A systematic literature review of emotion recognition using EEG signals
In this study, we conducted a systematic literature review of 107 primary studies conducted between 2017 and 2023 to discern trends in datasets, classifiers, and contributions to human emotion recognition using EEG signals. We identified DEAP (43%), SEED (29%), DREAMER (8%), and SEED-IV (5%) as the...
Uloženo v:
| Vydáno v: | Cognitive systems research Ročník 82; s. 101152 |
|---|---|
| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier B.V
01.12.2023
Elsevier |
| Témata: | |
| ISSN: | 1389-0417, 1389-0417 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | In this study, we conducted a systematic literature review of 107 primary studies conducted between 2017 and 2023 to discern trends in datasets, classifiers, and contributions to human emotion recognition using EEG signals. We identified DEAP (43%), SEED (29%), DREAMER (8%), and SEED-IV (5%) as the most commonly used EEG signal datasets. Deep learning techniques, especially transformer neural architecture search (TNAS), global-to-local feature aggregation network (GLFANet), attention-based convolutional transformer neural network (ACTNN) and efficient CNN-contrastive learning (ECNN-C), demonstrate superior performance across different datasets. Our comparative analysis of the validation scenarios revealed that subject-dependent validations outperformed subject-independent validations, highlighting the challenge of individual differences. This finding underscores the need for future research to address these individual differences in emotion recognition and enhance the generalizability of the models. The research contributions spanned seven categories: data augmentation, domain adaptation, channel selection, preprocessing, feature selection, classifiers, and feature extraction, with a major emphasis on feature extraction and classification (34%). This trend suggests a growing recognition of the importance of these contributions in the development of effective emotion-recognition systems. Our study contributes to the field by providing a comprehensive overview of recent trends, illuminating the performance of various classifiers, and highlighting key areas for future research on EEG-based emotion recognition. This work has significant implications for various applications, including mental health, human–computer interaction, and affective brain–computer interfaces. |
|---|---|
| AbstractList | In this study, we conducted a systematic literature review of 107 primary studies conducted between 2017 and 2023 to discern trends in datasets, classifiers, and contributions to human emotion recognition using EEG signals. We identified DEAP (43%), SEED (29%), DREAMER (8%), and SEED-IV (5%) as the most commonly used EEG signal datasets. Deep learning techniques, especially transformer neural architecture search (TNAS), global-to-local feature aggregation network (GLFANet), attention-based convolutional transformer neural network (ACTNN) and efficient CNN-contrastive learning (ECNN-C), demonstrate superior performance across different datasets. Our comparative analysis of the validation scenarios revealed that subject-dependent validations outperformed subject-independent validations, highlighting the challenge of individual differences. This finding underscores the need for future research to address these individual differences in emotion recognition and enhance the generalizability of the models. The research contributions spanned seven categories: data augmentation, domain adaptation, channel selection, preprocessing, feature selection, classifiers, and feature extraction, with a major emphasis on feature extraction and classification (34%). This trend suggests a growing recognition of the importance of these contributions in the development of effective emotion-recognition systems. Our study contributes to the field by providing a comprehensive overview of recent trends, illuminating the performance of various classifiers, and highlighting key areas for future research on EEG-based emotion recognition. This work has significant implications for various applications, including mental health, human–computer interaction, and affective brain–computer interfaces. |
| ArticleNumber | 101152 |
| Author | Nugroho, Hanung Adi Debayle, Johan Prabowo, Dwi Wahyu Setiawan, Noor Akhmad |
| Author_xml | – sequence: 1 givenname: Dwi Wahyu surname: Prabowo fullname: Prabowo, Dwi Wahyu organization: Department of Electrical and Information Engineering, Faculty of Engineering, Universitas Gadjah Mada, Jl. Grafika No. 2 Kampus UGM, Yogyakarta, 55281, Indonesia – sequence: 2 givenname: Hanung Adi surname: Nugroho fullname: Nugroho, Hanung Adi email: adinugroho@ugm.ac.id organization: Department of Electrical and Information Engineering, Faculty of Engineering, Universitas Gadjah Mada, Jl. Grafika No. 2 Kampus UGM, Yogyakarta, 55281, Indonesia – sequence: 3 givenname: Noor Akhmad surname: Setiawan fullname: Setiawan, Noor Akhmad organization: Department of Electrical and Information Engineering, Faculty of Engineering, Universitas Gadjah Mada, Jl. Grafika No. 2 Kampus UGM, Yogyakarta, 55281, Indonesia – sequence: 4 givenname: Johan surname: Debayle fullname: Debayle, Johan organization: MINES Saint-Etienne, CNRS, UMR 5307 LGF, Centre SPIN, Saint-Etienne, France |
| BackLink | https://hal-emse.ccsd.cnrs.fr/emse-04185401$$DView record in HAL |
| BookMark | eNqFkFFLwzAQx4NMcJt-Ax_6LHQ2TbM2PghjzE0Y-KLPIUkvM6NtJMkm-_amVkF80Kc7jv_vjvtN0KizHSB0jbMZzvD8dj9TdudPfpZnOelHmOZnaIxJxdKswOXoR3-BJt7vs4gxmo_RepFEMEArglFJYwI4EQ4OEgdHA--J1Qm0NhjbxUm80pnP_uBNt0tWq3Xiza4Tjb9E5zoWuPqqU_TysHpebtLt0_pxudimihQ4pKBkSVgldKHrspYVpoD1nLKyUIJVVGlKtNBSl0xqKqgguSyYFJLWkazYnEzRzbD3VTT8zZlWuBO3wvDNYsuh9cDjkxUtMnzEMXw3hJWz3jvQXJkg-geCE6bhOOO9P77ngz_e--ODvwgXv-Dvc_9g9wMG0UJU6LhXBjoFtYkCA6-t-XvBBw2sj7c |
| CitedBy_id | crossref_primary_10_1109_ACCESS_2023_3322294 crossref_primary_10_1016_j_bspc_2025_108111 crossref_primary_10_3389_fnhum_2024_1464431 crossref_primary_10_1007_s12021_025_09733_6 crossref_primary_10_1016_j_bspc_2025_108372 crossref_primary_10_1109_ACCESS_2025_3586713 crossref_primary_10_1007_s10791_025_09596_0 crossref_primary_10_1080_10447318_2025_2551049 crossref_primary_10_1109_ACCESS_2024_3409384 crossref_primary_10_1109_ACCESS_2024_3441514 crossref_primary_10_1016_j_jenvp_2025_102580 crossref_primary_10_1016_j_brainres_2024_149039 crossref_primary_10_1007_s10044_025_01501_1 crossref_primary_10_1007_s11571_025_10277_3 crossref_primary_10_7717_peerj_cs_2065 crossref_primary_10_3390_computers14080299 crossref_primary_10_3390_e26070540 crossref_primary_10_1155_2024_7499554 crossref_primary_10_1109_ACCESS_2024_3516198 crossref_primary_10_1088_1741_2552_ad4743 crossref_primary_10_1109_ACCESS_2024_3371904 crossref_primary_10_1016_j_asoc_2025_113478 crossref_primary_10_1016_j_knosys_2025_114318 crossref_primary_10_1016_j_prime_2024_100818 crossref_primary_10_3390_app15052328 |
| Cites_doi | 10.1016/j.bica.2018.04.012 10.3390/s17051014 10.1109/ACCESS.2019.2944273 10.1016/j.bspc.2022.104211 10.1016/j.ijhcs.2009.03.005 10.1186/s40537-020-00289-7 10.1080/10255842.2021.2007889 10.1016/j.bica.2018.01.001 10.1109/JBHI.2022.3212475 10.1007/s00500-022-07413-0 10.1088/1741-2552/abb580 10.3233/THC-174739 10.18201/ijisae.2021473639 10.1016/j.jestch.2021.03.012 10.1007/s10044-019-00860-w 10.3390/s19235218 10.1109/TII.2022.3170422 10.1088/1741-2552/aab2f2 10.1016/j.bspc.2019.101646 10.1007/s12652-020-02338-8 10.1109/ACCESS.2019.2904400 10.1109/JIOT.2021.3061727 10.1016/j.cogsys.2020.10.001 10.1016/j.comcom.2020.02.051 10.1016/j.compbiomed.2022.106344 10.1080/02699930143000392 10.1109/ACCESS.2020.2974009 10.1016/j.bspc.2020.101951 10.1155/2023/9223599 10.1016/j.bica.2018.04.011 10.3390/s22145158 10.1016/j.compbiomed.2022.105303 10.1016/j.bspc.2022.104141 10.1109/JSEN.2018.2883497 10.1016/j.compbiomed.2023.106860 10.1109/TCDS.2016.2587290 10.3390/jpm12010053 10.1109/ACCESS.2019.2891579 10.3390/s19214736 10.1088/1741-2552/ab0ab5 10.1109/JBHI.2020.2995767 10.1109/ACCESS.2019.2945059 10.3390/s18082739 10.1016/j.artmed.2018.01.001 10.1109/TAFFC.2020.3013711 10.14569/IJACSA.2018.090843 10.1016/j.bspc.2023.104835 10.1152/jn.2001.86.4.1983 10.1109/ACCESS.2022.3155647 10.1016/j.comcom.2020.10.004 10.3389/fcomp.2021.786964 10.3390/sym11050683 10.1016/j.future.2021.01.010 10.1109/TCDS.2021.3051465 10.1007/s13755-022-00201-y 10.1109/JBHI.2022.3198688 10.1016/j.cogsys.2018.06.009 10.1016/j.cogsys.2018.11.002 10.1016/j.compbiomed.2022.105519 10.1109/JBHI.2021.3091187 10.1186/1753-4631-3-2 10.1016/j.bspc.2023.104799 10.3390/computers9040095 10.1016/j.eij.2019.10.002 10.3390/s20072034 10.3390/s18051383 10.1016/j.compbiomed.2022.105327 10.1007/s10044-016-0567-6 10.1016/j.cmpb.2023.107380 10.1016/j.knosys.2022.109038 10.1016/j.bspc.2020.101867 10.1016/j.bspc.2022.103485 10.1016/j.knosys.2020.106243 10.1109/JSEN.2021.3073040 10.1109/JBHI.2022.3210158 10.1109/TCDS.2018.2868121 |
| ContentType | Journal Article |
| Copyright | 2023 Elsevier B.V. Distributed under a Creative Commons Attribution 4.0 International License |
| Copyright_xml | – notice: 2023 Elsevier B.V. – notice: Distributed under a Creative Commons Attribution 4.0 International License |
| DBID | AAYXX CITATION 1XC |
| DOI | 10.1016/j.cogsys.2023.101152 |
| DatabaseName | CrossRef Hyper Article en Ligne (HAL) |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Psychology Computer Science |
| EISSN | 1389-0417 |
| ExternalDocumentID | oai:HAL:emse-04185401v1 10_1016_j_cogsys_2023_101152 S1389041723000803 |
| GroupedDBID | --- --K --M .~1 0R~ 1B1 1RT 1~. 1~5 29F 4.4 457 4G. 53G 5GY 5VS 6J9 7-5 71M 8P~ AACTN AADFP AADPK AAEDT AAEDW AAGJA AAGUQ AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAXLA AAXUO AAYFN ABBOA ABCQJ ABFRF ABIVO ABJNI ABMAC ABOYX ABXDB ABYKQ ACDAQ ACGFO ACGFS ACHQT ACNNM ACRLP ACXNI ACZNC ADEZE ADJOM AEBSH AEFWE AEKER AENEX AFKWA AFTJW AFXIZ AFYLN AGHFR AGUBO AGWIK AGYEJ AHZHX AIALX AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD AXJTR BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ HVGLF HZ~ IHE J1W KOM M41 MO0 MOBAO N9A O-L O9- OAUVE OKEIE OZT P-8 P-9 P2P PC. Q38 RIG ROL RPZ SDF SDG SES SEW SPC SPCBC SSB SSN SST SSV SSY SSZ T5K UHS UNMZH ~G- 9DU AATTM AAXKI AAYWO AAYXX ACLOT AEIPS AFJKZ AIIUN ANKPU APXCP CITATION EFKBS ~HD 1XC |
| ID | FETCH-LOGICAL-c341t-ecb7398af4fd7db815e1f65974ca985cf53fafbf79bf5a5a32b49bab5decb8963 |
| ISICitedReferencesCount | 31 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001053403900001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1389-0417 |
| IngestDate | Tue Oct 14 20:22:14 EDT 2025 Sat Nov 29 07:03:46 EST 2025 Tue Nov 18 21:49:51 EST 2025 Fri Feb 23 02:35:43 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Emotion recognition Feature extraction EEG signals Classifier Dataset Research contribution |
| Language | English |
| License | Distributed under a Creative Commons Attribution 4.0 International License: http://creativecommons.org/licenses/by/4.0 |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c341t-ecb7398af4fd7db815e1f65974ca985cf53fafbf79bf5a5a32b49bab5decb8963 |
| ORCID | 0000-0002-9593-0467 |
| ParticipantIDs | hal_primary_oai_HAL_emse_04185401v1 crossref_citationtrail_10_1016_j_cogsys_2023_101152 crossref_primary_10_1016_j_cogsys_2023_101152 elsevier_sciencedirect_doi_10_1016_j_cogsys_2023_101152 |
| PublicationCentury | 2000 |
| PublicationDate | December 2023 2023-12-00 2023-12 |
| PublicationDateYYYYMMDD | 2023-12-01 |
| PublicationDate_xml | – month: 12 year: 2023 text: December 2023 |
| PublicationDecade | 2020 |
| PublicationTitle | Cognitive systems research |
| PublicationYear | 2023 |
| Publisher | Elsevier B.V Elsevier |
| Publisher_xml | – name: Elsevier B.V – name: Elsevier |
| References | Chanel, Kierkels, Soleymani, Pun (b16) 2009; 67 Jing, Liu, Hao (b39) 2009 Zhou, Chu, Li, Xiao, Sun (b96) 2020; 87 Chao, Dong, Liu, Lu (b17) 2020; 2020 Mert, Akan (b66) 2018; 21 Salama, A. El-Khoribi, E. Shoman, A. Wahby (b74) 2018; 9 Li, Song, Zhang, Zhang, Hou, Hu (b51) 2018; 12 Domínguez-Jiménez, Campo-Landines, Martínez-Santos, Delahoz, Contreras-Ortiz (b29) 2020; 55 Hwang, Hong, Son, Byun (b37) 2019; 23 Vafaei, Rahatabad, Setarehdan, Azadfallah (b85) 2023; 2023 Al Zoubi, Awad, Kasabov (b2) 2018; 86 Liu, Zhao, An, Zhao, Wang, Yan (b62) 2023; 85 Paus, Sipila, Strafella (b70) 2001; 86 Chakladar, Chakraborty (b15) 2018; 24 Topic, Russo (b83) 2021; 24 Gong, Li, Zhang, Chen (b33) 2023; 84 Asadur Rahman, Foisal Hossain, Hossain, Ahmmed (b7) 2020; 21 Klonowski (b44) 2009; 3 Li, Chai, Wang, Yang, Du (b47) 2021; 13 Luo, Zhu, Wan, Lu (b64) 2020; 17 Lin, Jung (b58) 2017; 11 Chai, Wang, Zhao, Li, Liu, Liu (b13) 2017; 17 Li, Lin, Liu, Song, Cheng, Chen (b50) 2022; 22 Cai, Chen, Yin (b12) 2019; 11 Xing, Li, Xu, Shu, Hu, Xu (b89) 2019; 13 Abhang, Gawali, Mehrotra (b1) 2016 Samavat, Khalili, Ayati, Ayati (b75) 2022; 10 Yin, Wang, Liu, Zhang, Zhang (b92) 2017; 11 Ma, Cui, Chen (b65) 2022 Ullah, Uzair, Mahmood, Ullah, Khan, Cheikh (b84) 2019; 7 Lin, Chen, Ma, Tang, Wang (b57) 2023; 231 Chai, Wang, Zhao, Liu, Liu, Bai (b14) 2018; 26 Pandey, Seeja (b69) 2020; 12 (b76) 2010 Kwon, Shin, Kim (b46) 2018; 18 Khojasteh, Ornek (b41) 2021; 9 Craik, He, Contreras-Vidal (b23) 2019; 16 Wankhade, Doye (b88) 2022; 25 Yerigeri, Ragha (b91) 2021; 65 Chao, Liu (b18) 2020; 8 Miao, Zheng, Xu, Yang, Hu (b67) 2023; 79 Sorbello, Tramonte, Calí, Giardina, Nishio, Ishiguro (b81) 2018; 23 Arnau-Gonzalez, Katsigiannis, Arevalillo-Herraez, Ramzan (b6) 2021; 8 Joshi, Ghongade (b40) 2020 Lotte, Bougrain, Cichocki, Clerc, Congedo, Rakotomamonjy (b63) 2018; 15 Priyasad, Fernando, Denman, Sridharan, Fookes (b72) 2022; 250 Liu, Xie, Wu, Cao, Li, Li (b61) 2019; 11 Liu, Fu (b59) 2021; 119 Liu, Li, Tang, Xu, Chen, Bezerianos (b60) 2019; 7 Guo, Li, Lu, Yang (b34) 2021; 3 Li, Zhang, Zhang, Huang, Liu, Chen (b54) 2023; 19 Chen, Jiang, Zhang, Zhang (b19) 2020; 154 Du, Ma, Zhang, Li, Lai, Zhao (b31) 2022; 13 Li, Chen, Li, Fu, Wu, Ji (b48) 2022 Song, Zheng, Lu, Zong, Zhang, Cui (b80) 2019; 7 Zheng (b94) 2017; 9 Kitchenham, Charters (b43) 2007 Dong, Ren (b30) 2020; 164 Oker, Glas, Pecune, Pelachaud (b68) 2018; 24 Pusarla, Singh, Tripathi (b73) 2022; 74 Tian, Li, Yang, Hou, Yang, Song (b82) 2023; 27 She, Shi, Fang, Ma, Zhang (b79) 2023; 159 Alazrai, Homoud, Alwanni, Daoud (b4) 2018; 18 Philippot, Chapelle, Blairy (b71) 2002; 16 Wang, Qiu, Ma, He (b87) 2020; 110 Li, Hua, Xu, Shu, Xu, Kuang (b49) 2022; 145 Cheng, Chen, Li, Liu, Song, Liu (b20) 2021; 25 Cimtay, Ekmekcioglu (b21) 2020; 20 Bigirimana, Siddique, Coyle (b11) 2016 Gupta, Chopda, Pachori (b35) 2019; 19 Cui, Liu, Zhang, Chen, Wang, Chen (b24) 2020; 205 Cizmeci, Ozcan, Durgut (b22) 2022; 26 Kurbalija, Ivanović, Radovanović, Geler, Dai, Zhao (b45) 2018; 52 Wang, Hu, Song (b86) 2019; 7 Li, Wang, Zhang, Liu, Song, Cheng (b52) 2022; 143 da Silva Junior, de Freitas, dos Santos, da Silva, Rodrigues, Conde (b25) 2019; 54 Ivanov, Kazantsev, Zavarzin, Klimenko, Milakhina, Matushkin (b38) 2022; 12 Deniz, Sobahi, Omar, Sengur, Acharya (b27) 2022; 10 Aslan (b9) 2021 ul Haq, Yao, Rahmaniar, Fawad, Islam (b36) 2022; 22 Li, Zhu, Jin, Fan, He, Cai (b56) 2022; 26 Kim, Yang, Nguyen, Prabhakar, Lee (b42) 2022; 26 Doma, Pirouz (b28) 2020; 7 Li, Zhao, Song, Zhang, Pan, Wu (b55) 2020; 14 Sangnark, Autthasan, Ponglertnapakorn, Chalekarn, Sudhawiyangkul, Trakulruangroj (b77) 2021; 21 Alhalaseh, Alasasfeh (b5) 2020; 9 Dar, Akram, Yuvaraj, Khawaja, Murugappan (b26) 2022; 144 Sharma, Pachori, Sircar (b78) 2020; 58 Bai, Liu, Hou, Chen, Cheng, Mao (b10) 2023; 152 Alakus, Gonen, Turkoglu (b3) 2020; 60 Li, Zhang, Song, Cheng, Liu, Chen (b53) 2021 Yang, Han, Min (b90) 2019; 19 Zeng, Wu, Jin, Zheng, Li, Zhao (b93) 2022; 71 yu Zhong, yu Yang, Liu, yu Zhen, da Zhao, bei Xie (b95) 2023; 79 Asghar, Khan, Fawad, Amin, Rizwan, Rahman (b8) 2019; 19 Feng, Cheng, Zhao, Deng, Zhang (b32) 2022; 26 Lin (10.1016/j.cogsys.2023.101152_b58) 2017; 11 Lotte (10.1016/j.cogsys.2023.101152_b63) 2018; 15 Bigirimana (10.1016/j.cogsys.2023.101152_b11) 2016 Miao (10.1016/j.cogsys.2023.101152_b67) 2023; 79 Priyasad (10.1016/j.cogsys.2023.101152_b72) 2022; 250 Salama (10.1016/j.cogsys.2023.101152_b74) 2018; 9 Li (10.1016/j.cogsys.2023.101152_b50) 2022; 22 Cheng (10.1016/j.cogsys.2023.101152_b20) 2021; 25 Cizmeci (10.1016/j.cogsys.2023.101152_b22) 2022; 26 Zeng (10.1016/j.cogsys.2023.101152_b93) 2022; 71 Oker (10.1016/j.cogsys.2023.101152_b68) 2018; 24 Cai (10.1016/j.cogsys.2023.101152_b12) 2019; 11 Joshi (10.1016/j.cogsys.2023.101152_b40) 2020 Craik (10.1016/j.cogsys.2023.101152_b23) 2019; 16 Arnau-Gonzalez (10.1016/j.cogsys.2023.101152_b6) 2021; 8 Li (10.1016/j.cogsys.2023.101152_b53) 2021 Dong (10.1016/j.cogsys.2023.101152_b30) 2020; 164 Chai (10.1016/j.cogsys.2023.101152_b13) 2017; 17 Xing (10.1016/j.cogsys.2023.101152_b89) 2019; 13 Li (10.1016/j.cogsys.2023.101152_b51) 2018; 12 Al Zoubi (10.1016/j.cogsys.2023.101152_b2) 2018; 86 Samavat (10.1016/j.cogsys.2023.101152_b75) 2022; 10 Ma (10.1016/j.cogsys.2023.101152_b65) 2022 Li (10.1016/j.cogsys.2023.101152_b49) 2022; 145 Ullah (10.1016/j.cogsys.2023.101152_b84) 2019; 7 Zheng (10.1016/j.cogsys.2023.101152_b94) 2017; 9 Khojasteh (10.1016/j.cogsys.2023.101152_b41) 2021; 9 Chai (10.1016/j.cogsys.2023.101152_b14) 2018; 26 Feng (10.1016/j.cogsys.2023.101152_b32) 2022; 26 Philippot (10.1016/j.cogsys.2023.101152_b71) 2002; 16 Topic (10.1016/j.cogsys.2023.101152_b83) 2021; 24 Zhou (10.1016/j.cogsys.2023.101152_b96) 2020; 87 Chen (10.1016/j.cogsys.2023.101152_b19) 2020; 154 Wang (10.1016/j.cogsys.2023.101152_b87) 2020; 110 Domínguez-Jiménez (10.1016/j.cogsys.2023.101152_b29) 2020; 55 Lin (10.1016/j.cogsys.2023.101152_b57) 2023; 231 ul Haq (10.1016/j.cogsys.2023.101152_b36) 2022; 22 Kim (10.1016/j.cogsys.2023.101152_b42) 2022; 26 Deniz (10.1016/j.cogsys.2023.101152_b27) 2022; 10 Asghar (10.1016/j.cogsys.2023.101152_b8) 2019; 19 Vafaei (10.1016/j.cogsys.2023.101152_b85) 2023; 2023 Pandey (10.1016/j.cogsys.2023.101152_b69) 2020; 12 Kitchenham (10.1016/j.cogsys.2023.101152_b43) 2007 Wang (10.1016/j.cogsys.2023.101152_b86) 2019; 7 Yin (10.1016/j.cogsys.2023.101152_b92) 2017; 11 Bai (10.1016/j.cogsys.2023.101152_b10) 2023; 152 Cimtay (10.1016/j.cogsys.2023.101152_b21) 2020; 20 Kwon (10.1016/j.cogsys.2023.101152_b46) 2018; 18 Wankhade (10.1016/j.cogsys.2023.101152_b88) 2022; 25 Aslan (10.1016/j.cogsys.2023.101152_b9) 2021 Chakladar (10.1016/j.cogsys.2023.101152_b15) 2018; 24 Kurbalija (10.1016/j.cogsys.2023.101152_b45) 2018; 52 Jing (10.1016/j.cogsys.2023.101152_b39) 2009 Luo (10.1016/j.cogsys.2023.101152_b64) 2020; 17 Mert (10.1016/j.cogsys.2023.101152_b66) 2018; 21 Dar (10.1016/j.cogsys.2023.101152_b26) 2022; 144 Liu (10.1016/j.cogsys.2023.101152_b59) 2021; 119 Alhalaseh (10.1016/j.cogsys.2023.101152_b5) 2020; 9 Yang (10.1016/j.cogsys.2023.101152_b90) 2019; 19 da Silva Junior (10.1016/j.cogsys.2023.101152_b25) 2019; 54 Li (10.1016/j.cogsys.2023.101152_b55) 2020; 14 Li (10.1016/j.cogsys.2023.101152_b47) 2021; 13 Gong (10.1016/j.cogsys.2023.101152_b33) 2023; 84 Asadur Rahman (10.1016/j.cogsys.2023.101152_b7) 2020; 21 Doma (10.1016/j.cogsys.2023.101152_b28) 2020; 7 Du (10.1016/j.cogsys.2023.101152_b31) 2022; 13 Guo (10.1016/j.cogsys.2023.101152_b34) 2021; 3 Sorbello (10.1016/j.cogsys.2023.101152_b81) 2018; 23 Chao (10.1016/j.cogsys.2023.101152_b18) 2020; 8 Li (10.1016/j.cogsys.2023.101152_b48) 2022 Song (10.1016/j.cogsys.2023.101152_b80) 2019; 7 Li (10.1016/j.cogsys.2023.101152_b54) 2023; 19 Alazrai (10.1016/j.cogsys.2023.101152_b4) 2018; 18 Pusarla (10.1016/j.cogsys.2023.101152_b73) 2022; 74 Ivanov (10.1016/j.cogsys.2023.101152_b38) 2022; 12 Paus (10.1016/j.cogsys.2023.101152_b70) 2001; 86 Cui (10.1016/j.cogsys.2023.101152_b24) 2020; 205 Liu (10.1016/j.cogsys.2023.101152_b62) 2023; 85 She (10.1016/j.cogsys.2023.101152_b79) 2023; 159 Li (10.1016/j.cogsys.2023.101152_b52) 2022; 143 Yerigeri (10.1016/j.cogsys.2023.101152_b91) 2021; 65 Klonowski (10.1016/j.cogsys.2023.101152_b44) 2009; 3 (10.1016/j.cogsys.2023.101152_b76) 2010 yu Zhong (10.1016/j.cogsys.2023.101152_b95) 2023; 79 Tian (10.1016/j.cogsys.2023.101152_b82) 2023; 27 Sangnark (10.1016/j.cogsys.2023.101152_b77) 2021; 21 Chanel (10.1016/j.cogsys.2023.101152_b16) 2009; 67 Liu (10.1016/j.cogsys.2023.101152_b61) 2019; 11 Abhang (10.1016/j.cogsys.2023.101152_b1) 2016 Gupta (10.1016/j.cogsys.2023.101152_b35) 2019; 19 Hwang (10.1016/j.cogsys.2023.101152_b37) 2019; 23 Alakus (10.1016/j.cogsys.2023.101152_b3) 2020; 60 Liu (10.1016/j.cogsys.2023.101152_b60) 2019; 7 Li (10.1016/j.cogsys.2023.101152_b56) 2022; 26 Sharma (10.1016/j.cogsys.2023.101152_b78) 2020; 58 Chao (10.1016/j.cogsys.2023.101152_b17) 2020; 2020 |
| References_xml | – volume: 19 start-page: 2266 year: 2019 end-page: 2274 ident: b35 article-title: Cross-subject emotion recognition using flexible analytic wavelet transform from EEG signals publication-title: IEEE Sensors Journal – volume: 9 start-page: 191 year: 2021 end-page: 197 ident: b41 article-title: TheRobust EEG based emotion recognition using deep neural network publication-title: International Journal of Intelligent Systems and Applications in Engineering – volume: 26 start-page: 10115 year: 2022 end-page: 10125 ident: b22 article-title: Channel selection and feature extraction on deep EEG classification using metaheuristic and welch PSD publication-title: Soft Computing – year: 2021 ident: b9 article-title: CNN based efficient approach for emotion recognition publication-title: Journal of King Saud University - Computer and Information Sciences – volume: 17 start-page: 1014 year: 2017 ident: b13 article-title: A fast, efficient domain adaptation technique for cross-domain electroencephalography(EEG)-based emotion recognition publication-title: Sensors – volume: 19 start-page: 5218 year: 2019 ident: b8 article-title: EEG-based multi-modal emotion recognition using bag of deep features: An optimal feature selection approach publication-title: Sensors – volume: 152 year: 2023 ident: b10 article-title: Emotion recognition with residual network driven by spatial-frequency characteristics of EEG recorded from hearing-impaired adults in response to video clips publication-title: Computers in Biology and Medicine – volume: 65 start-page: 79 year: 2021 end-page: 97 ident: b91 article-title: Speech stress recognition using semi-eager learning publication-title: Cognitive Systems Research – volume: 145 year: 2022 ident: b49 article-title: Cross-subject EEG emotion recognition combined with connectivity features and meta-transfer learning publication-title: Computers in Biology and Medicine – volume: 15 year: 2018 ident: b63 article-title: A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update publication-title: Journal of Neural Engineering – year: 2022 ident: b65 article-title: An improved Bi-LSTM EEG emotion recognition algorithm publication-title: Journal of Network Intelligence – volume: 7 start-page: 18 year: 2020 ident: b28 article-title: A comparative analysis of machine learning methods for emotion recognition using EEG and peripheral physiological signals publication-title: Journal of Big Data – volume: 12 start-page: 53 year: 2022 ident: b38 article-title: ICBrainDB: An integrated database for finding associations between genetic factors and EEG markers of depressive disorders publication-title: Journal of Personalized Medicine – start-page: 1 year: 2021 ident: b53 article-title: EEG -based emotion recognition via neural architecture search publication-title: IEEE Transactions on Affective Computing – volume: 14 start-page: 1 year: 2020 end-page: 13 ident: b55 article-title: Latent factor decoding of multi-channel EEG for emotion recognition through autoencoder-like neural networks publication-title: Frontiers in Neuroscience – volume: 23 start-page: 19 year: 2018 end-page: 26 ident: b81 article-title: Embodied responses to musical experience detected by human bio-feedback brain features in a geminoid augmented architecture publication-title: Biologically Inspired Cognitive Architectures – volume: 13 start-page: 1 year: 2019 end-page: 14 ident: b89 article-title: SAE+LSTM: A new framework for emotion recognition from multi-channel EEG publication-title: Frontiers in Neurorobotics – volume: 8 start-page: 12219 year: 2021 end-page: 12230 ident: b6 article-title: BED: A new data set for EEG-based biometrics publication-title: IEEE Internet of Things Journal – volume: 26 start-page: 5964 year: 2022 end-page: 5973 ident: b56 article-title: Dynamic domain adaptation for class-aware cross-subject and cross-session EEG emotion recognition publication-title: IEEE Journal of Biomedical and Health Informatics – volume: 144 year: 2022 ident: b26 article-title: EEG -based emotion charting for Parkinson's disease patients using convolutional recurrent neural networks and cross dataset learning publication-title: Computers in Biology and Medicine – volume: 3 start-page: 2 year: 2009 ident: b44 article-title: Everything you wanted to ask about EEG but were afraid to get the right answer publication-title: Nonlinear Biomedical Physics – volume: 26 start-page: 327 year: 2018 end-page: 335 ident: b14 article-title: Multi-subject subspace alignment for non-stationary EEG-based emotion recognition publication-title: Technology and Health Care – volume: 24 start-page: 107 year: 2018 end-page: 114 ident: b68 article-title: An embodied virtual agent platform for emotional stroop effect experiments: A proof of concept publication-title: Biologically Inspired Cognitive Architectures – volume: 54 start-page: 1 year: 2019 end-page: 20 ident: b25 article-title: Exploratory study of the effect of binaural beat stimulation on the EEG activity pattern in resting state using artificial neural networks publication-title: Cognitive Systems Research – volume: 85 year: 2023 ident: b62 article-title: GLFANet: A global to local feature aggregation network for EEG emotion recognition publication-title: Biomedical Signal Processing and Control – volume: 21 start-page: 23 year: 2020 end-page: 35 ident: b7 article-title: Employing PCA and t-statistical approach for feature extraction and classification of emotion from multichannel EEG signal publication-title: Egyptian Informatics Journal – volume: 7 start-page: 143303 year: 2019 end-page: 143311 ident: b86 article-title: Channel selection method for EEG emotion recognition using normalized mutual information publication-title: IEEE Access – volume: 60 year: 2020 ident: b3 article-title: Database for an emotion recognition system based on EEG signals and various computer games – GAMEEMO publication-title: Biomedical Signal Processing and Control – volume: 25 start-page: 453 year: 2021 end-page: 464 ident: b20 article-title: Emotion recognition from multi-channel EEG via deep forest publication-title: IEEE Journal of Biomedical and Health Informatics – start-page: 497 year: 2009 end-page: 500 ident: b39 article-title: The research on emotion recognition from ECG signal publication-title: 2009 Int. conf. inf. technol. comput. sci., vol. 1 – volume: 58 year: 2020 ident: b78 article-title: Automated emotion recognition based on higher order statistics and deep learning algorithm publication-title: Biomedical Signal Processing and Control – volume: 22 start-page: 5158 year: 2022 ident: b36 article-title: A hybrid hand-crafted and deep neural spatio-temporal EEG features clustering framework for precise emotional status recognition publication-title: Sensors – volume: 79 year: 2023 ident: b67 article-title: A multiple frequency bands parallel spatial–temporal 3D deep residual learning framework for EEG-based emotion recognition publication-title: Biomedical Signal Processing and Control – start-page: 004429 year: 2016 end-page: 004434 ident: b11 article-title: A hybrid ICA-wavelet transform for automated artefact removal in EEG-based emotion recognition publication-title: 2016 IEEE int. conf. syst. man, cybern. – volume: 19 start-page: 6016 year: 2023 end-page: 6025 ident: b54 article-title: EEG -based emotion recognition via transformer neural architecture search publication-title: IEEE Transactions on Industrial Informatics – volume: 55 year: 2020 ident: b29 article-title: A machine learning model for emotion recognition from physiological signals publication-title: Biomedical Signal Processing and Control – volume: 250 year: 2022 ident: b72 article-title: Affect recognition from scalp-EEG using channel-wise encoder networks coupled with geometric deep learning and multi-channel feature fusion publication-title: Knowledge-Based Systems – volume: 22 start-page: 19608 year: 2022 end-page: 19619 ident: b50 article-title: EEG-based emotion recognition via efficient convolutional neural network and contrastive learning – volume: 11 start-page: 517 year: 2019 end-page: 526 ident: b61 article-title: Electroencephalogram emotion recognition based on empirical mode decomposition and optimal feature selection publication-title: IEEE Transactions on Cognitive and Developmental Systems – volume: 24 start-page: 1442 year: 2021 end-page: 1454 ident: b83 article-title: Emotion recognition based on EEG feature maps through deep learning network publication-title: Engineering Science and Technology, an International Journal – volume: 74 year: 2022 ident: b73 article-title: Learning DenseNet features from EEG based spectrograms for subject independent emotion recognition publication-title: Biomedical Signal Processing and Control – volume: 67 start-page: 607 year: 2009 end-page: 627 ident: b16 article-title: Short-term emotion assessment in a recall paradigm publication-title: International Journal of Human-Computer Studies – volume: 110 year: 2020 ident: b87 article-title: A prototype-based SPD matrix network for domain adaptation EEG emotion recognition publication-title: Pattern Recognition – volume: 18 start-page: 1383 year: 2018 ident: b46 article-title: Electroencephalography based fusion two-dimensional (2D)-convolution neural networks (CNN) model for emotion recognition system publication-title: Sensors – volume: 52 start-page: 103 year: 2018 end-page: 116 ident: b45 article-title: Emotion perception and recognition: An exploration of cultural differences and similarities publication-title: Cognitive Systems Research – start-page: 480 year: 2010 end-page: 482 ident: b76 article-title: Encyclopedia of machine learning publication-title: Encycl. mach. learn. data min. – volume: 79 year: 2023 ident: b95 article-title: EEG emotion recognition based on TQWT-features and hybrid convolutional recurrent neural network publication-title: Biomedical Signal Processing and Control – volume: 84 year: 2023 ident: b33 article-title: EEG emotion recognition using attention-based convolutional transformer neural network publication-title: Biomedical Signal Processing and Control – volume: 19 start-page: 4736 year: 2019 ident: b90 article-title: A multi-column CNN model for emotion recognition from EEG signals publication-title: Sensors – year: 2007 ident: b43 article-title: Guidelines for performing systematic literature reviews in software engineering – volume: 143 year: 2022 ident: b52 article-title: Emotion recognition from EEG based on multi-task learning with capsule network and attention mechanism publication-title: Computers in Biology and Medicine – volume: 26 start-page: 5406 year: 2022 end-page: 5417 ident: b32 article-title: EEG-based emotion recognition using spatial-temporal graph convolutional LSTM with attention mechanism publication-title: IEEE Journal of Biomedical and Health Informatics – volume: 87 year: 2020 ident: b96 article-title: An EEG emotion recognition method based on transfer learning and echo state network for HilCPS publication-title: Microprocessors and Microsystems – start-page: 1 year: 2016 end-page: 8 ident: b1 article-title: Introduction to EEG- and speech-based emotion recognition – volume: 13 start-page: 885 year: 2021 end-page: 897 ident: b47 article-title: EEG emotion recognition based on 3-D feature representation and dilated fully convolutional networks publication-title: IEEE Transactions on Cognitive and Developmental Systems – volume: 7 start-page: 12177 year: 2019 end-page: 12191 ident: b80 article-title: MPED: A multi-modal physiological emotion database for discrete emotion recognition publication-title: IEEE Access – volume: 231 year: 2023 ident: b57 article-title: EEG emotion recognition using improved graph neural network with channel selection publication-title: Computer Methods and Programs in Biomedicine – volume: 12 start-page: 2311 year: 2020 end-page: 2320 ident: b69 article-title: Subject independent emotion recognition system for people with facial deformity: an EEG based approach publication-title: Journal of Ambient Intelligence and Humanized Computing – volume: 164 start-page: 177 year: 2020 end-page: 184 ident: b30 article-title: Multi-reservoirs EEG signal feature sensing and recognition method based on generative adversarial networks publication-title: Computer Communications – volume: 3 year: 2021 ident: b34 article-title: Singular learning of deep multilayer perceptrons for EEG-based emotion recognition publication-title: Frontiers in Computer Science – year: 2020 ident: b40 article-title: IDEA: Intellect database for emotion analysis using EEG signal publication-title: Journal of King Saud University - Computer and Information Sciences – volume: 2023 start-page: 1 year: 2023 end-page: 19 ident: b85 article-title: Extracting a novel emotional EEG topographic map based on a stacked autoencoder network publication-title: Journal of Healthcare Engineering – volume: 9 start-page: 329 year: 2018 end-page: 337 ident: b74 article-title: EEG-based emotion recognition using 3D convolutional neural networks publication-title: International Journal of Advanced Computer Science Applications – volume: 205 year: 2020 ident: b24 article-title: EEG-based emotion recognition using an end-to-end regional-asymmetric convolutional neural network publication-title: Knowledge-Based Systems – volume: 7 start-page: 143293 year: 2019 end-page: 143302 ident: b60 article-title: Emotion recognition and dynamic functional connectivity analysis based on EEG publication-title: IEEE Access – volume: 9 start-page: 95 year: 2020 ident: b5 article-title: Machine-learning-based emotion recognition system using EEG signals publication-title: Computers – volume: 16 year: 2019 ident: b23 article-title: Deep learning for electroencephalogram (EEG) classification tasks: a review publication-title: Journal of Neural Engineering – volume: 27 start-page: 363 year: 2023 end-page: 373 ident: b82 article-title: A novel domain adversarial networks based on 3D-LSTM and local domain discriminator for hearing-impaired emotion recognition publication-title: IEEE Journal of Biomedical and Health Informatics – volume: 2020 start-page: 1 year: 2020 end-page: 15 ident: b17 article-title: Improved deep feature learning by synchronization measurements for multi-channel EEG emotion recognition publication-title: Complexity – volume: 71 start-page: 1 year: 2022 end-page: 9 ident: b93 article-title: Siam-GCAN: A siamese graph convolutional attention network for EEG emotion recognition publication-title: IEEE Transactions on Instrumentation and Measurement – volume: 11 start-page: 683 year: 2019 ident: b12 article-title: Multiple transferable recursive feature elimination technique for emotion recognition based on EEG signals publication-title: Symmetry (Basel) – volume: 7 start-page: 40144 year: 2019 end-page: 40153 ident: b84 article-title: Internal emotion classification using EEG signal with sparse discriminative ensemble publication-title: IEEE Access – volume: 26 start-page: 264 year: 2022 end-page: 275 ident: b42 article-title: Wedea: A new EEG-based framework for emotion recognition publication-title: IEEE Journal of Biomedical and Health Informatics – volume: 119 start-page: 1 year: 2021 end-page: 6 ident: b59 article-title: Emotion recognition by deeply learned multi-channel textual and EEG features publication-title: Future Generation Computer Systems – volume: 24 start-page: 98 year: 2018 end-page: 106 ident: b15 article-title: EEG based emotion classification using “Correlation Based Subset Selection” publication-title: Biologically Inspired Cognitive Architectures – volume: 20 start-page: 2034 year: 2020 ident: b21 article-title: Investigating the use of pretrained convolutional neural network on cross-subject and cross-dataset EEG emotion recognition publication-title: Sensors – volume: 23 start-page: 1323 year: 2019 end-page: 1335 ident: b37 article-title: Learning CNN features from DE features for EEG-based emotion recognition publication-title: Pattern Analysis and Applications – volume: 159 year: 2023 ident: b79 article-title: Cross-subject EEG emotion recognition using multi-source domain manifold feature selection publication-title: Computers in Biology and Medicine – volume: 12 year: 2018 ident: b51 article-title: Exploring EEG features in cross-subject emotion recognition publication-title: Frontiers in Neuroscience – volume: 18 start-page: 2739 year: 2018 ident: b4 article-title: EEG-based emotion recognition using quadratic time-frequency distribution publication-title: Sensors – volume: 86 start-page: 1 year: 2018 end-page: 8 ident: b2 article-title: Anytime multipurpose emotion recognition from EEG data using a liquid state machine based framework publication-title: Artificial Intelligence in Medicine – volume: 10 start-page: 24520 year: 2022 end-page: 24527 ident: b75 article-title: Deep learning model with adaptive regularization for EEG-based emotion recognition using temporal and frequency features publication-title: IEEE Access – volume: 25 start-page: 1311 year: 2022 end-page: 1331 ident: b88 article-title: Hybrid hunt-based deep convolutional neural network for emotion recognition using EEG signals publication-title: Computer Methods in Biomechanics and Biomedical Engineering – volume: 10 year: 2022 ident: b27 article-title: Automated robust human emotion classification system using hybrid EEG features with ICBrainDB dataset publication-title: Health Information Science and Systems – volume: 21 start-page: 81 year: 2018 end-page: 89 ident: b66 article-title: Emotion recognition from EEG signals by using multivariate empirical mode decomposition publication-title: Pattern Analysis and Applications – volume: 9 start-page: 281 year: 2017 end-page: 290 ident: b94 article-title: Multichannel EEG-based emotion recognition via group sparse canonical correlation analysis publication-title: IEEE Transactions on Cognitive and Developmental Systems – volume: 8 start-page: 33002 year: 2020 end-page: 33012 ident: b18 article-title: Emotion recognition from multi-channel EEG signals by exploiting the deep belief-conditional random field framework publication-title: IEEE Access – volume: 11 start-page: 1 year: 2017 end-page: 16 ident: b92 article-title: Cross-subject EEG feature selection for emotion recognition using transfer recursive feature elimination publication-title: Frontiers in Neurorobotics – volume: 17 year: 2020 ident: b64 article-title: Data augmentation for enhancing EEG-based emotion recognition with deep generative models publication-title: Journal of Neural Engineering – volume: 21 start-page: 14931 year: 2021 end-page: 14940 ident: b77 article-title: Revealing preference in popular music through familiarity and brain response publication-title: IEEE Sensors Journal – volume: 154 start-page: 58 year: 2020 end-page: 65 ident: b19 article-title: Emotion recognition from spatiotemporal EEG representations with hybrid convolutional recurrent neural networks via wearable multi-channel headset publication-title: Computer Communications – volume: 86 start-page: 1983 year: 2001 end-page: 1990 ident: b70 article-title: Synchronization of neuronal activity in the human primary motor cortex by transcranial magnetic stimulation: An EEG study publication-title: Journal of Neurophysiology – start-page: 1 year: 2022 ident: b48 article-title: GMSS: Graph-based multi-task self-supervised learning for EEG emotion recognition publication-title: IEEE Transactions on Affective Computing – volume: 16 start-page: 605 year: 2002 end-page: 627 ident: b71 article-title: Respiratory feedback in the generation of emotion publication-title: Cognition and Emotion – volume: 13 start-page: 1528 year: 2022 end-page: 1540 ident: b31 article-title: An efficient LSTM network for emotion recognition from multichannel EEG signals publication-title: IEEE Transactions on Affective Computing – volume: 11 start-page: 1 year: 2017 end-page: 11 ident: b58 article-title: Improving EEG-based emotion classification using conditional transfer learning publication-title: Frontiers in Human Neuroscience – volume: 24 start-page: 98 issue: March year: 2018 ident: 10.1016/j.cogsys.2023.101152_b15 article-title: EEG based emotion classification using “Correlation Based Subset Selection” publication-title: Biologically Inspired Cognitive Architectures doi: 10.1016/j.bica.2018.04.012 – volume: 17 start-page: 1014 issue: 5 year: 2017 ident: 10.1016/j.cogsys.2023.101152_b13 article-title: A fast, efficient domain adaptation technique for cross-domain electroencephalography(EEG)-based emotion recognition publication-title: Sensors doi: 10.3390/s17051014 – volume: 7 start-page: 143303 year: 2019 ident: 10.1016/j.cogsys.2023.101152_b86 article-title: Channel selection method for EEG emotion recognition using normalized mutual information publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2944273 – volume: 79 year: 2023 ident: 10.1016/j.cogsys.2023.101152_b95 article-title: EEG emotion recognition based on TQWT-features and hybrid convolutional recurrent neural network publication-title: Biomedical Signal Processing and Control doi: 10.1016/j.bspc.2022.104211 – volume: 67 start-page: 607 issue: 8 year: 2009 ident: 10.1016/j.cogsys.2023.101152_b16 article-title: Short-term emotion assessment in a recall paradigm publication-title: International Journal of Human-Computer Studies doi: 10.1016/j.ijhcs.2009.03.005 – volume: 7 start-page: 18 issue: 1 year: 2020 ident: 10.1016/j.cogsys.2023.101152_b28 article-title: A comparative analysis of machine learning methods for emotion recognition using EEG and peripheral physiological signals publication-title: Journal of Big Data doi: 10.1186/s40537-020-00289-7 – start-page: 1 year: 2022 ident: 10.1016/j.cogsys.2023.101152_b48 article-title: GMSS: Graph-based multi-task self-supervised learning for EEG emotion recognition publication-title: IEEE Transactions on Affective Computing – volume: 25 start-page: 1311 issue: 12 year: 2022 ident: 10.1016/j.cogsys.2023.101152_b88 article-title: Hybrid hunt-based deep convolutional neural network for emotion recognition using EEG signals publication-title: Computer Methods in Biomechanics and Biomedical Engineering doi: 10.1080/10255842.2021.2007889 – volume: 23 start-page: 19 issue: January year: 2018 ident: 10.1016/j.cogsys.2023.101152_b81 article-title: Embodied responses to musical experience detected by human bio-feedback brain features in a geminoid augmented architecture publication-title: Biologically Inspired Cognitive Architectures doi: 10.1016/j.bica.2018.01.001 – year: 2022 ident: 10.1016/j.cogsys.2023.101152_b65 article-title: An improved Bi-LSTM EEG emotion recognition algorithm publication-title: Journal of Network Intelligence – volume: 27 start-page: 363 issue: 1 year: 2023 ident: 10.1016/j.cogsys.2023.101152_b82 article-title: A novel domain adversarial networks based on 3D-LSTM and local domain discriminator for hearing-impaired emotion recognition publication-title: IEEE Journal of Biomedical and Health Informatics doi: 10.1109/JBHI.2022.3212475 – volume: 26 start-page: 10115 issue: 19 year: 2022 ident: 10.1016/j.cogsys.2023.101152_b22 article-title: Channel selection and feature extraction on deep EEG classification using metaheuristic and welch PSD publication-title: Soft Computing doi: 10.1007/s00500-022-07413-0 – volume: 17 issue: 5 year: 2020 ident: 10.1016/j.cogsys.2023.101152_b64 article-title: Data augmentation for enhancing EEG-based emotion recognition with deep generative models publication-title: Journal of Neural Engineering doi: 10.1088/1741-2552/abb580 – start-page: 480 year: 2010 ident: 10.1016/j.cogsys.2023.101152_b76 article-title: Encyclopedia of machine learning – volume: 26 start-page: 327 issue: S1 year: 2018 ident: 10.1016/j.cogsys.2023.101152_b14 article-title: Multi-subject subspace alignment for non-stationary EEG-based emotion recognition publication-title: Technology and Health Care doi: 10.3233/THC-174739 – volume: 9 start-page: 191 issue: 4 year: 2021 ident: 10.1016/j.cogsys.2023.101152_b41 article-title: TheRobust EEG based emotion recognition using deep neural network publication-title: International Journal of Intelligent Systems and Applications in Engineering doi: 10.18201/ijisae.2021473639 – year: 2021 ident: 10.1016/j.cogsys.2023.101152_b9 article-title: CNN based efficient approach for emotion recognition publication-title: Journal of King Saud University - Computer and Information Sciences – volume: 24 start-page: 1442 issue: 6 year: 2021 ident: 10.1016/j.cogsys.2023.101152_b83 article-title: Emotion recognition based on EEG feature maps through deep learning network publication-title: Engineering Science and Technology, an International Journal doi: 10.1016/j.jestch.2021.03.012 – volume: 23 start-page: 1323 issue: 3 year: 2019 ident: 10.1016/j.cogsys.2023.101152_b37 article-title: Learning CNN features from DE features for EEG-based emotion recognition publication-title: Pattern Analysis and Applications doi: 10.1007/s10044-019-00860-w – volume: 19 start-page: 5218 issue: 23 year: 2019 ident: 10.1016/j.cogsys.2023.101152_b8 article-title: EEG-based multi-modal emotion recognition using bag of deep features: An optimal feature selection approach publication-title: Sensors doi: 10.3390/s19235218 – volume: 19 start-page: 6016 issue: 4 year: 2023 ident: 10.1016/j.cogsys.2023.101152_b54 article-title: EEG -based emotion recognition via transformer neural architecture search publication-title: IEEE Transactions on Industrial Informatics doi: 10.1109/TII.2022.3170422 – volume: 15 issue: 3 year: 2018 ident: 10.1016/j.cogsys.2023.101152_b63 article-title: A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update publication-title: Journal of Neural Engineering doi: 10.1088/1741-2552/aab2f2 – volume: 11 start-page: 1 issue: June year: 2017 ident: 10.1016/j.cogsys.2023.101152_b58 article-title: Improving EEG-based emotion classification using conditional transfer learning publication-title: Frontiers in Human Neuroscience – start-page: 004429 year: 2016 ident: 10.1016/j.cogsys.2023.101152_b11 article-title: A hybrid ICA-wavelet transform for automated artefact removal in EEG-based emotion recognition – volume: 55 year: 2020 ident: 10.1016/j.cogsys.2023.101152_b29 article-title: A machine learning model for emotion recognition from physiological signals publication-title: Biomedical Signal Processing and Control doi: 10.1016/j.bspc.2019.101646 – volume: 12 start-page: 2311 issue: 2 year: 2020 ident: 10.1016/j.cogsys.2023.101152_b69 article-title: Subject independent emotion recognition system for people with facial deformity: an EEG based approach publication-title: Journal of Ambient Intelligence and Humanized Computing doi: 10.1007/s12652-020-02338-8 – volume: 7 start-page: 40144 year: 2019 ident: 10.1016/j.cogsys.2023.101152_b84 article-title: Internal emotion classification using EEG signal with sparse discriminative ensemble publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2904400 – volume: 8 start-page: 12219 issue: 15 year: 2021 ident: 10.1016/j.cogsys.2023.101152_b6 article-title: BED: A new data set for EEG-based biometrics publication-title: IEEE Internet of Things Journal doi: 10.1109/JIOT.2021.3061727 – volume: 65 start-page: 79 year: 2021 ident: 10.1016/j.cogsys.2023.101152_b91 article-title: Speech stress recognition using semi-eager learning publication-title: Cognitive Systems Research doi: 10.1016/j.cogsys.2020.10.001 – volume: 154 start-page: 58 issue: 6 year: 2020 ident: 10.1016/j.cogsys.2023.101152_b19 article-title: Emotion recognition from spatiotemporal EEG representations with hybrid convolutional recurrent neural networks via wearable multi-channel headset publication-title: Computer Communications doi: 10.1016/j.comcom.2020.02.051 – volume: 152 year: 2023 ident: 10.1016/j.cogsys.2023.101152_b10 article-title: Emotion recognition with residual network driven by spatial-frequency characteristics of EEG recorded from hearing-impaired adults in response to video clips publication-title: Computers in Biology and Medicine doi: 10.1016/j.compbiomed.2022.106344 – volume: 16 start-page: 605 issue: 5 year: 2002 ident: 10.1016/j.cogsys.2023.101152_b71 article-title: Respiratory feedback in the generation of emotion publication-title: Cognition and Emotion doi: 10.1080/02699930143000392 – volume: 8 start-page: 33002 year: 2020 ident: 10.1016/j.cogsys.2023.101152_b18 article-title: Emotion recognition from multi-channel EEG signals by exploiting the deep belief-conditional random field framework publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2974009 – volume: 87 issue: October year: 2020 ident: 10.1016/j.cogsys.2023.101152_b96 article-title: An EEG emotion recognition method based on transfer learning and echo state network for HilCPS publication-title: Microprocessors and Microsystems – year: 2007 ident: 10.1016/j.cogsys.2023.101152_b43 – volume: 60 year: 2020 ident: 10.1016/j.cogsys.2023.101152_b3 article-title: Database for an emotion recognition system based on EEG signals and various computer games – GAMEEMO publication-title: Biomedical Signal Processing and Control doi: 10.1016/j.bspc.2020.101951 – volume: 2023 start-page: 1 year: 2023 ident: 10.1016/j.cogsys.2023.101152_b85 article-title: Extracting a novel emotional EEG topographic map based on a stacked autoencoder network publication-title: Journal of Healthcare Engineering doi: 10.1155/2023/9223599 – volume: 24 start-page: 107 issue: December 2017 year: 2018 ident: 10.1016/j.cogsys.2023.101152_b68 article-title: An embodied virtual agent platform for emotional stroop effect experiments: A proof of concept publication-title: Biologically Inspired Cognitive Architectures doi: 10.1016/j.bica.2018.04.011 – volume: 22 start-page: 5158 issue: 14 year: 2022 ident: 10.1016/j.cogsys.2023.101152_b36 article-title: A hybrid hand-crafted and deep neural spatio-temporal EEG features clustering framework for precise emotional status recognition publication-title: Sensors doi: 10.3390/s22145158 – volume: 143 year: 2022 ident: 10.1016/j.cogsys.2023.101152_b52 article-title: Emotion recognition from EEG based on multi-task learning with capsule network and attention mechanism publication-title: Computers in Biology and Medicine doi: 10.1016/j.compbiomed.2022.105303 – volume: 79 year: 2023 ident: 10.1016/j.cogsys.2023.101152_b67 article-title: A multiple frequency bands parallel spatial–temporal 3D deep residual learning framework for EEG-based emotion recognition publication-title: Biomedical Signal Processing and Control doi: 10.1016/j.bspc.2022.104141 – volume: 13 start-page: 1 issue: June year: 2019 ident: 10.1016/j.cogsys.2023.101152_b89 article-title: SAE+LSTM: A new framework for emotion recognition from multi-channel EEG publication-title: Frontiers in Neurorobotics – volume: 19 start-page: 2266 issue: 6 year: 2019 ident: 10.1016/j.cogsys.2023.101152_b35 article-title: Cross-subject emotion recognition using flexible analytic wavelet transform from EEG signals publication-title: IEEE Sensors Journal doi: 10.1109/JSEN.2018.2883497 – year: 2020 ident: 10.1016/j.cogsys.2023.101152_b40 article-title: IDEA: Intellect database for emotion analysis using EEG signal publication-title: Journal of King Saud University - Computer and Information Sciences – volume: 71 start-page: 1 year: 2022 ident: 10.1016/j.cogsys.2023.101152_b93 article-title: Siam-GCAN: A siamese graph convolutional attention network for EEG emotion recognition publication-title: IEEE Transactions on Instrumentation and Measurement – volume: 159 year: 2023 ident: 10.1016/j.cogsys.2023.101152_b79 article-title: Cross-subject EEG emotion recognition using multi-source domain manifold feature selection publication-title: Computers in Biology and Medicine doi: 10.1016/j.compbiomed.2023.106860 – volume: 9 start-page: 281 issue: 3 year: 2017 ident: 10.1016/j.cogsys.2023.101152_b94 article-title: Multichannel EEG-based emotion recognition via group sparse canonical correlation analysis publication-title: IEEE Transactions on Cognitive and Developmental Systems doi: 10.1109/TCDS.2016.2587290 – volume: 12 start-page: 53 issue: 1 year: 2022 ident: 10.1016/j.cogsys.2023.101152_b38 article-title: ICBrainDB: An integrated database for finding associations between genetic factors and EEG markers of depressive disorders publication-title: Journal of Personalized Medicine doi: 10.3390/jpm12010053 – start-page: 1 year: 2021 ident: 10.1016/j.cogsys.2023.101152_b53 article-title: EEG -based emotion recognition via neural architecture search publication-title: IEEE Transactions on Affective Computing – volume: 14 start-page: 1 issue: March year: 2020 ident: 10.1016/j.cogsys.2023.101152_b55 article-title: Latent factor decoding of multi-channel EEG for emotion recognition through autoencoder-like neural networks publication-title: Frontiers in Neuroscience – volume: 7 start-page: 12177 year: 2019 ident: 10.1016/j.cogsys.2023.101152_b80 article-title: MPED: A multi-modal physiological emotion database for discrete emotion recognition publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2891579 – volume: 19 start-page: 4736 issue: 21 year: 2019 ident: 10.1016/j.cogsys.2023.101152_b90 article-title: A multi-column CNN model for emotion recognition from EEG signals publication-title: Sensors doi: 10.3390/s19214736 – volume: 16 issue: 3 year: 2019 ident: 10.1016/j.cogsys.2023.101152_b23 article-title: Deep learning for electroencephalogram (EEG) classification tasks: a review publication-title: Journal of Neural Engineering doi: 10.1088/1741-2552/ab0ab5 – volume: 25 start-page: 453 issue: 2 year: 2021 ident: 10.1016/j.cogsys.2023.101152_b20 article-title: Emotion recognition from multi-channel EEG via deep forest publication-title: IEEE Journal of Biomedical and Health Informatics doi: 10.1109/JBHI.2020.2995767 – volume: 7 start-page: 143293 year: 2019 ident: 10.1016/j.cogsys.2023.101152_b60 article-title: Emotion recognition and dynamic functional connectivity analysis based on EEG publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2945059 – volume: 18 start-page: 2739 issue: 8 year: 2018 ident: 10.1016/j.cogsys.2023.101152_b4 article-title: EEG-based emotion recognition using quadratic time-frequency distribution publication-title: Sensors doi: 10.3390/s18082739 – volume: 86 start-page: 1 year: 2018 ident: 10.1016/j.cogsys.2023.101152_b2 article-title: Anytime multipurpose emotion recognition from EEG data using a liquid state machine based framework publication-title: Artificial Intelligence in Medicine doi: 10.1016/j.artmed.2018.01.001 – volume: 13 start-page: 1528 issue: 3 year: 2022 ident: 10.1016/j.cogsys.2023.101152_b31 article-title: An efficient LSTM network for emotion recognition from multichannel EEG signals publication-title: IEEE Transactions on Affective Computing doi: 10.1109/TAFFC.2020.3013711 – volume: 9 start-page: 329 issue: 8 year: 2018 ident: 10.1016/j.cogsys.2023.101152_b74 article-title: EEG-based emotion recognition using 3D convolutional neural networks publication-title: International Journal of Advanced Computer Science Applications doi: 10.14569/IJACSA.2018.090843 – volume: 84 year: 2023 ident: 10.1016/j.cogsys.2023.101152_b33 article-title: EEG emotion recognition using attention-based convolutional transformer neural network publication-title: Biomedical Signal Processing and Control doi: 10.1016/j.bspc.2023.104835 – volume: 86 start-page: 1983 issue: 4 year: 2001 ident: 10.1016/j.cogsys.2023.101152_b70 article-title: Synchronization of neuronal activity in the human primary motor cortex by transcranial magnetic stimulation: An EEG study publication-title: Journal of Neurophysiology doi: 10.1152/jn.2001.86.4.1983 – volume: 22 start-page: 19608 issue: 20 year: 2022 ident: 10.1016/j.cogsys.2023.101152_b50 article-title: EEG-based emotion recognition via efficient convolutional neural network and contrastive learning – volume: 10 start-page: 24520 year: 2022 ident: 10.1016/j.cogsys.2023.101152_b75 article-title: Deep learning model with adaptive regularization for EEG-based emotion recognition using temporal and frequency features publication-title: IEEE Access doi: 10.1109/ACCESS.2022.3155647 – volume: 164 start-page: 177 issue: October year: 2020 ident: 10.1016/j.cogsys.2023.101152_b30 article-title: Multi-reservoirs EEG signal feature sensing and recognition method based on generative adversarial networks publication-title: Computer Communications doi: 10.1016/j.comcom.2020.10.004 – volume: 3 year: 2021 ident: 10.1016/j.cogsys.2023.101152_b34 article-title: Singular learning of deep multilayer perceptrons for EEG-based emotion recognition publication-title: Frontiers in Computer Science doi: 10.3389/fcomp.2021.786964 – start-page: 1 year: 2016 ident: 10.1016/j.cogsys.2023.101152_b1 – volume: 11 start-page: 683 issue: 5 year: 2019 ident: 10.1016/j.cogsys.2023.101152_b12 article-title: Multiple transferable recursive feature elimination technique for emotion recognition based on EEG signals publication-title: Symmetry (Basel) doi: 10.3390/sym11050683 – volume: 119 start-page: 1 year: 2021 ident: 10.1016/j.cogsys.2023.101152_b59 article-title: Emotion recognition by deeply learned multi-channel textual and EEG features publication-title: Future Generation Computer Systems doi: 10.1016/j.future.2021.01.010 – volume: 13 start-page: 885 issue: 4 year: 2021 ident: 10.1016/j.cogsys.2023.101152_b47 article-title: EEG emotion recognition based on 3-D feature representation and dilated fully convolutional networks publication-title: IEEE Transactions on Cognitive and Developmental Systems doi: 10.1109/TCDS.2021.3051465 – volume: 10 issue: 1 year: 2022 ident: 10.1016/j.cogsys.2023.101152_b27 article-title: Automated robust human emotion classification system using hybrid EEG features with ICBrainDB dataset publication-title: Health Information Science and Systems doi: 10.1007/s13755-022-00201-y – volume: 26 start-page: 5406 issue: 11 year: 2022 ident: 10.1016/j.cogsys.2023.101152_b32 article-title: EEG-based emotion recognition using spatial-temporal graph convolutional LSTM with attention mechanism publication-title: IEEE Journal of Biomedical and Health Informatics doi: 10.1109/JBHI.2022.3198688 – volume: 52 start-page: 103 year: 2018 ident: 10.1016/j.cogsys.2023.101152_b45 article-title: Emotion perception and recognition: An exploration of cultural differences and similarities publication-title: Cognitive Systems Research doi: 10.1016/j.cogsys.2018.06.009 – volume: 54 start-page: 1 year: 2019 ident: 10.1016/j.cogsys.2023.101152_b25 article-title: Exploratory study of the effect of binaural beat stimulation on the EEG activity pattern in resting state using artificial neural networks publication-title: Cognitive Systems Research doi: 10.1016/j.cogsys.2018.11.002 – volume: 145 year: 2022 ident: 10.1016/j.cogsys.2023.101152_b49 article-title: Cross-subject EEG emotion recognition combined with connectivity features and meta-transfer learning publication-title: Computers in Biology and Medicine doi: 10.1016/j.compbiomed.2022.105519 – volume: 110 year: 2020 ident: 10.1016/j.cogsys.2023.101152_b87 article-title: A prototype-based SPD matrix network for domain adaptation EEG emotion recognition publication-title: Pattern Recognition – volume: 12 issue: MAR year: 2018 ident: 10.1016/j.cogsys.2023.101152_b51 article-title: Exploring EEG features in cross-subject emotion recognition publication-title: Frontiers in Neuroscience – volume: 26 start-page: 264 issue: 1 year: 2022 ident: 10.1016/j.cogsys.2023.101152_b42 article-title: Wedea: A new EEG-based framework for emotion recognition publication-title: IEEE Journal of Biomedical and Health Informatics doi: 10.1109/JBHI.2021.3091187 – volume: 3 start-page: 2 issue: 1 year: 2009 ident: 10.1016/j.cogsys.2023.101152_b44 article-title: Everything you wanted to ask about EEG but were afraid to get the right answer publication-title: Nonlinear Biomedical Physics doi: 10.1186/1753-4631-3-2 – volume: 85 year: 2023 ident: 10.1016/j.cogsys.2023.101152_b62 article-title: GLFANet: A global to local feature aggregation network for EEG emotion recognition publication-title: Biomedical Signal Processing and Control doi: 10.1016/j.bspc.2023.104799 – volume: 9 start-page: 95 issue: 4 year: 2020 ident: 10.1016/j.cogsys.2023.101152_b5 article-title: Machine-learning-based emotion recognition system using EEG signals publication-title: Computers doi: 10.3390/computers9040095 – volume: 21 start-page: 23 issue: 1 year: 2020 ident: 10.1016/j.cogsys.2023.101152_b7 article-title: Employing PCA and t-statistical approach for feature extraction and classification of emotion from multichannel EEG signal publication-title: Egyptian Informatics Journal doi: 10.1016/j.eij.2019.10.002 – volume: 20 start-page: 2034 issue: 7 year: 2020 ident: 10.1016/j.cogsys.2023.101152_b21 article-title: Investigating the use of pretrained convolutional neural network on cross-subject and cross-dataset EEG emotion recognition publication-title: Sensors doi: 10.3390/s20072034 – start-page: 497 year: 2009 ident: 10.1016/j.cogsys.2023.101152_b39 article-title: The research on emotion recognition from ECG signal – volume: 18 start-page: 1383 issue: 5 year: 2018 ident: 10.1016/j.cogsys.2023.101152_b46 article-title: Electroencephalography based fusion two-dimensional (2D)-convolution neural networks (CNN) model for emotion recognition system publication-title: Sensors doi: 10.3390/s18051383 – volume: 144 year: 2022 ident: 10.1016/j.cogsys.2023.101152_b26 article-title: EEG -based emotion charting for Parkinson's disease patients using convolutional recurrent neural networks and cross dataset learning publication-title: Computers in Biology and Medicine doi: 10.1016/j.compbiomed.2022.105327 – volume: 21 start-page: 81 issue: 1 year: 2018 ident: 10.1016/j.cogsys.2023.101152_b66 article-title: Emotion recognition from EEG signals by using multivariate empirical mode decomposition publication-title: Pattern Analysis and Applications doi: 10.1007/s10044-016-0567-6 – volume: 231 year: 2023 ident: 10.1016/j.cogsys.2023.101152_b57 article-title: EEG emotion recognition using improved graph neural network with channel selection publication-title: Computer Methods and Programs in Biomedicine doi: 10.1016/j.cmpb.2023.107380 – volume: 250 year: 2022 ident: 10.1016/j.cogsys.2023.101152_b72 article-title: Affect recognition from scalp-EEG using channel-wise encoder networks coupled with geometric deep learning and multi-channel feature fusion publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2022.109038 – volume: 58 year: 2020 ident: 10.1016/j.cogsys.2023.101152_b78 article-title: Automated emotion recognition based on higher order statistics and deep learning algorithm publication-title: Biomedical Signal Processing and Control doi: 10.1016/j.bspc.2020.101867 – volume: 74 year: 2022 ident: 10.1016/j.cogsys.2023.101152_b73 article-title: Learning DenseNet features from EEG based spectrograms for subject independent emotion recognition publication-title: Biomedical Signal Processing and Control doi: 10.1016/j.bspc.2022.103485 – volume: 205 year: 2020 ident: 10.1016/j.cogsys.2023.101152_b24 article-title: EEG-based emotion recognition using an end-to-end regional-asymmetric convolutional neural network publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2020.106243 – volume: 21 start-page: 14931 issue: 13 year: 2021 ident: 10.1016/j.cogsys.2023.101152_b77 article-title: Revealing preference in popular music through familiarity and brain response publication-title: IEEE Sensors Journal doi: 10.1109/JSEN.2021.3073040 – volume: 11 start-page: 1 issue: APR year: 2017 ident: 10.1016/j.cogsys.2023.101152_b92 article-title: Cross-subject EEG feature selection for emotion recognition using transfer recursive feature elimination publication-title: Frontiers in Neurorobotics – volume: 2020 start-page: 1 year: 2020 ident: 10.1016/j.cogsys.2023.101152_b17 article-title: Improved deep feature learning by synchronization measurements for multi-channel EEG emotion recognition publication-title: Complexity – volume: 26 start-page: 5964 issue: 12 year: 2022 ident: 10.1016/j.cogsys.2023.101152_b56 article-title: Dynamic domain adaptation for class-aware cross-subject and cross-session EEG emotion recognition publication-title: IEEE Journal of Biomedical and Health Informatics doi: 10.1109/JBHI.2022.3210158 – volume: 11 start-page: 517 issue: 4 year: 2019 ident: 10.1016/j.cogsys.2023.101152_b61 article-title: Electroencephalogram emotion recognition based on empirical mode decomposition and optimal feature selection publication-title: IEEE Transactions on Cognitive and Developmental Systems doi: 10.1109/TCDS.2018.2868121 |
| SSID | ssj0016952 |
| Score | 2.4896555 |
| Snippet | In this study, we conducted a systematic literature review of 107 primary studies conducted between 2017 and 2023 to discern trends in datasets, classifiers,... |
| SourceID | hal crossref elsevier |
| SourceType | Open Access Repository Enrichment Source Index Database Publisher |
| StartPage | 101152 |
| SubjectTerms | Artificial Intelligence Chemical and Process Engineering Classifier Computer Science Dataset EEG signals Emotion recognition Engineering Sciences Feature extraction Human-Computer Interaction Neural and Evolutionary Computing Research contribution |
| Title | A systematic literature review of emotion recognition using EEG signals |
| URI | https://dx.doi.org/10.1016/j.cogsys.2023.101152 https://hal-emse.ccsd.cnrs.fr/emse-04185401 |
| Volume | 82 |
| WOSCitedRecordID | wos001053403900001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1389-0417 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0016952 issn: 1389-0417 databaseCode: AIEXJ dateStart: 19991201 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3db9MwELfYxsNeJj7FGCBL8FZlamI7H4_RKCtoqpAY0LfIju2lW0mmfqzrf885dt1qBQ2QeIkitz63vl8u55_Pdwi9i2gpUqF5wEJJAyoIDTLZZUEsuCRM8yRrd8-_nSWDQTocZp_ddsG0LSeQ1HV6e5td_1dVQxso2xyd_Qt1e6HQAPegdLiC2uH6R4rPN7Mzj33W5I1DKsqW7un44CG4n7ecQa932jERHXw83fRaT3yMkRVttho2WDBjWScApkVLu75fjDrfebWce555fjFpqvazPq_nhoaRI8_sKDAxC8vCDppm0smvqh9cev9aCb4cO56_ckh2JEVE7gR8bJ-eaY0tMeFa1J7dPFa_aHMW2pYn2jL2lne4BF1dwL8_NuOaxtCmxL2TRvuLkWwEw5rLuMlkB-1FCcvAEu7lH3vDT37vKc7aOk3-l6wOXLZRgdtj_c6h2alW1Hzrqpw_QgdujYFzi43H6IGqn6B9_6pbPkWnOV6DBK9Bgi1IcKOxAwneAAluQYIBJNiB5Bn6-qF3ftIPXEmNoAR3ZRaoUiQkS7mmWiZSpCFToY7NorLkWcpKzYjmWugkE5pxxkkkaCa4YBJ6pmCsn6PduqnVC4QpoWnJeBQnIDoKpeiWkZaUJZoTKmJ5iMhqXorS5Zs3ZU_GxSqw8LKws1mY2SzsbB6iwPe6tvlW7vl-sprywvmM1hcsACX39HwLGvKDmDTr_fysgGdIFV2T0ol2w5vw5T_LP0L76yfhFdqdTebqNXpY3sxG08kbB7qfcYuhpQ |
| linkProvider | Elsevier |
| 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=A+systematic+literature+review+of+emotion+recognition+using+EEG+signals&rft.jtitle=Cognitive+systems+research&rft.au=Prabowo%2C+Dwi+Wahyu&rft.au=Nugroho%2C+Hanung+Adi&rft.au=Setiawan%2C+Noor+Akhmad&rft.au=Debayle%2C+Johan&rft.date=2023-12-01&rft.pub=Elsevier+B.V&rft.issn=1389-0417&rft.eissn=1389-0417&rft.volume=82&rft_id=info:doi/10.1016%2Fj.cogsys.2023.101152&rft.externalDocID=S1389041723000803 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1389-0417&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1389-0417&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1389-0417&client=summon |