outlier EEG patterns in the same scenario. To address the problem, in this paper, we invent a Possibilistic Clustering-Promoting semi-supervised learning method for EEG-based Emotion Recognition. Specifically, it constrains each instance to have the same label membership value with its local weighted mean to improve the reliability of the recognition method. In addition, a regularization term about fuzzy entropy is introduced into the objective function, and the generalization ability of membership function is enhanced by increasing the amount of sample discrimination information, which improves the robustness of the method to noise and the outlier. A large number of experimental results on the three real datasets (i.e., DEAP, SEED, and SEED-IV) show that the proposed method improves the reliability and robustness of the EEG-based emotion recognition. The purpose of the latest brain computer interface is to perform accurate emotion recognition through the customization of their recognizers to each subject. In the field of machine learning, graph-based semi-supervised learning (GSSL) has attracted more and more attention due to its intuitive and good learning performance for emotion recognition. However, the existing GSSL methods are sensitive or not robust enough to noise or outlier electroencephalogram (EEG)-based data since each individual subject may present noise or outlier EEG patterns in the same scenario. To address the problem, in this paper, we invent a Possibilistic Clustering-Promoting semi-supervised learning method for EEG-based Emotion Recognition. Specifically, it constrains each instance to have the same label membership value with its local weighted mean to improve the reliability of the recognition method. In addition, a regularization term about fuzzy entropy is introduced into the objective function, and the generalization ability of membership function is enhanced by increasing the amount of sample discrimination information, which improves the robustness of the method to noise and the outlier. A large number of experimental results on the three real datasets (i.e., DEAP, SEED, and SEED-IV) show that the proposed method improves the reliability and robustness of the EEG-based emotion recognition. The purpose of the latest brain computer interface(BCI) is to perform accurate emotion recognition through the customized their recognizers to each subject. In the field of machine learning, graph-based semi-supervised learning has attracted more and more attention due to its intuitive and good learning performance for emotion recognition. However, each individual subject may present noise or outlier EEG patterns in the same scenario, the existing GSSL methods are sensitive or not enough robust to noise or outlier EEG-based data. To address the problem, in this paper, we invent a Possibilistic Clustering-Promoting semi-supervised learning for EEG-based Emotion Recognition (PCP-ER). Specifically, it constrains each instance to have the same label membership value with its local weighted mean (LWM), so as to improve the reliability of the recognition method. In addition, a regularization term about fuzzy entropy is introduced into the objective function, and the generalization ability of membership function is enhanced by increasing the amount of sample discrimination information, which improves the robustness of the method to noise and outlier. A large number of experimental results on the three real datasets (i.e., DEAP, SEED and SEED-IV) show that the proposed method improves the reliability and robustness of the EEG emotion recognition. |
| Author | Tao, Jianwen Dan, Yufang Zhou, Di Fu, Jianjing |
| AuthorAffiliation | 2 School of Media Engineering, Communication University of Zhejiang , Hangzhou , China 1 Institute of Artificial Intelligence Application, Ningbo Polytechnic , Ningbo , China 3 Dazhou Industrial Technological Institute of Intelligent Manufacturing, Sichuan University of Arts and Science , Dazhou , China |
| AuthorAffiliation_xml | – name: 2 School of Media Engineering, Communication University of Zhejiang , Hangzhou , China – name: 1 Institute of Artificial Intelligence Application, Ningbo Polytechnic , Ningbo , China – name: 3 Dazhou Industrial Technological Institute of Intelligent Manufacturing, Sichuan University of Arts and Science , Dazhou , China |
| Author_xml | – sequence: 1 givenname: Yufang surname: Dan fullname: Dan, Yufang – sequence: 2 givenname: Jianwen surname: Tao fullname: Tao, Jianwen – sequence: 3 givenname: Jianjing surname: Fu fullname: Fu, Jianjing – sequence: 4 givenname: Di surname: Zhou fullname: Zhou, Di |
| BookMark | eNp1Uk2LFDEQDbLifugP8NbgxUuP-ahOdy6CDuO6MODiKniRkM7HmKE7GZPuhf33pndWcBc85VH16hWv8s7RSYjBIvSa4BVjnXjngg95RTElKy4wBniGzgjntIaG_Tj5B5-i85z3GHPaAX2BThnQllPRnKGf1zFn3_vB58nraj3MebLJh119neIYp4KqGzv6-mY-2HTrszXV1qoUloaLqdpsLuuPailvFnoM1Ver4y74Bb9Ez50asn318F6g758239af6-2Xy6v1h22tocVTTbUFw4E4wM70jnauV442HHRjlNFCM8Maq8E44rBrG2d4g6HHmAiHsRPsAl0ddU1Ue3lIflTpTkbl5X0hpp1UqfgbrOStYMAZ1QwYENH3nSK9cKTTuGzroGi9P2od5n60RtswJTU8En3cCf6X3MVb2dGOiJYUgbcPAin-nm2e5OiztsOggo1zlrRpGAUQmBbqmyfUfZxTKKcqLABGMReLu_bI0ql8VrJOaj-p5b5lvx8kwXKJg7yPg1ziII9xKJPkyeRfG_-f-QO4DrvJ |
| CitedBy_id | crossref_primary_10_1109_TNSRE_2022_3175464 crossref_primary_10_1016_j_jneumeth_2023_109909 crossref_primary_10_3389_fnins_2023_1247082 crossref_primary_10_3389_fnins_2024_1458815 crossref_primary_10_1007_s40815_023_01666_z crossref_primary_10_1109_ACCESS_2024_3454082 crossref_primary_10_1016_j_compbiomed_2022_105327 crossref_primary_10_1016_j_bspc_2023_105464 crossref_primary_10_1145_3712259 crossref_primary_10_3389_fnins_2023_1213099 crossref_primary_10_1016_j_jksuci_2023_03_014 crossref_primary_10_3389_fphys_2024_1425582 crossref_primary_10_3934_mbe_2023505 crossref_primary_10_3389_fnins_2022_855421 |
| Cites_doi | 10.1007/s11063-016-9569-z 10.1166/jmihi.2016.1923 10.1126/science.1076358 10.1007/s10115-009-0209-z 10.1080/2326263x.2014.912881 10.1109/tcds.2018.2826840 10.1109/tkde.2007.190672 10.1007/978-1-4757-2440-0 10.1007/978-94-017-2053-3_2 10.1007/s12652-019-01495-9 10.1155/2013/573734 10.24963/ijcai.2019/521 10.1162/neco.1992.4.6.888 10.1109/tamd.2015.2431497 10.1016/j.patcog.2018.10.023 10.1109/tip.2010.2044958 10.1166/jmihi.2017.2181 10.1109/ICWAPR.2007.4420738 10.1007/s10044-012-0298-2 10.1109/JPROC.2012.2197809 10.1109/t-affc.2011.15 10.1109/taffc.2018.2817622 10.1016/j.patcog.2016.07.006 10.1109/91.531779 10.1016/j.inffus.2020.09.002 10.1016/j.knosys.2016.01.021 10.1109/tcyb.2018.2797176 10.1109/91.227387 10.1109/tnn.2011.2108315 10.1007/s00186-007-0161-1 10.1016/0020-0255(86)90006-x 10.1109/tnnls.2012.2186825 10.1016/j.neunet.2015.01.009 10.3233/jifs-182561 10.1109/IJCNN.2014.6889641 10.7551/mitpress/9780262033589.001.0001 10.1007/978-3-031-01548-9 10.1109/access.2019.2937657 10.1109/taffc.2020.2994159 10.1109/taffc.2014.2339834 10.1145/1553374.1553456 10.1016/j.knosys.2019.105330 10.1109/CVPR.2016.90 10.1109/TAFFC.2018.2828819 10.1007/bf02471106 10.1137/1.9781611975673.33 10.1109/TAFFC.2017.2650899 10.1109/TPAMI.2016.2547397 10.1609/aaai.v25i1.7920 |
| ContentType | Journal Article |
| Copyright | 2021. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Copyright © 2021 Dan, Tao, Fu and Zhou. Copyright © 2021 Dan, Tao, Fu and Zhou. 2021 Dan, Tao, Fu and Zhou |
| Copyright_xml | – notice: 2021. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: Copyright © 2021 Dan, Tao, Fu and Zhou. – notice: Copyright © 2021 Dan, Tao, Fu and Zhou. 2021 Dan, Tao, Fu and Zhou |
| DBID | AAYXX CITATION 3V. 7XB 88I 8FE 8FH 8FK ABUWG AFKRA AZQEC BBNVY BENPR BHPHI CCPQU DWQXO GNUQQ HCIFZ LK8 M2P M7P PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS Q9U 7X8 5PM DOA |
| DOI | 10.3389/fnins.2021.690044 |
| DatabaseName | CrossRef ProQuest Central (Corporate) ProQuest Central (purchase pre-March 2016) Science Database (Alumni Edition) ProQuest SciTech Collection ProQuest Natural Science Collection ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials Biological Science Collection ProQuest Central Natural Science Collection ProQuest One Community College ProQuest Central ProQuest Central Student SciTech Premium Collection Biological Sciences Science Database Biological Science Database ProQuest Central Premium ProQuest One Academic Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China ProQuest Central Basic MEDLINE - Academic PubMed Central (Full Participant titles) Directory of Open Access Journals (DOAJ) |
| DatabaseTitle | CrossRef Publicly Available Content Database ProQuest Central Student ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Natural Science Collection ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences Natural Science Collection ProQuest Central Korea Biological Science Collection ProQuest Central (New) ProQuest Science Journals (Alumni Edition) ProQuest Biological Science Collection ProQuest Central Basic ProQuest Science Journals ProQuest One Academic Eastern Edition Biological Science Database ProQuest SciTech Collection ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic Publicly Available Content Database CrossRef |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Anatomy & Physiology |
| EISSN | 1662-453X |
| ExternalDocumentID | oai_doaj_org_article_67934632c343419bb8a1b9f18c0c5d84 PMC8281971 10_3389_fnins_2021_690044 |
| GroupedDBID | --- 29H 2WC 53G 5GY 5VS 88I 8FE 8FH 9T4 AAFWJ AAYXX ABUWG ACGFO ACGFS ACXDI ADRAZ AEGXH AENEX AFKRA AFPKN AIAGR ALMA_UNASSIGNED_HOLDINGS AZQEC BBNVY BENPR BHPHI BPHCQ CCPQU CITATION CS3 DIK DU5 DWQXO E3Z EBS EJD EMOBN F5P FRP GNUQQ GROUPED_DOAJ GX1 HCIFZ HYE KQ8 LK8 M2P M48 M7P O5R O5S OK1 OVT P2P PGMZT PHGZM PHGZT PIMPY PQQKQ PROAC RNS RPM W2D 3V. 7XB 8FK PKEHL PQEST PQGLB PQUKI PRINS Q9U 7X8 PUEGO 5PM |
| ID | FETCH-LOGICAL-c470t-2ce4d641f40fdbf28fbaf2564c5dadc9c3d35ec4df1f0f75fd6504b0019f00f93 |
| IEDL.DBID | M48 |
| ISSN | 1662-453X 1662-4548 |
| IngestDate | Wed Aug 27 01:22:26 EDT 2025 Thu Aug 21 18:00:42 EDT 2025 Sun Aug 24 03:09:16 EDT 2025 Fri Jul 25 11:38:47 EDT 2025 Thu Apr 24 23:01:21 EDT 2025 Tue Jul 01 01:39:24 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Language | English |
| License | This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c470t-2ce4d641f40fdbf28fbaf2564c5dadc9c3d35ec4df1f0f75fd6504b0019f00f93 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 This article was submitted to Brain Imaging Methods, a section of the journal Frontiers in Neuroscience Edited by: Yuanpeng Zhang, Nantong University, China These authors have contributed equally to this work Reviewed by: Yue Zhao, Harbin Institute of Technology, China; Jin Cui, Northwest University, China |
| OpenAccessLink | http://journals.scholarsportal.info/openUrl.xqy?doi=10.3389/fnins.2021.690044 |
| PMID | 34276295 |
| PQID | 2544320699 |
| PQPubID | 4424402 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_67934632c343419bb8a1b9f18c0c5d84 pubmedcentral_primary_oai_pubmedcentral_nih_gov_8281971 proquest_miscellaneous_2553244902 proquest_journals_2544320699 crossref_citationtrail_10_3389_fnins_2021_690044 crossref_primary_10_3389_fnins_2021_690044 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2021-06-23 |
| PublicationDateYYYYMMDD | 2021-06-23 |
| PublicationDate_xml | – month: 06 year: 2021 text: 2021-06-23 day: 23 |
| PublicationDecade | 2020 |
| PublicationPlace | Lausanne |
| PublicationPlace_xml | – name: Lausanne |
| PublicationTitle | Frontiers in neuroscience |
| PublicationYear | 2021 |
| Publisher | Frontiers Research Foundation Frontiers Media S.A |
| Publisher_xml | – name: Frontiers Research Foundation – name: Frontiers Media S.A |
| References | Zhang (B44) Joachims (B12) 1999 Tao (B31) 2019; 87 Li (B19) 2018 Wang (B36) 2019 Wang (B38) 2017; 46 Xue (B40) 2007 Li (B21) 2009 Zhao (B49) 2018 Zhou (B56) 2010; 24 Zhu (B57) 2003 Musha (B24) 1997; 1 Zheng (B51) 2019; 49 Kim (B13) 2013 Zheng (B50) 2015; 7 Koelstra (B14) 2012; 3 Atkeson (B1) 1997; 11 Bottou (B4) 1992; 4 Jenke (B11) 2014; 5 Xue (B41) 2011; 22 Zhang (B47); 193 Song (B27) 2018; 11 Belkin (B2) 2006; 7 Gorski (B9) 2007; 66 Kosko (B15) 1986; 40 Mühl (B23) 2014; 1 Liu (B22) 2012; 100 Lan (B18) 2019; 11 Zhang (B42); 37 Zhang (B43); 7 Zhu (B60) 2017 Tao (B28) 2017; 61 Zhu (B59) 2009 Chu (B6) 2017; 39 Zhang (B45) 2017; 7 Zhou (B53) 2003 Zu (B61) 2019; 13 Gao (B8) 2010; 38 Tao (B29) 2015; 69 Nie (B25) 2010; 19 Zhang (B46) 2016; 6 Zhu (B58) 2008 Bishop (B3) 2006 Wu (B39) 2018; 54 Wang (B34) 2008; 20 Li (B20) 2011 Dolan (B7) 2002; 298 Chapelle (B5) 2006 Zhang (B48); 66 Tu (B32) 2013; 16 Simonyan (B26) 2014 Zhong (B52) 2020; 2020 He (B10) 2016 Krishnapuram (B16) 1993; 1 Tao (B30) 2016; 98 Vapnik (B33) 1995 Zhou (B54) 2014 Wang (B37) 2012; 23 Wang (B35) 2019 Zhou (B55) 2018; 11 Krishnapuram (B17) 1996; 4 |
| References_xml | – volume: 46 start-page: 1 year: 2017 ident: B38 article-title: Toward-s safe semi-supervised classification: adjusted cluster assumption via clustering. publication-title: Neural Process. Lett. doi: 10.1007/s11063-016-9569-z – volume: 6 start-page: 1337 year: 2016 ident: B46 article-title: A clustering method based on fast exemplar finding and its application on brain magnetic resonance images segmentation. publication-title: J. Med. Imag. Health Inform. doi: 10.1166/jmihi.2016.1923 – volume: 298 start-page: 1191 year: 2002 ident: B7 article-title: Emotion, cognition, and behavior. publication-title: Science doi: 10.1126/science.1076358 – volume: 24 start-page: 415 year: 2010 ident: B56 article-title: Semi-supervised learning by disagreement. publication-title: Knowl. Inform. Syst. doi: 10.1007/s10115-009-0209-z – volume: 1 start-page: 66 year: 2014 ident: B23 article-title: A survey of affective brain computer interfaces: principles, state-of-the-art, and challenges. publication-title: Brain Comput. Interf. doi: 10.1080/2326263x.2014.912881 – volume: 11 start-page: 85 year: 2019 ident: B18 article-title: Domain adaptation techniques for eeg-based emotion recognition: a comparative study on two public datasets. publication-title: IEEE Trans. Cogn. Dev. Syst. doi: 10.1109/tcds.2018.2826840 – volume: 20 start-page: 55 year: 2008 ident: B34 article-title: Label propagation through linear neighborhoods. publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/tkde.2007.190672 – start-page: 69 year: 1995 ident: B33 publication-title: The Nature of Statistical Learning Theory. doi: 10.1007/978-1-4757-2440-0 – volume: 13 start-page: 1217 year: 2019 ident: B61 article-title: Semi-supervised classification application of remote sensing image based on block low rank images. publication-title: J. Front. Comput. Sci. Technol. – start-page: 1 year: 2014 ident: B26 article-title: Very deep convolutional networks for large-scale image recognition publication-title: Proceedings of the International Conference on Learning Representations (ICLR) – volume: 11 start-page: 11 year: 1997 ident: B1 article-title: Locally weighted learning. publication-title: Artif. Intell. Rev. doi: 10.1007/978-94-017-2053-3_2 – start-page: 1 ident: B44 article-title: A view-reduction based multi-view TSK fuzzy system and its application for textile color classification. publication-title: J. Ambient Intellig. Hum. Comput. doi: 10.1007/s12652-019-01495-9 – year: 2013 ident: B13 article-title: A review on the computational methods for emotional state estimation from the human EEG. doi: 10.1155/2013/573734 – start-page: 3755 year: 2019 ident: B35 article-title: Partial label learning with unlabeled data publication-title: Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI’19) doi: 10.24963/ijcai.2019/521 – year: 2018 ident: B19 article-title: A novel neural network model based on cerebral hemispheric asymmetry for EEG emotion recognition publication-title: 27th International Joint Conference on Artificial Intelligence (IJCAI) – volume: 4 start-page: 888 year: 1992 ident: B4 article-title: Local learning algorithms. publication-title: Neural Comput. doi: 10.1162/neco.1992.4.6.888 – volume: 54 start-page: 109 year: 2018 ident: B39 article-title: Semi-supervised pattern classification method based on Tri-DE-ELM. publication-title: Comput. Eng. Appl. – volume: 7 start-page: 162 year: 2015 ident: B50 article-title: Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks. publication-title: IEEE Trans. Autonom. Ment. Dev. doi: 10.1109/tamd.2015.2431497 – volume: 87 start-page: 296 year: 2019 ident: B31 article-title: Latent multi-feature co-regression for visual recognition by discriminatively leveraging multi-source models. publication-title: Patt. Recogn. doi: 10.1016/j.patcog.2018.10.023 – volume: 38 start-page: 1626 year: 2010 ident: B8 article-title: Global and local preserving based semi-supervised support vector machine. publication-title: Acta Electron. Sin. – volume: 19 start-page: 1921 year: 2010 ident: B25 article-title: Flexible manifold embedding: a framework for semi-supervised and unsupervised di- mension reduction. publication-title: IEEE Trans. Image Process. doi: 10.1109/tip.2010.2044958 – volume: 7 start-page: 1654 year: 2017 ident: B45 article-title: Brain MRI tissue classification based fuzzy clustering with competitive learning. publication-title: J. Med. Imag. Health Inform. doi: 10.1166/jmihi.2017.2181 – year: 2007 ident: B40 article-title: Alternative robust local embedding publication-title: Proceedings of the International Conference Wavelet Analysis Pattern Recognition doi: 10.1109/ICWAPR.2007.4420738 – start-page: 912 year: 2003 ident: B57 article-title: Semi-supervised learning using Gaussian fields and harmonic functions publication-title: Proceedings of the 20th International Conference on Machine Learning – volume: 16 start-page: 213 year: 2013 ident: B32 article-title: Semi-supervised feature extraction for EEG classification. publication-title: Patt. Analys. Appl. Paa doi: 10.1007/s10044-012-0298-2 – volume: 100 start-page: 2624 year: 2012 ident: B22 article-title: Robust and scalable graph-based semi-supervised learning. publication-title: Proc. IEEE doi: 10.1109/JPROC.2012.2197809 – volume: 3 start-page: 18 year: 2012 ident: B14 article-title: DEAP: a database for emotion analysis using physiological signals. publication-title: IEEE Trans. Affect. Comput. doi: 10.1109/t-affc.2011.15 – volume: 11 start-page: 532 year: 2018 ident: B27 article-title: EEG emotion recognition using dynamical graph convolutional neural networks. publication-title: Proc. IEEE Trans. Affect. Comput. doi: 10.1109/taffc.2018.2817622 – volume: 61 start-page: 47 year: 2017 ident: B28 article-title: Robust multi-source adaptation visual classification using supervised low-rank representation. publication-title: Patt. Recogn. doi: 10.1016/j.patcog.2016.07.006 – start-page: 200 year: 1999 ident: B12 article-title: Transductive inference for text classification using support vector machines publication-title: Proceedings of the 16th International Conference on Machine Learning – volume: 4 start-page: 385 year: 1996 ident: B17 article-title: The possibilistic c-means algorithm: insights and recommendations. publication-title: IEEE Trans. Fuzzy Syst. doi: 10.1109/91.531779 – volume: 7 start-page: 2399 year: 2006 ident: B2 article-title: Manifold regularization: a geometric framework for learning from examples. publication-title: J. Mach. Learn. Res. – volume: 66 start-page: 170 ident: B48 article-title: Alzheimer’s disease multiclass diagnosis via multimodal neuroimaging embedding feature selection and fusion. publication-title: Inform. Fusion doi: 10.1016/j.inffus.2020.09.002 – volume: 98 start-page: 76 year: 2016 ident: B30 article-title: Multi-source adaptation learning with global and local regularization by exploiting joint kernel sparse representation. publication-title: Knowl. Based Syst. doi: 10.1016/j.knosys.2016.01.021 – start-page: 4506 year: 2018 ident: B49 article-title: Label distribution learning by optimal transport publication-title: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI’18) – volume: 49 start-page: 1110 year: 2019 ident: B51 article-title: EmotionMeter: a multimodal framework for recognizing human emotions. publication-title: IEEE Trans. Cybernet. doi: 10.1109/tcyb.2018.2797176 – volume: 1 start-page: 98 year: 1993 ident: B16 article-title: A possibilistic approach to clustering. publication-title: IEEE Trans. Fuzzy Syst. doi: 10.1109/91.227387 – volume: 22 start-page: 573 year: 2011 ident: B41 article-title: Structural regularized support vector machine: a framework for structural large margin classifier. publication-title: IEEE Trans. Neural Netw. doi: 10.1109/tnn.2011.2108315 – volume: 66 start-page: 373 year: 2007 ident: B9 article-title: Biconvex sets and optimization with biconvex functions: a survey and extensions. publication-title: Math. Methods Operation Res. doi: 10.1007/s00186-007-0161-1 – volume: 40 start-page: 165 year: 1986 ident: B15 article-title: Fuzzy entropy and conditioning. publication-title: Inform. Sci. doi: 10.1016/0020-0255(86)90006-x – volume: 23 start-page: 689 year: 2012 ident: B37 article-title: New semi-supervised classification method based on modified cluster assumption. publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/tnnls.2012.2186825 – volume: 69 start-page: 80 year: 2015 ident: B29 article-title: L1-norm locally linear representation regularization multi-source adaptation learning. publication-title: Neural Netw. doi: 10.1016/j.neunet.2015.01.009 – volume: 37 start-page: 8535 ident: B42 article-title: Takagi-sugeno-kang fuzzy systems with dynamic rule weights. publication-title: J. Intellig. Fuzzy Syst. doi: 10.3233/jifs-182561 – start-page: 550 year: 2014 ident: B54 article-title: Spectral clustering-based local and global structure preservation for feature selection publication-title: Proceedings of 2014 International Joint Conference on Neural Networks doi: 10.1109/IJCNN.2014.6889641 – year: 2006 ident: B5 publication-title: Semi-Supervised Learning. doi: 10.7551/mitpress/9780262033589.001.0001 – year: 2009 ident: B59 publication-title: Introduction to Semi-Supervised Learning. doi: 10.1007/978-3-031-01548-9 – volume: 7 start-page: 127600 ident: B43 article-title: Common and special knowledge-driven TSK fuzzy system and its modeling and application for epileptic EEG signals recognition. publication-title: IEEE Access doi: 10.1109/access.2019.2937657 – start-page: 321 year: 2003 ident: B53 article-title: Learning with local and global consistency publication-title: Proceedings of the Advances in Neural Information Processing Systems (NIPS ’03) – volume: 2020 year: 2020 ident: B52 article-title: EEG-based emotion recognition using regularized graph neural networks. publication-title: IEEE Trans. Affect. Comput. doi: 10.1109/taffc.2020.2994159 – volume: 5 start-page: 327 year: 2014 ident: B11 article-title: Feature extraction and selection for emotion recognition from EEG. publication-title: IEEE Trans. Affect. Comput. doi: 10.1109/taffc.2014.2339834 – start-page: 633 year: 2009 ident: B21 article-title: Semi-supervised learning using label mean publication-title: Proceedings of the 26th International Conference on Machine Learning doi: 10.1145/1553374.1553456 – year: 2006 ident: B3 publication-title: Pattern Recognition and Machine Learning. – volume: 193 ident: B47 article-title: Clustering by transmission learning from data density to label manifold with statistical diffusion. publication-title: Knowl. Based Syst. doi: 10.1016/j.knosys.2019.105330 – year: 2008 ident: B58 publication-title: Semi-Supervised Learning Literature Survey. Computer Science TR 1530. – start-page: 770 year: 2016 ident: B10 article-title: Deep residual learning for image recognition publication-title: Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) doi: 10.1109/CVPR.2016.90 – volume: 11 start-page: 542 year: 2018 ident: B55 article-title: Visually interpretable representation learning for depression recognition from facial Im-ages. publication-title: IEEE Trans. Affect. Comput. doi: 10.1109/TAFFC.2018.2828819 – volume: 1 start-page: 15 year: 1997 ident: B24 article-title: Feature extraction from EEGs associated with emotions. publication-title: Artific. Life Robot. doi: 10.1007/bf02471106 – start-page: 289 year: 2019 ident: B36 article-title: Towards identifying causal relation between instances and labels publication-title: Proceedings of the 19th SIAM International Conference on Data Mining (SDM’19) doi: 10.1137/1.9781611975673.33 – year: 2017 ident: B60 article-title: Automated depression diagnosis based on deep networks to encode facial appearance and dynamics publication-title: Proceedings of the IEEE Transactions on Affective Computing doi: 10.1109/TAFFC.2017.2650899 – volume: 39 start-page: 529 year: 2017 ident: B6 article-title: Selective transfer machine for personalized facial action unit detection. publication-title: IEEE Trans. Patt. Analys. Mach. Intellig. doi: 10.1109/TPAMI.2016.2547397 – start-page: 386 year: 2011 ident: B20 article-title: Improving semi-supervised support vector machines through unlabeled instances selection publication-title: Proceedings of the 25th AAAI Conference on Artificial Intelligence doi: 10.1609/aaai.v25i1.7920 |
| SSID | ssj0062842 |
| Score | 2.3560617 |
| Snippet | The purpose of the latest brain computer interface is to perform accurate emotion recognition through the customization of their recognizers to each subject.... The purpose of the latest brain computer interface(BCI) is to perform accurate emotion recognition through the customized their recognizers to each subject. In... |
| SourceID | doaj pubmedcentral proquest crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Enrichment Source Index Database |
| StartPage | 690044 |
| SubjectTerms | Algorithms Brain Brain research Classification Clustering Computer applications Datasets EEG electroencephalogram Electroencephalography emotion recognition Emotions Entropy fuzzy entropy Implants Learning algorithms Machine learning membership function Neuroscience Noise semi-supervised classification |
| SummonAdditionalLinks | – databaseName: Directory of Open Access Journals (DOAJ) dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwELZQT1wQtEUESmWkigNSqGNPEvvYVlsqJFBFqdQLiuJXWan1VnT30H_fmTi7ai5w4Wo7ivN5PI94_A1jB1D3uu-bpnSipSs5QZVGVQ6VoY9gnYt1pPvO3743Z5fw9aq-elLqi3LCMj1wBu6wQQGCRkmngKjHrNV9ZU2stBOu9npgAkWbtw6msg5uUOnKfIaJIZg5jGmeiJtbVp8xGhQAEys0kPVPPMxpfuQTg3P6kr0YPUV-lGf4ij0LaZvtHCWMkm8f-Ec-5G4OP8V32K_zxX3OcyXaZX5ysyL-A7RK5XlOt0vX_CLczsuL1R0ph_vg-cises3RbeWz2ZfyuKfmWa7rw3-sM4sWaZddns5-npyVY-GE0kErlqV0AXwDVQQRvY1SR9tH9G0AEeu9M055VQcHPlZRxBaXA_00IP_JRCGiUa_ZVlqk8IZxE2KU3ltliBqrbY0G31qlwVpptPcFE2sgOzeyilNxi5sOowvCvhuw7wj7LmNfsE-bR-4ypcbfBh_T6mwGEhv20IAy0o0y0v1LRgq2t17bbtyi-BJi_pOiMaZgHzbduLnoxKRPYbGiMTU6nGCELFg7kYnJhKY9af57oOnWdEbZVm__xxe8Y88JFMpRk2qPbS3_rMJ79IaWdn8Q_Ee_MQre priority: 102 providerName: Directory of Open Access Journals – databaseName: ProQuest Central dbid: BENPR link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1Nb9QwELVge-GCCgWxtFRGQhyQQp3YSewTaquUColq1VKpJ6L4a1mpdbbd3UP_PTOJs2ouvcZOHI_t8RvP-A0hX0TeyKYpisSwEq_kOJ4onhpQhtYLbYzPPd53_n1RnF-LXzf5TTxwW8WwykEndoratgbPyI-QSotnrFDqx_I-waxR6F2NKTRekh1QwVJOyM5JdTG7HHRxAcq383cWeDcIwHnv1wSzTB35sAjI152l38FCZEKMdqaOwH-EOscxk082obNd8jqiR3rcD_cb8sKFt2TvOIDlfPdIv9IunrM7KN8jf2ftqo99RSpmenq7QU4E2KmSWR-CF-b0yt0tkqvNEhXGylka2VbnFKAsraqfCcVdztKqT_ZDL4dwoza8I9dn1Z_T8yRmU0iMKNk6yYwTthCpF8xb7TPpdeMB8AiT28YaZbjluTPC-tQzX8IYAXgTCKqUZ8wr_p5MQhvcB0KV8z6zVnOFfFllqaSwpeZSaJ0pae2UsEGStYlU45jx4rYGkwOFX3fCr1H4dS_8Kfm2fWXZ82w8V_kEh2dbESmyuwftw7yOK64uQPOIgmeGC-Ss01o2qVY-lYZBjyV85GAY3DquW2hkO8um5PO2GFYculGa4NoN1skBhQrFsikpR5Ni9EPjkrD413F3S3RclunH5xvfJ6-wuxiSlvEDMlk_bNwnAD9rfRhn-H82Rgbp priority: 102 providerName: ProQuest |
| Title | Possibilistic Clustering-Promoting Semi-Supervised Learning for EEG-Based Emotion Recognition |
| URI | https://www.proquest.com/docview/2544320699 https://www.proquest.com/docview/2553244902 https://pubmed.ncbi.nlm.nih.gov/PMC8281971 https://doaj.org/article/67934632c343419bb8a1b9f18c0c5d84 |
| Volume | 15 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1LT9wwELYoXHqpSmnVbWFlJNRDpVAndh4-oApQAFUCraAr7alR_FpWWhy6Dwn-fWfyWDUS4pJD4jjx-DHfeMbfEHIk4jIryyQJNEvxSI7lgeShhsXQOKG0drHD887XN8nVWPyaxJMt0qW3agW4fNG0w3xS48X8-Onv80-Y8CdocYK-_eH8zCPzdhQeg63HhHhDdkAxpZjQ4FpsnAoJrMS18zPBg0KA1Bsn58tV9NRUzebfg6D9AMr_NNLFe_KuhZL0tOn7XbJl_Qeyd-rBjH54pt9oHdxZ75rvkT-jatkEwiIvMz2fr5EgAdRWMGri8fyU3tmHWXC3fsTVY2kNbalXpxRwLc3zy4CiyjM0bzL_0Nsu9qjyH8n4Iv99fhW0qRUCLVK2CiJthUlE6ARzRrkoc6p0gH6Ejk1ptNTc8NhqYVzomEuhwwDJCURY0jHmJP9Etn3l7WdCpXUuMkZxieRZaSozYVLFM6FUJDNjBoR1kix0yzuO6S_mBdgfKPyiFn6Bwi8a4Q_I980rjw3pxmuFz7B7NgWRL7u-US2mRTv9igSWIZHwSHOBBHZKZWWopAszzaDFGVSy33Vu0Y3BAtnbeMQSKQfkcPMYph_6VEpvqzWWiQGSCsmiAUl7g6L3Q_0nfnZfE3ln6MVMwy-vf_wreYvNxfi0iO-T7dVibQ8ACa3UkOyc5Tej22G9kwDXy0k4rMf8P586DOE |
| linkProvider | Scholars Portal |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9NAEF5V6QEuiFIQgRYWCTggma531489INSWlJS2UdSH1BOu9xUitXZoEqH-KX4jM35E-NJbr_b6NTv7zYxn9htC3ssoT_M8jgPDEtyS40SgRGgADK2X2hgfedzvfDKKhxfyx2V0uUb-tnthsKyyxcQKqG1p8B_5DlJpCc5ipb7OfgfYNQqzq20LjVotjtzdHwjZ5l8Ov8H8fuD8YHC-PwyargKBkQlbBNw4aWMZesm81Z6nXuceDL80kc2tUUZYETkjrQ898wm8KzgxEp0L5RnzSL4EkL8uRcx4j6zvDUbj0xb7YwD7Kr8a414kCAbqPCqEgWrHF9MC-cF5-BkiUiZlxxJWDQM6Xm63RvM_o3fwlDxpvFW6W6vXBllzxTOyuVtApH5zRz_Sqn60-jG_SX6Oy3lda4vUz3T_eokcDGAZg3Fd8ldM6Jm7mQZnyxkC1NxZ2rC7Tii4znQw-B5QtKqWDurmQvS0LW8qi-fk4kHk_IL0irJwLwlVznturRYK-bmSRKXSJlqkUmuuUmv7hLWSzExDbY4dNq4zCHFQ-Fkl_AyFn9XC75NPq0tmNa_HfYP3cHpWA5GSuzpQ3k6yZoVnMSCdjAU3QiJHntZpHmrlw9Qw-OIUbrLVTm7W4AQ8ZKXVffJudRpWOKZt8sKVSxwTgdcrFeN9knSUovNC3TPF9FfFFZ5iojQJX93_8Lfk0fD85Dg7PhwdvSaP8dOxHI6LLdJb3C7dNjheC_2m0XZKrh56gf0DZYFFtg |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwELaqrYS4IKAgFgoYCTgghTq28_ABoT6ytBRWq5ZKPZHGr2WlNlm6u0L9a_w6ZvJYkUtvvcZOHI_H8_CMvyHkrYyKtCjiODAswSs5TgRKhAaEofVSG-Mjj_edv4_jwzP59Tw63yB_u7swmFbZycRaUNvK4Bn5DkJpCc5ipXZ8mxYxORh9nv8OsIIURlq7choNixy7mz_gvi0-HR3AWr_jfJT92D8M2goDgZEJWwbcOGljGXrJvNWep14XHowAaSJbWKOMsCJyRlofeuYT-G8waCQaGsoz5hGICcT_ZoJe0YBs7mXjyUmnB2IQ_HWsNcZ7SeAYNDFVcAlhBuWsRKxwHn4E75RJ2dOKdfGAnsXbz9f8TwGOHpIHreVKdxtWe0Q2XPmYbO2W4LVf3dD3tM4lrQ_pt8jPSbVo8m4RBpruX64QjwG0ZDBp0v_KKT11V7PgdDVHYbVwlrZIr1MKZjTNsi8BRQ1radYUGqInXapTVT4hZ3dC56dkUFale0aoct5za7VQiNWVJCqVNtEilVpzlVo7JKyjZG5amHOstnGZg7uDxM9r4udI_Lwh_pB8WL8ybzA-buu8h8uz7ojw3PWD6nqat7s9j0HqyVhwIyTi5WmdFqFWPkwNgxmn8JHtbnHzVmbAIGsOH5I362bY7RjCKUpXrbBPBBawVIwPSdJjit4P9VvK2a8aNzzFoGkSPr998NfkHmys_NvR-PgFuY8zx8w4LrbJYHm9ci_BBlvqVy2zU3Jx1_vrH0KLSes |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Possibilistic+Clustering-Promoting+Semi-Supervised+Learning+for+EEG-+based+Emotion+Recognition&rft.jtitle=Frontiers+in+neuroscience&rft.au=Dan%2C+Yufang&rft.au=Tao%2C+Jianwen&rft.au=Fu%2C+Jianjing&rft.au=Zhou%2C+Di&rft.date=2021-06-23&rft.pub=Frontiers+Research+Foundation&rft.issn=1662-4548&rft.eissn=1662-453X&rft_id=info:doi/10.3389%2Ffnins.2021.690044&rft.externalDBID=HAS_PDF_LINK |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1662-453X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1662-453X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1662-453X&client=summon |