Multi-Source Transfer Learning for EEG Classification Based on Domain Adversarial Neural Network
Electroencephalogram (EEG) classification has attracted great attention in recent years, and many models have been presented for this task. Nevertheless, EEG data vary from subject to subject, which may lead to the performance of a classifier degrades due to individual differences. To collect enough...
Saved in:
| Published in | IEEE transactions on neural systems and rehabilitation engineering Vol. 31; pp. 218 - 228 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
IEEE
2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | Electroencephalogram (EEG) classification has attracted great attention in recent years, and many models have been presented for this task. Nevertheless, EEG data vary from subject to subject, which may lead to the performance of a classifier degrades due to individual differences. To collect enough labeled data to model would address the issue, but it is often time-consuming and labor-intensive. In this paper, we propose a new multi-source transfer learning method based on domain adversarial neural network for EEG classification. Specifically, we design a domain adversarial neural network, which includes a feature extractor, a classifier, and a domain discriminator, and therefore reduce the domain shift to achieve the purpose. In addition, a unified multi-source optimization framework is constructed to further improve the performance, and the result for EEG classification is induced by the weighted combination of the predictions from multiple source domains. Experiments on three publicly available EEG datasets validate the advantages of the proposed method. |
|---|---|
| AbstractList | Electroencephalogram (EEG) classification has attracted great attention in recent years, and many models have been presented for this task. Nevertheless, EEG data vary from subject to subject, which may lead to the performance of a classifier degrades due to individual differences. To collect enough labeled data to model would address the issue, but it is often time-consuming and labor-intensive. In this paper, we propose a new multi-source transfer learning method based on domain adversarial neural network for EEG classification. Specifically, we design a domain adversarial neural network, which includes a feature extractor, a classifier, and a domain discriminator, and therefore reduce the domain shift to achieve the purpose. In addition, a unified multi-source optimization framework is constructed to further improve the performance, and the result for EEG classification is induced by the weighted combination of the predictions from multiple source domains. Experiments on three publicly available EEG datasets validate the advantages of the proposed method. Electroencephalogram (EEG) classification has attracted great attention in recent years, and many models have been presented for this task. Nevertheless, EEG data vary from subject to subject, which may lead to the performance of a classifier degrades due to individual differences. To collect enough labeled data to model would address the issue, but it is often time-consuming and labor-intensive. In this paper, we propose a new multi-source transfer learning method based on domain adversarial neural network for EEG classification. Specifically, we design a domain adversarial neural network, which includes a feature extractor, a classifier, and a domain discriminator, and therefore reduce the domain shift to achieve the purpose. In addition, a unified multi-source optimization framework is constructed to further improve the performance, and the result for EEG classification is induced by the weighted combination of the predictions from multiple source domains. Experiments on three publicly available EEG datasets validate the advantages of the proposed method.Electroencephalogram (EEG) classification has attracted great attention in recent years, and many models have been presented for this task. Nevertheless, EEG data vary from subject to subject, which may lead to the performance of a classifier degrades due to individual differences. To collect enough labeled data to model would address the issue, but it is often time-consuming and labor-intensive. In this paper, we propose a new multi-source transfer learning method based on domain adversarial neural network for EEG classification. Specifically, we design a domain adversarial neural network, which includes a feature extractor, a classifier, and a domain discriminator, and therefore reduce the domain shift to achieve the purpose. In addition, a unified multi-source optimization framework is constructed to further improve the performance, and the result for EEG classification is induced by the weighted combination of the predictions from multiple source domains. Experiments on three publicly available EEG datasets validate the advantages of the proposed method. |
| Author | Liu, Siwei Long, Jinyi Wu, Hanrui Liu, Dezheng Zhang, Jia |
| Author_xml | – sequence: 1 givenname: Dezheng surname: Liu fullname: Liu, Dezheng organization: College of Information Science and Technology, Jinan University, Guangzhou, China – sequence: 2 givenname: Jia orcidid: 0000-0002-6079-2818 surname: Zhang fullname: Zhang, Jia organization: College of Information Science and Technology, Jinan University, Guangzhou, China – sequence: 3 givenname: Hanrui orcidid: 0000-0003-3565-6635 surname: Wu fullname: Wu, Hanrui organization: College of Information Science and Technology, Jinan University, Guangzhou, China – sequence: 4 givenname: Siwei orcidid: 0000-0001-9188-5622 surname: Liu fullname: Liu, Siwei organization: College of Information Science and Technology, Jinan University, Guangzhou, China – sequence: 5 givenname: Jinyi orcidid: 0000-0001-6150-987X surname: Long fullname: Long, Jinyi email: jinyil@jnu.edu.cn organization: College of Information Science and Technology, Jinan University, Guangzhou, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36331634$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kU1v1DAQhiNURD_gD4CEInHpJYvHdhznWJalVFqKRJezmSTjyks2bu2Ein-P96M99MBpRqPnfcee9zQ7GvxAWfYW2AyA1R9X1zc_FjPOOJ8JDrUE_SI7gbLUBePAjra9kIUUnB1npzGuGYNKldWr7FgoIUAJeZL9-jb1oytu_BRaylcBh2gp5EvCMLjhNrc-5IvFZT7vMUZnXYuj80P-CSN1eWo--w26Ib_o_lCIGBz2-TVNYVfGBx9-v85eWuwjvTnUs-znl8Vq_rVYfr-8ml8si1YqGItGaMu7WgE2tZRtralTaJuaiEnFOQgNWHVMMl03HIUWRNJi2ZSCq67VXJxlV3vfzuPa3AW3wfDXeHRmN_Dh1mAYXduTYQigVCeqRlpZSaulTls5qMqWAmDrdb73ugv-fqI4mo2LLfU9DuSnaHgleCmBa5nQD8_QdTrlkH6aqCoFkCCWqPcHamo21D097zGHBOg90AYfYyBrWjfuTj0GdL0BZraRm13kZhu5OUSepPyZ9NH9v6J3e5EjoidBXYuagRD_AIPBtFU |
| CODEN | ITNSB3 |
| CitedBy_id | crossref_primary_10_3390_brainsci14070688 crossref_primary_10_1109_TNSRE_2024_3445115 crossref_primary_10_1109_TII_2024_3450010 crossref_primary_10_1088_1741_2552_ad5fbd crossref_primary_10_1080_10255842_2024_2404541 crossref_primary_10_1109_TIM_2024_3451593 crossref_primary_10_1016_j_artmed_2023_102738 crossref_primary_10_1088_1741_2552_ad1f7a crossref_primary_10_1088_1741_2552_ad5761 crossref_primary_10_1109_ACCESS_2024_3430838 crossref_primary_10_3390_app14062253 crossref_primary_10_1109_ACCESS_2024_3421569 crossref_primary_10_1016_j_jneumeth_2024_110332 crossref_primary_10_1109_ACCESS_2024_3403106 crossref_primary_10_1016_j_engappai_2025_110340 crossref_primary_10_1016_j_knosys_2023_111365 crossref_primary_10_1109_RBME_2024_3449790 crossref_primary_10_1007_s11517_024_03032_z |
| Cites_doi | 10.1109/RBME.2017.2656388 10.1007/s13755-019-0076-2 10.1016/j.neunet.2014.05.012 10.1145/1961189.1961199 10.1007/978-3-319-70093-9_57 10.1155/2018/9871603 10.1002/hbm.23730 10.3390/s19030551 10.1609/aaai.v30i1.10306 10.1109/TNSRE.2019.2923315 10.1109/JSEN.2021.3101684 10.1109/TBME.2019.2913914 10.1109/CAC.2018.8623637 10.1088/1741-2552/ac4430 10.1109/TBME.2017.2742541 10.1007/978-3-030-27526-6_49 10.1109/TNSRE.2020.2985996 10.1109/ICCV.2013.274 10.1145/3492804 10.1088/1741-2552/aace8c 10.1145/3391229 10.1016/j.bspc.2020.102172 10.1109/ACCESS.2019.2930958 10.2307/2283970 10.1109/TKDE.2017.2659740 10.1109/JSEN.2016.2519886 10.1016/S1388-2457(02)00057-3 10.1088/1741-2552/ab405f 10.1016/j.neucom.2017.10.013 10.1109/ICASSP.2018.8462115 10.1109/TCDS.2019.2949306 10.1109/TAFFC.2019.2916015 10.1109/TCDS.2018.2826840 10.1109/TCYB.2019.2904052 10.1109/TNSRE.2011.2116125 10.1109/MSP.2008.4408446 10.1109/WACV48630.2021.00134 10.1109/CVPR.2017.547 10.1109/TBCAS.2019.2929053 10.1109/MCI.2015.2501545 10.1109/TBME.2021.3115799 10.1111/j.2517-6161.1995.tb02031.x 10.1016/j.neuroimage.2007.01.051 10.1109/IWW-BCI.2019.8737345 10.1109/TKDE.2009.191 10.1109/TNNLS.2020.3010780 10.1109/TAFFC.2017.2714671 10.1109/TAFFC.2018.2885474 10.1007/978-0-387-45528-0 10.1088/1741-2552/ab0ab5 10.1109/TNSRE.2020.3006180 10.1109/TBME.2004.827088 10.1109/TNSRE.2020.2966749 10.1109/TKDE.2020.3014697 10.3156/jsoft.29.5_177_2 10.1109/CVPR.2018.00577 10.1007/978-0-387-78189-1_8 10.1109/RBME.2011.2172408 10.1088/1741-2560/4/2/R01 10.1088/1741-2552/ab64a0 10.1109/ICCV.2017.194 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023 |
| DBID | 97E ESBDL RIA RIE AAYXX CITATION CGR CUY CVF ECM EIF NPM 7QF 7QO 7QQ 7SC 7SE 7SP 7SR 7TA 7TB 7TK 7U5 8BQ 8FD F28 FR3 H8D JG9 JQ2 KR7 L7M L~C L~D NAPCQ P64 7X8 DOA |
| DOI | 10.1109/TNSRE.2022.3219418 |
| DatabaseName | IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE Xplore Open Access Journals IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Aluminium Industry Abstracts Biotechnology Research Abstracts Ceramic Abstracts Computer and Information Systems Abstracts Corrosion Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts Materials Business File Mechanical & Transportation Engineering Abstracts Neurosciences Abstracts Solid State and Superconductivity Abstracts METADEX Technology Research Database ANTE: Abstracts in New Technology & Engineering Engineering Research Database Aerospace Database Materials Research Database ProQuest Computer Science Collection Civil Engineering Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Nursing & Allied Health Premium Biotechnology and BioEngineering Abstracts MEDLINE - Academic DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Materials Research Database Civil Engineering Abstracts Aluminium Industry Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Mechanical & Transportation Engineering Abstracts Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Ceramic Abstracts Neurosciences Abstracts Materials Business File METADEX Biotechnology and BioEngineering Abstracts Computer and Information Systems Abstracts Professional Aerospace Database Nursing & Allied Health Premium Engineered Materials Abstracts Biotechnology Research Abstracts Solid State and Superconductivity Abstracts Engineering Research Database Corrosion Abstracts Advanced Technologies Database with Aerospace ANTE: Abstracts in New Technology & Engineering MEDLINE - Academic |
| DatabaseTitleList | MEDLINE MEDLINE - Academic Materials Research Database |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database – sequence: 4 dbid: RIE name: IEEE Electronic Library (IEL) url: https://proxy.k.utb.cz/login?url=https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Occupational Therapy & Rehabilitation |
| EISSN | 1558-0210 |
| EndPage | 228 |
| ExternalDocumentID | oai_doaj_org_article_0a1166d37b4f474f848ab92167f53112 36331634 10_1109_TNSRE_2022_3219418 9939013 |
| Genre | orig-research Research Support, Non-U.S. Gov't Journal Article |
| GrantInformation_xml | – fundername: Outstanding Youth Project of Guangdong Natural Science Foundation of China grantid: 2021B1515020076 funderid: 10.13039/501100003453 – fundername: National Natural Science Foundation of Guangdong, China grantid: 2019A1515012175; 2022A1515010468 funderid: 10.13039/501100003453 – fundername: Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization grantid: 2021B1212040007 – fundername: Fundamental Research Funds for the Central Universities, Jinan University grantid: 21621026; 21622326 funderid: 10.13039/501100004024 – fundername: National Natural Science Foundation of China grantid: 62206111; 62276115; 62106084 funderid: 10.13039/501100001809 |
| GroupedDBID | --- -~X 0R~ 29I 4.4 53G 5GY 5VS 6IK 97E AAFWJ AAJGR AASAJ AAWTH ABAZT ABVLG ACGFO ACGFS ACIWK ACPRK AENEX AETIX AFPKN AFRAH AGSQL AIBXA ALMA_UNASSIGNED_HOLDINGS BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD ESBDL F5P GROUPED_DOAJ HZ~ H~9 IFIPE IPLJI JAVBF LAI M43 O9- OCL OK1 P2P RIA RIE RNS AAYXX CITATION RIG CGR CUY CVF ECM EIF NPM 7QF 7QO 7QQ 7SC 7SE 7SP 7SR 7TA 7TB 7TK 7U5 8BQ 8FD F28 FR3 H8D JG9 JQ2 KR7 L7M L~C L~D NAPCQ P64 7X8 |
| ID | FETCH-LOGICAL-c461t-b38f2d961ab944c98ed6afb9ee046221381a7d04089b2a383ee4fa5b5326dc823 |
| IEDL.DBID | RIE |
| ISSN | 1534-4320 1558-0210 |
| IngestDate | Wed Aug 27 01:26:21 EDT 2025 Thu Jul 10 19:57:07 EDT 2025 Sun Jul 13 04:27:01 EDT 2025 Thu Apr 03 07:03:17 EDT 2025 Tue Jul 01 00:43:26 EDT 2025 Thu Apr 24 23:13:02 EDT 2025 Wed Aug 27 02:18:09 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Language | English |
| License | https://creativecommons.org/licenses/by-nc-nd/4.0 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c461t-b38f2d961ab944c98ed6afb9ee046221381a7d04089b2a383ee4fa5b5326dc823 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0001-9188-5622 0000-0002-6079-2818 0000-0001-6150-987X 0000-0003-3565-6635 |
| OpenAccessLink | https://proxy.k.utb.cz/login?url=https://ieeexplore.ieee.org/document/9939013 |
| PMID | 36331634 |
| PQID | 2771532840 |
| PQPubID | 85423 |
| PageCount | 11 |
| ParticipantIDs | proquest_miscellaneous_2732541284 doaj_primary_oai_doaj_org_article_0a1166d37b4f474f848ab92167f53112 proquest_journals_2771532840 ieee_primary_9939013 pubmed_primary_36331634 crossref_citationtrail_10_1109_TNSRE_2022_3219418 crossref_primary_10_1109_TNSRE_2022_3219418 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 20230000 2023-00-00 20230101 2023-01-01 |
| PublicationDateYYYYMMDD | 2023-01-01 |
| PublicationDate_xml | – year: 2023 text: 20230000 |
| PublicationDecade | 2020 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States – name: New York |
| PublicationTitle | IEEE transactions on neural systems and rehabilitation engineering |
| PublicationTitleAbbrev | TNSRE |
| PublicationTitleAlternate | IEEE Trans Neural Syst Rehabil Eng |
| PublicationYear | 2023 |
| Publisher | IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher_xml | – name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| References | ref13 ref57 ref12 ref56 ref15 ref59 ref14 ref58 ref53 ref52 ref11 ref55 ref10 ref54 ref17 ref16 ref19 ref18 Schlögl (ref50) 2005 ref51 ref46 ref45 ref48 ref47 ref41 ref44 ref43 ref49 Kingma (ref61) 2014 ref8 ref7 ref4 ref3 ref6 ref5 ref40 ref35 ref34 ref37 ref36 ref31 ref30 ref33 ref32 ref2 ref1 Maaten (ref66) 2008; 9 ref39 ref38 Zheng (ref9) ref24 ref23 ref26 ref25 ref20 ref64 ref63 ref22 ref21 ref65 ref28 ref27 ref29 Demir (ref42) 2018 ref60 ref62 |
| References_xml | – ident: ref2 doi: 10.1109/RBME.2017.2656388 – ident: ref10 doi: 10.1007/s13755-019-0076-2 – ident: ref12 doi: 10.1016/j.neunet.2014.05.012 – ident: ref60 doi: 10.1145/1961189.1961199 – volume: 9 start-page: 2579 year: 2008 ident: ref66 article-title: Visualizing datausing t-SNE publication-title: J. Mach. Learn. Res. – ident: ref39 doi: 10.1007/978-3-319-70093-9_57 – ident: ref28 doi: 10.1155/2018/9871603 – ident: ref45 doi: 10.1002/hbm.23730 – ident: ref47 doi: 10.3390/s19030551 – ident: ref57 doi: 10.1609/aaai.v30i1.10306 – ident: ref29 doi: 10.1109/TNSRE.2019.2923315 – ident: ref52 doi: 10.1109/JSEN.2021.3101684 – ident: ref56 doi: 10.1109/TBME.2019.2913914 – ident: ref53 doi: 10.1109/CAC.2018.8623637 – ident: ref54 doi: 10.1088/1741-2552/ac4430 – ident: ref30 doi: 10.1109/TBME.2017.2742541 – ident: ref40 doi: 10.1007/978-3-030-27526-6_49 – ident: ref11 doi: 10.1109/TNSRE.2020.2985996 – ident: ref58 doi: 10.1109/ICCV.2013.274 – ident: ref22 doi: 10.1145/3492804 – ident: ref26 doi: 10.1088/1741-2552/aace8c – ident: ref24 doi: 10.1145/3391229 – ident: ref32 doi: 10.1016/j.bspc.2020.102172 – ident: ref38 doi: 10.1109/ACCESS.2019.2930958 – ident: ref64 doi: 10.2307/2283970 – volume-title: Dataset IIIB: Non-stationary 2-class BCI data year: 2005 ident: ref50 – ident: ref48 doi: 10.1109/TKDE.2017.2659740 – ident: ref6 doi: 10.1109/JSEN.2016.2519886 – ident: ref1 doi: 10.1016/S1388-2457(02)00057-3 – year: 2014 ident: ref61 article-title: Adam: A method for stochastic optimization publication-title: arXiv:1412.6980 – start-page: 2732 volume-title: Proc. 25th Int. Joint Conf. Artif. Intell. ident: ref9 article-title: Personalizing EEG-based affective models with transfer learning – ident: ref46 doi: 10.1088/1741-2552/ab405f – ident: ref31 doi: 10.1016/j.neucom.2017.10.013 – ident: ref41 doi: 10.1109/ICASSP.2018.8462115 – ident: ref15 doi: 10.1109/TCDS.2019.2949306 – ident: ref37 doi: 10.1109/TAFFC.2019.2916015 – ident: ref13 doi: 10.1109/TCDS.2018.2826840 – ident: ref14 doi: 10.1109/TCYB.2019.2904052 – ident: ref3 doi: 10.1109/TNSRE.2011.2116125 – ident: ref5 doi: 10.1109/MSP.2008.4408446 – ident: ref20 doi: 10.1109/WACV48630.2021.00134 – ident: ref59 doi: 10.1109/CVPR.2017.547 – ident: ref35 doi: 10.1109/TBCAS.2019.2929053 – year: 2018 ident: ref42 article-title: Patch-based image inpainting with generative adversarial networks publication-title: arXiv:1803.07422 – ident: ref8 doi: 10.1109/MCI.2015.2501545 – ident: ref62 doi: 10.1109/TBME.2021.3115799 – ident: ref65 doi: 10.1111/j.2517-6161.1995.tb02031.x – ident: ref51 doi: 10.1016/j.neuroimage.2007.01.051 – ident: ref17 doi: 10.1109/IWW-BCI.2019.8737345 – ident: ref25 doi: 10.1109/TKDE.2009.191 – ident: ref16 doi: 10.1109/TNNLS.2020.3010780 – ident: ref34 doi: 10.1109/TAFFC.2017.2714671 – ident: ref19 doi: 10.1109/TAFFC.2018.2885474 – ident: ref27 doi: 10.1007/978-0-387-45528-0 – ident: ref33 doi: 10.1088/1741-2552/ab0ab5 – ident: ref18 doi: 10.1109/TNSRE.2020.3006180 – ident: ref49 doi: 10.1109/TBME.2004.827088 – ident: ref63 doi: 10.1109/TNSRE.2020.2966749 – ident: ref23 doi: 10.1109/TKDE.2020.3014697 – ident: ref21 doi: 10.3156/jsoft.29.5_177_2 – ident: ref43 doi: 10.1109/CVPR.2018.00577 – ident: ref55 doi: 10.1007/978-0-387-78189-1_8 – ident: ref4 doi: 10.1109/RBME.2011.2172408 – ident: ref7 doi: 10.1088/1741-2560/4/2/R01 – ident: ref36 doi: 10.1088/1741-2552/ab64a0 – ident: ref44 doi: 10.1109/ICCV.2017.194 |
| SSID | ssj0017657 |
| Score | 2.49404 |
| Snippet | Electroencephalogram (EEG) classification has attracted great attention in recent years, and many models have been presented for this task. Nevertheless, EEG... |
| SourceID | doaj proquest pubmed crossref ieee |
| SourceType | Open Website Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 218 |
| SubjectTerms | adversarial learning Brain modeling Brain-computer interfaces Classification Classifiers Deep learning Domains EEG electroencephalogram Electroencephalography Feature extraction Generative adversarial networks Humans Learning Machine Learning multi-source fusion Neural networks Neural Networks, Computer Optimization Performance enhancement Training Transfer learning |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1NT9wwEB1VnHqpaCklhVauVLhUgcR27PhYYCmqBAdYJG6u49hVpZKtYPn_nXG80fZAe-kpUdaxsp43ejP-mAfw0aETVb7xpZG-KqXmsUTcyLJytC7EG1EFmhq4uFTnN_LrbXO7JvVFe8LG8sDjwB1Vrq6V6oXuZJRaxla2rjO8VjoifJK-MEfOWyVTef1Aq0avjshU5mh-eX01w2SQ80OBLipJ4mONhlK1_iyv8nSkmRjnbBNe5FCRfR4_8SU8C8Mr2F8vC8zmY00AdsCu_qi4vQXf0sna8jrNzbPESDHcs1xO9TvDWJXNZl9YEsWk7ULpPXaMpNYzvDld3LkfA0t6zQ-OUMqojke6pI3jr-HmbDY_OS-zmkLppaqXZSfayHujahw-Kb1pQ69c7EwIdD6V10jdTvfo063puMPENQQZXdM1GOD1vuViGzaGxRB2gEV8inlWh33VlF5jSBOU9k6iYYz2sYB6NbjW5z9Oihc_bUo5KmOTQSwZxGaDFPBpeufXWGjjr62PyWZTSyqSnR4gdGyGjv0XdArYIotPnWCohtGRKGBvhQCbHfrBcq2RG5DLqwI-TD-jK9L6ihvC4pHaILglEX4Bb0bkTH0LJQSGvvLt__jwXXhOqvfjTNAebCzvH8M7jI2W3fvkBr8BAEcFEw priority: 102 providerName: Directory of Open Access Journals |
| Title | Multi-Source Transfer Learning for EEG Classification Based on Domain Adversarial Neural Network |
| URI | https://ieeexplore.ieee.org/document/9939013 https://www.ncbi.nlm.nih.gov/pubmed/36331634 https://www.proquest.com/docview/2771532840 https://www.proquest.com/docview/2732541284 https://doaj.org/article/0a1166d37b4f474f848ab92167f53112 |
| Volume | 31 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1Lb9QwEB61PXGBQnmklMpIwAWy9St2cqSwpUJqD-1W6s3Yjl0hIIva3Qu_nrHzEEWAOCVKHO9aM-P5Zmx_A_DCohFRX_mykZ6WUvNYot7Iktq0LsQrQUNKDZycquML-fGyutyAN9NZmBBC3nwWZuk2r-W3S79OqbID9KXovsQmbGLg1p_VmlYMtMqsnmjAspSC0_GADG0OFqfnZ3MMBTmfCTRQyVKRPqGEQCwib_mjTNs_1Fn5O-TMrufoHpyMf7rfcfJltl65mf_xG5_j_45qG-4OGJS87ZXmPmyE7gG8_JVvmCx6sgHyipzdovLegU_5yG55npP-JLu6GK7JwNN6RRAEk_n8A8nVNtM-pPwdOURv2RK8eb_8Zj93JBeCvrFJ_UkiCMmXvCP9IVwczRfvjsuhTEPppWKr0ok68rZRzLpGSt_UoVU2uiaEdPCVM8QEVrc4WdSN4xYj4hBktJWrEDm2vubiEWx1yy48ARLxKQZwDvtiKW5HrBSU9lY6RHHaxwLYKCzjh4GnUhpfTY5laGOyrE2StRlkXcDr6ZvvPYPHP1sfJh2YWib27fwARWYGYzbUMqZUK7STUWoZa1nj2DlTOuKUxngBO0nMUyeDhAvYGzXKDDPFjeFao84iSKAFPJ9eo42nhRvbheU6tUGrkQlJFPC418Sp71GPd__8m0_hDg5P9EmjPdhaXa_DM4RRK7ef0w_72Yp-AkJWFHo |
| linkProvider | IEEE |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1Lb9QwEB6VcoALr_IIFDAScIFs40fs5EhhywLdPbRbqTdjOzZCQBa1uxd-PWPnIYoAcUqUON61Zsbzje35BuCpQSMqXOnyWrgiF4qFHPVG5IWJ-0Ks5IWPSwPzhZydiPen5ekWvBxzYbz36fCZn8TbtJffrNwmLpXtoS9F98UvwWX0-yXrsrXGPQMlE68nmrDIBWfFkCJT1HvLxfHRFINBxiYcTVTQWKaPS84RjYgLHikR9_eVVv4OOpPzObgO8-Fvd2dOvkw2aztxP35jdPzfcd2Aaz0KJa86tbkJW769Bc9-ZRwmy45ugDwnRxfIvHfgY0razY_Tsj9Jzi74M9IztX4iCIPJdPqWpHqb8SRS-o7so79sCN68WX0zn1uSSkGfm2gAJFKEpEs6k34bTg6my9ezvC_UkDsh6Tq3vAqsqSU1thbC1ZVvpAm29j6mvjKKqMCoBqeLqrbMYEzsvQimtCVix8ZVjN-B7XbV-ntAAj7FEM5iXzRG7oiWvFTOCIs4TrmQAR2EpV0_8FhM46tO0UxR6yRrHWWte1ln8GL85nvH4fHP1vtRB8aWkX87PUCR6d6cdWEolbLhyooglAiVqHDsjEoVcFKjLIOdKOaxk17CGewOGqX7ueJcM6VQZxEmFBk8GV-jlcetG9P61Sa2QbsREUtkcLfTxLHvQY_v__k3H8OV2XJ-qA_fLT48gKs4VN4tIe3C9vps4x8iqFrbR8mWfgJNFBbP |
| 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=Multi-Source+Transfer+Learning+for+EEG+Classification+Based+on+Domain+Adversarial+Neural+Network&rft.jtitle=IEEE+transactions+on+neural+systems+and+rehabilitation+engineering&rft.au=Liu%2C+Dezheng&rft.au=Zhang%2C+Jia&rft.au=Wu%2C+Hanrui&rft.au=Liu%2C+Siwei&rft.date=2023&rft.pub=IEEE&rft.issn=1534-4320&rft.volume=31&rft.spage=218&rft.epage=228&rft_id=info:doi/10.1109%2FTNSRE.2022.3219418&rft_id=info%3Apmid%2F36331634&rft.externalDocID=9939013 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1534-4320&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1534-4320&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1534-4320&client=summon |