SMMTM: Motor imagery EEG decoding algorithm using a hybrid multi-branch separable convolutional self-attention temporal convolutional network

Motor imagery (MI) is a brain-computer interface (BCI) technology with the potential to change human life in the future. MI signals have been widely applied in various BCI applications, including neurorehabilitation, smart home control, and prosthetic control. However, the limited accuracy of MI sig...

Full description

Saved in:

Bibliographic Details
Published in:	PloS one Vol. 20; no. 10; p. e0333805
Main Authors:	Cao, DianGuo, Yu, ZhenYuan, Wang, Jinqiang, Wu, Yuqiang
Format:	Journal Article
Language:	English
Published:	United States Public Library of Science 23.10.2025 Public Library of Science (PLoS)
Subjects:	Accuracy Algorithms Analysis Biochips Brain Brain research Brain-Computer Interfaces Classification Computational linguistics Computer applications Convolution Datasets Decoding Deep learning Electroencephalography - methods Human-computer interface Humans Imagery Imagination - physiology Implants Language processing Mental task performance Natural language interfaces Neural networks Neural Networks, Computer Neurology Prostheses Rehabilitation Smart buildings Technology application Temporal variations China
ISSN:	1932-6203, 1932-6203
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	Motor imagery (MI) is a brain-computer interface (BCI) technology with the potential to change human life in the future. MI signals have been widely applied in various BCI applications, including neurorehabilitation, smart home control, and prosthetic control. However, the limited accuracy of MI signals decoding remains a significant barrier to the broader growth of the BCI applications. In this study, we propose the SMMTM model, which combines spatiotemporal convolution (SC), multi-branch separable convolution (MSC), multi-head self-attention (MSA), temporal convolution network (TCN), and multimodal feature fusion (MFF). Specifically, we use the SC module to capture both temporal and spatial features. We design a MSC to capture temporal features at multiple scales. In addition, MSA is designed to extract valuable global features with long-term dependence. The TCN is employed to capture higher-level temporal features. The MFF consists of feature fusion and decision fusion, using the features output from the SMMTM to improve robustness. The SMMTM was evaluated on the public benchmark BCI Comparison IV 2a and 2b datasets, the results showed that the within-subject classification accuracies for the datasets were 84.96% and 89.26% respectively, with kappa values of 0.797 and 0.756. The cross-subject classification accuracy for the 2a dataset was 69.21%, with a kappa value of 0.584. These results indicate that the SMMTM significantly enhances decoding performance, providing a strong foundation for advancing practical BCI implementations.
AbstractList	Motor imagery (MI) is a brain-computer interface (BCI) technology with the potential to change human life in the future. MI signals have been widely applied in various BCI applications, including neurorehabilitation, smart home control, and prosthetic control. However, the limited accuracy of MI signals decoding remains a significant barrier to the broader growth of the BCI applications. In this study, we propose the SMMTM model, which combines spatiotemporal convolution (SC), multi-branch separable convolution (MSC), multi-head self-attention (MSA), temporal convolution network (TCN), and multimodal feature fusion (MFF). Specifically, we use the SC module to capture both temporal and spatial features. We design a MSC to capture temporal features at multiple scales. In addition, MSA is designed to extract valuable global features with long-term dependence. The TCN is employed to capture higher-level temporal features. The MFF consists of feature fusion and decision fusion, using the features output from the SMMTM to improve robustness. The SMMTM was evaluated on the public benchmark BCI Comparison IV 2a and 2b datasets, the results showed that the within-subject classification accuracies for the datasets were 84.96% and 89.26% respectively, with kappa values of 0.797 and 0.756. The cross-subject classification accuracy for the 2a dataset was 69.21%, with a kappa value of 0.584. These results indicate that the SMMTM significantly enhances decoding performance, providing a strong foundation for advancing practical BCI implementations. Motor imagery (MI) is a brain-computer interface (BCI) technology with the potential to change human life in the future. MI signals have been widely applied in various BCI applications, including neurorehabilitation, smart home control, and prosthetic control. However, the limited accuracy of MI signals decoding remains a significant barrier to the broader growth of the BCI applications. In this study, we propose the SMMTM model, which combines spatiotemporal convolution (SC), multi-branch separable convolution (MSC), multi-head self-attention (MSA), temporal convolution network (TCN), and multimodal feature fusion (MFF). Specifically, we use the SC module to capture both temporal and spatial features. We design a MSC to capture temporal features at multiple scales. In addition, MSA is designed to extract valuable global features with long-term dependence. The TCN is employed to capture higher-level temporal features. The MFF consists of feature fusion and decision fusion, using the features output from the SMMTM to improve robustness. The SMMTM was evaluated on the public benchmark BCI Comparison IV 2a and 2b datasets, the results showed that the within-subject classification accuracies for the datasets were 84.96% and 89.26% respectively, with kappa values of 0.797 and 0.756. The cross-subject classification accuracy for the 2a dataset was 69.21%, with a kappa value of 0.584. These results indicate that the SMMTM significantly enhances decoding performance, providing a strong foundation for advancing practical BCI implementations.Motor imagery (MI) is a brain-computer interface (BCI) technology with the potential to change human life in the future. MI signals have been widely applied in various BCI applications, including neurorehabilitation, smart home control, and prosthetic control. However, the limited accuracy of MI signals decoding remains a significant barrier to the broader growth of the BCI applications. In this study, we propose the SMMTM model, which combines spatiotemporal convolution (SC), multi-branch separable convolution (MSC), multi-head self-attention (MSA), temporal convolution network (TCN), and multimodal feature fusion (MFF). Specifically, we use the SC module to capture both temporal and spatial features. We design a MSC to capture temporal features at multiple scales. In addition, MSA is designed to extract valuable global features with long-term dependence. The TCN is employed to capture higher-level temporal features. The MFF consists of feature fusion and decision fusion, using the features output from the SMMTM to improve robustness. The SMMTM was evaluated on the public benchmark BCI Comparison IV 2a and 2b datasets, the results showed that the within-subject classification accuracies for the datasets were 84.96% and 89.26% respectively, with kappa values of 0.797 and 0.756. The cross-subject classification accuracy for the 2a dataset was 69.21%, with a kappa value of 0.584. These results indicate that the SMMTM significantly enhances decoding performance, providing a strong foundation for advancing practical BCI implementations.
Audience	Academic
Author	Yu, ZhenYuan Wu, Yuqiang Wang, Jinqiang Cao, DianGuo
Author_xml	– sequence: 1 givenname: DianGuo orcidid: 0000-0002-6179-2991 surname: Cao fullname: Cao, DianGuo – sequence: 2 givenname: ZhenYuan surname: Yu fullname: Yu, ZhenYuan – sequence: 3 givenname: Jinqiang surname: Wang fullname: Wang, Jinqiang – sequence: 4 givenname: Yuqiang surname: Wu fullname: Wu, Yuqiang
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/41129590$$D View this record in MEDLINE/PubMed
BookMark	eNqNk11v0zAUhiM0xD7gHyCIhITgIsUfiZtwN01jVFo1iQ1ureOPtC5OXGwH6I_gP-Ou2bSiXaBcOH79-Jzj1z7H2UHvep1lLzGaYDrFH1Zu8D3YyTrJE0QprVH1JDvCDSUFI4gePPg_zI5DWCFU0ZqxZ9lhiTFpqgYdZX-u5_Ob-cd87qLzuelgof0mPz-_yJWWTpl-kYNdOG_issuHcDvPlxvhjcq7wUZTCA-9XOZBr8GDsDqXrv_p7BCNS-Ul3bYFxKj7rZBH3a2dT_o-1ev4y_nvz7OnLdigX4zjSfb10_nN2efi8upidnZ6WciqrmNBFCKSgcSa4obVQKASpG6xSKtkKoFUjdAM11pU6aSYSZAtY1IBRVMiJNCT7PUu7tq6wEcnA6eEldW0Rg1LxGxHKAcrvvbJGb_hDgy_FZxfcPDRSKu5aDGhSirVYFI2ohVTkEpgLVIlbStFivVuzObdj0GHyDsTpLYWeu2GXVpWIcTKhL75B328uJFaQMpv-tZFD3IblJ_WLBF1VTaJmjxCpU_pziT7dWuSvrfh_d6GxET9Oy5gCIHPrr_8P3v1bZ99-4BdarBxGca7D_vgq_H0g-i0urf97rkmoNwB0rsQvG7vEYz4tivu7OLbruBjV9C_ymoBEg
Cites_doi	10.1109/TNSRE.2023.3237375 10.1109/SMC.2019.8914544 10.1016/j.cmpb.2020.105464 10.1109/TII.2022.3197419 10.1109/ACCESS.2019.2934018 10.1007/978-3-319-74060-7_9 10.1016/S1388-2457(02)00057-3 10.1007/s13246-020-00897-w 10.7717/peerj-cs.375 10.1109/ACCESS.2024.3351204 10.36227/techrxiv.24003582 10.1088/1741-2552/ab0ab5 10.1161/STROKEAHA.116.016304 10.1002/hbm.23730 10.1109/SMC.2018.00185 10.1016/j.neuroscience.2020.04.006 10.3390/app122111255 10.1016/j.bspc.2022.103718 10.1109/ANDESCON.2016.7836266 10.1007/s42979-024-02773-w 10.1109/TNSRE.2022.3194600 10.1109/TNSRE.2023.3242280 10.1109/ICCP.2017.8116986 10.1016/j.jneumeth.2023.109953 10.1016/j.bspc.2021.102826 10.1007/978-3-030-51935-3_11 10.1109/TNSRE.2024.3382226 10.1109/TITB.2006.879600 10.1109/SMC.2018.00178 10.3390/s20123496 10.1109/SMC42975.2020.9283028 10.1109/JBHI.2015.2450196 10.1016/j.future.2019.06.027 10.1109/TNSRE.2004.834627 10.1142/S0129065723500685 10.1109/LSP.2019.2906824 10.1016/j.eswa.2018.08.031 10.1109/ICIEA.2019.8834381 10.3389/fnbot.2024.1343249 10.1016/j.bspc.2022.104066 10.1109/JBHI.2020.2967128 10.1088/1741-2552/aace8c 10.1109/TNSRE.2024.3351863 10.1109/JBHI.2023.3243698 10.1109/TAFFC.2022.3169001 10.1109/TNSRE.2023.3323325 10.3390/s19132854
ContentType	Journal Article
Copyright	Copyright: © 2025 Cao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. COPYRIGHT 2025 Public Library of Science 2025 Cao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2025 Cao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml	– notice: Copyright: © 2025 Cao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. – notice: COPYRIGHT 2025 Public Library of Science – notice: 2025 Cao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2025 Cao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID	AAYXX CITATION CGR CUY CVF ECM EIF NPM IOV ISR 3V. 7QG 7QL 7QO 7RV 7SN 7SS 7T5 7TG 7TM 7U9 7X2 7X7 7XB 88E 8AO 8C1 8FD 8FE 8FG 8FH 8FI 8FJ 8FK ABJCF ABUWG AEUYN AFKRA ARAPS ATCPS AZQEC BBNVY BENPR BGLVJ BHPHI C1K CCPQU D1I DWQXO FR3 FYUFA GHDGH GNUQQ H94 HCIFZ K9. KB. KB0 KL. L6V LK8 M0K M0S M1P M7N M7P M7S NAPCQ P5Z P62 P64 PATMY PDBOC PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS PTHSS PYCSY RC3 7X8 DOA
DOI	10.1371/journal.pone.0333805
DatabaseName	CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Gale In Context: Opposing Viewpoints Gale In Context: Science ProQuest Central (Corporate) Animal Behavior Abstracts Bacteriology Abstracts (Microbiology B) Biotechnology Research Abstracts Nursing & Allied Health Database Ecology Abstracts Entomology Abstracts (Full archive) Immunology Abstracts Meteorological & Geoastrophysical Abstracts Nucleic Acids Abstracts Virology and AIDS Abstracts Agricultural Science Collection Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) ProQuest Pharma Collection Public Health Database Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) Materials Science & Engineering Collection ProQuest Central (Alumni Edition) ProQuest One Sustainability ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection Agricultural & Environmental Science Collection ProQuest Central Essentials Biological Science Collection ProQuest Central Technology Collection Natural Science Collection Environmental Sciences and Pollution Management ProQuest One Community College ProQuest Materials Science Collection ProQuest Central Korea Engineering Research Database Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student AIDS and Cancer Research Abstracts SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) Materials Science Database Nursing & Allied Health Database (Alumni Edition) Meteorological & Geoastrophysical Abstracts - Academic ProQuest Engineering Collection ProQuest Biological Science Collection Agricultural Science Database Health & Medical Collection (Alumni) Medical Database Algology Mycology and Protozoology Abstracts (Microbiology C) Biological Science Database Engineering Database Nursing & Allied Health Premium Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts Environmental Science Database Materials Science Collection ProQuest One Academic ProQuest One Academic (New) Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China Engineering Collection Environmental Science Collection Genetics Abstracts MEDLINE - Academic DOAJ Directory of Open Access Journals (WRLC)
DatabaseTitle	CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Agricultural Science Database Publicly Available Content Database ProQuest Central Student ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials Nucleic Acids Abstracts SciTech Premium Collection ProQuest Central China Environmental Sciences and Pollution Management ProQuest One Applied & Life Sciences ProQuest One Sustainability Health Research Premium Collection Meteorological & Geoastrophysical Abstracts Natural Science Collection Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) Engineering Collection Advanced Technologies & Aerospace Collection Engineering Database Virology and AIDS Abstracts ProQuest Biological Science Collection ProQuest One Academic Eastern Edition Agricultural Science Collection ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Biological Science Database Ecology Abstracts ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts Environmental Science Collection Entomology Abstracts Nursing & Allied Health Premium ProQuest Health & Medical Complete ProQuest One Academic UKI Edition Environmental Science Database ProQuest Nursing & Allied Health Source (Alumni) Engineering Research Database ProQuest One Academic Meteorological & Geoastrophysical Abstracts - Academic ProQuest One Academic (New) Technology Collection Technology Research Database ProQuest One Academic Middle East (New) Materials Science Collection ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Pharma Collection ProQuest Central ProQuest Health & Medical Research Collection Genetics Abstracts ProQuest Engineering Collection Biotechnology Research Abstracts Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Bacteriology Abstracts (Microbiology B) Algology Mycology and Protozoology Abstracts (Microbiology C) Agricultural & Environmental Science Collection AIDS and Cancer Research Abstracts Materials Science Database ProQuest Materials Science Collection ProQuest Public Health ProQuest Nursing & Allied Health Source ProQuest SciTech Collection Advanced Technologies & Aerospace Database ProQuest Medical Library Animal Behavior Abstracts Materials Science & Engineering Collection Immunology Abstracts ProQuest Central (Alumni) MEDLINE - Academic
DatabaseTitleList	CrossRef MEDLINE - Academic MEDLINE Agricultural Science 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: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: Publicly Available Content Database (ProQuest) url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Sciences (General)
EISSN	1932-6203
ExternalDocumentID	3264578096 oai_doaj_org_article_bf123dcdd91249bfb7acdb1eb7caffcb A860968549 41129590 10_1371_journal_pone_0333805
Genre	Journal Article
GeographicLocations	China
GeographicLocations_xml	– name: China
GroupedDBID	--- 123 29O 2WC 53G 5VS 7RV 7X2 7X7 7XC 88E 8AO 8C1 8CJ 8FE 8FG 8FH 8FI 8FJ A8Z AAFWJ AAUCC AAWOE AAYXX ABDBF ABIVO ABJCF ABUWG ACCTH ACGFO ACIHN ACIWK ACPRK ACUHS ADBBV AEAQA AENEX AEUYN AFFHD AFKRA AFPKN AFRAH AHMBA ALMA_UNASSIGNED_HOLDINGS AOIJS APEBS ARAPS ATCPS BAIFH BAWUL BBNVY BBTPI BCNDV BENPR BGLVJ BHPHI BKEYQ BPHCQ BVXVI BWKFM CCPQU CITATION CS3 D1I D1J D1K DIK DU5 E3Z EAP EAS EBD EMOBN ESX EX3 F5P FPL FYUFA GROUPED_DOAJ GX1 HCIFZ HH5 HMCUK HYE IAO IEA IGS IHR IHW INH INR IOV IPY ISE ISR ITC K6- KB. KQ8 L6V LK5 LK8 M0K M1P M48 M7P M7R M7S M~E NAPCQ O5R O5S OK1 OVT P2P P62 PATMY PDBOC PHGZM PHGZT PIMPY PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO PTHSS PV9 PYCSY RNS RPM RZL SV3 TR2 UKHRP WOQ WOW ~02 ~KM ADRAZ CGR CUY CVF ECM EIF ESTFP IPNFZ NPM RIG 3V. 7QG 7QL 7QO 7SN 7SS 7T5 7TG 7TM 7U9 7XB 8FD 8FK AZQEC C1K DWQXO FR3 GNUQQ H94 K9. KL. M7N P64 PKEHL PQEST PQUKI PRINS RC3 7X8
ID	FETCH-LOGICAL-c588t-2d02c6ac1e31968a2a5b28f1b58827ca259be618eb541116cacf66cda3072bca3
IEDL.DBID	DOA
ISICitedReferencesCount	0
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001600295000050&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1932-6203
IngestDate	Tue Dec 02 00:10:34 EST 2025 Tue Oct 28 02:22:34 EDT 2025 Sat Oct 25 08:52:15 EDT 2025 Sat Nov 01 11:21:50 EDT 2025 Sat Nov 29 13:46:26 EST 2025 Sat Nov 29 10:30:21 EST 2025 Wed Nov 26 10:43:02 EST 2025 Wed Nov 26 10:43:03 EST 2025 Tue Nov 04 03:26:52 EST 2025 Tue Oct 28 02:37:12 EDT 2025 Sat Nov 29 07:01:26 EST 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	10
Language	English
License	Copyright: © 2025 Cao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Creative Commons Attribution License
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c588t-2d02c6ac1e31968a2a5b28f1b58827ca259be618eb541116cacf66cda3072bca3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ORCID	0000-0002-6179-2991
OpenAccessLink	https://doaj.org/article/bf123dcdd91249bfb7acdb1eb7caffcb
PMID	41129590
PQID	3264578096
PQPubID	1436336
PageCount	e0333805
ParticipantIDs	plos_journals_3264578096 doaj_primary_oai_doaj_org_article_bf123dcdd91249bfb7acdb1eb7caffcb proquest_miscellaneous_3264650064 proquest_journals_3264578096 gale_infotracmisc_A860968549 gale_infotracacademiconefile_A860968549 gale_incontextgauss_ISR_A860968549 gale_incontextgauss_IOV_A860968549 gale_healthsolutions_A860968549 pubmed_primary_41129590 crossref_primary_10_1371_journal_pone_0333805
PublicationCentury	2000
PublicationDate	2025-10-23
PublicationDateYYYYMMDD	2025-10-23
PublicationDate_xml	– month: 10 year: 2025 text: 2025-10-23 day: 23
PublicationDecade	2020
PublicationPlace	United States
PublicationPlace_xml	– name: United States – name: San Francisco
PublicationTitle	PloS one
PublicationTitleAlternate	PLoS One
PublicationYear	2025
Publisher	Public Library of Science Public Library of Science (PLoS)
Publisher_xml	– name: Public Library of Science – name: Public Library of Science (PLoS)
References	G Liang (pone.0333805.ref043) 2024; 32 pone.0333805.ref014 pone.0333805.ref011 N Mammone (pone.0333805.ref028) 2023; 27 H Li (pone.0333805.ref024) 2023; 79 R Zhang (pone.0333805.ref046) 2023; 398 X Tang (pone.0333805.ref050) 2023; 31 SU Amin (pone.0333805.ref052) 2019; 101 C Wang (pone.0333805.ref047) 2024; 12 X Xie (pone.0333805.ref051) 2024; 18 pone.0333805.ref019 GE Fabiani (pone.0333805.ref007) 2004; 12 R Alazrai (pone.0333805.ref009) 2019; 7 Y Qin (pone.0333805.ref048) 2024; 32 R Sharma (pone.0333805.ref033) 2024; 5 pone.0333805.ref025 K-W Ha (pone.0333805.ref049) 2019; 19 J Luo (pone.0333805.ref030) 2020; 193 VJ Lawhern (pone.0333805.ref021) 2018; 15 pone.0333805.ref023 pone.0333805.ref020 D Zhang (pone.0333805.ref053) 2019; 26 Y Ding (pone.0333805.ref032) 2023; 14 H Zhi (pone.0333805.ref045) 2023; 31 N Grover (pone.0333805.ref004) 2023; 31 D Zhang (pone.0333805.ref054) 2020; 24 L Gu (pone.0333805.ref005) 2020; 436 M Saeedi (pone.0333805.ref013) 2020; 43 Z Hu (pone.0333805.ref026) 2022; 12 DT Bundy (pone.0333805.ref008) 2017; 48 H Altaheri (pone.0333805.ref027) 2023; 19 YK Musallam (pone.0333805.ref022) 2021; 69 pone.0333805.ref039 pone.0333805.ref038 pone.0333805.ref037 H Dose (pone.0333805.ref015) 2018; 114 pone.0333805.ref035 I Güler (pone.0333805.ref012) 2007; 11 J Xie (pone.0333805.ref034) 2022; 30 S Kumar (pone.0333805.ref017) 2021; 7 A Craik (pone.0333805.ref016) 2019; 16 C Brunner (pone.0333805.ref041) 2008; 16 JR Wolpaw (pone.0333805.ref001) 2002; 113 pone.0333805.ref006 A Vaswani (pone.0333805.ref036) 2017; 30 pone.0333805.ref003 pone.0333805.ref002 N Mammone (pone.0333805.ref029) 2024; 34 W Ma (pone.0333805.ref031) 2022; 77 G Wang (pone.0333805.ref010) 2016; 20 J Xu (pone.0333805.ref018) 2020; 20 pone.0333805.ref040 RT Schirrmeister (pone.0333805.ref044) 2017; 38 R Leeb (pone.0333805.ref042) 2008
References_xml	– volume: 31 start-page: 464 year: 2023 ident: pone.0333805.ref004 article-title: Schizo-net: a novel schizophrenia diagnosis framework using late fusion multimodal deep learning on electroencephalogram-based brain connectivity indices publication-title: IEEE Trans Neural Syst Rehabil Eng. doi: 10.1109/TNSRE.2023.3237375 – ident: pone.0333805.ref006 doi: 10.1109/SMC.2019.8914544 – volume: 193 start-page: 105464 year: 2020 ident: pone.0333805.ref030 article-title: Motor imagery EEG classification based on ensemble support vector learning publication-title: Comput Methods Programs Biomed. doi: 10.1016/j.cmpb.2020.105464 – volume: 19 start-page: 2249 issue: 2 year: 2023 ident: pone.0333805.ref027 article-title: Physics-informed attention temporal convolutional network for EEG-based motor imagery classification publication-title: IEEE Trans Ind Inf. doi: 10.1109/TII.2022.3197419 – ident: pone.0333805.ref039 – volume: 7 start-page: 109612 year: 2019 ident: pone.0333805.ref009 article-title: A deep learning framework for decoding motor imagery tasks of the same hand using EEG signals publication-title: IEEE Access. doi: 10.1109/ACCESS.2019.2934018 – ident: pone.0333805.ref011 doi: 10.1007/978-3-319-74060-7_9 – volume: 113 start-page: 767 issue: 6 year: 2002 ident: pone.0333805.ref001 article-title: Brain-computer interfaces for communication and control publication-title: Clin Neurophysiol. doi: 10.1016/S1388-2457(02)00057-3 – volume: 43 start-page: 1007 issue: 3 year: 2020 ident: pone.0333805.ref013 article-title: Major depressive disorder assessment via enhanced k-nearest neighbor method and EEG signals publication-title: Phys Eng Sci Med. doi: 10.1007/s13246-020-00897-w – volume: 16 start-page: 1 year: 2008 ident: pone.0333805.ref041 article-title: BCI competition 2008 –Graz data set A publication-title: Institute for Knowledge Discovery (Laboratory of Brain-Computer Interfaces), Graz University of Technology. – volume: 7 year: 2021 ident: pone.0333805.ref017 article-title: OPTICAL+: a frequency-based deep learning scheme for recognizing brain wave signals publication-title: PeerJ Comput Sci. doi: 10.7717/peerj-cs.375 – volume: 12 start-page: 8325 year: 2024 ident: pone.0333805.ref047 article-title: MSFNet: a multi-scale space-time frequency fusion network for motor imagery EEG classification publication-title: IEEE Access. doi: 10.1109/ACCESS.2024.3351204 – ident: pone.0333805.ref035 doi: 10.36227/techrxiv.24003582 – ident: pone.0333805.ref040 – ident: pone.0333805.ref038 – volume: 16 start-page: 031001 issue: 3 year: 2019 ident: pone.0333805.ref016 article-title: Deep learning for electroencephalogram (EEG) classification tasks: a review publication-title: J Neural Eng. doi: 10.1088/1741-2552/ab0ab5 – volume: 48 start-page: 1908 issue: 7 year: 2017 ident: pone.0333805.ref008 article-title: Contralesional brain-computer interface control of a powered exoskeleton for motor recovery in chronic stroke survivors publication-title: Stroke. doi: 10.1161/STROKEAHA.116.016304 – volume: 38 start-page: 5391 issue: 11 year: 2017 ident: pone.0333805.ref044 article-title: Deep learning with convolutional neural networks for EEG decoding and visualization publication-title: Hum Brain Mapp. doi: 10.1002/hbm.23730 – ident: pone.0333805.ref020 doi: 10.1109/SMC.2018.00185 – volume: 436 start-page: 93 year: 2020 ident: pone.0333805.ref005 article-title: EEG-based classification of lower limb motor imagery with brain network analysis publication-title: Neuroscience. doi: 10.1016/j.neuroscience.2020.04.006 – volume: 12 start-page: 11255 issue: 21 year: 2022 ident: pone.0333805.ref026 article-title: EEG-based emotion recognition using convolutional recurrent neural network with multi-head self-attention publication-title: Applied Sciences. doi: 10.3390/app122111255 – volume: 77 start-page: 103718 year: 2022 ident: pone.0333805.ref031 article-title: A novel multi-branch hybrid neural network for motor imagery EEG signal classification publication-title: Biomedical Signal Processing and Control. doi: 10.1016/j.bspc.2022.103718 – ident: pone.0333805.ref002 doi: 10.1109/ANDESCON.2016.7836266 – volume: 5 issue: 4 year: 2024 ident: pone.0333805.ref033 article-title: Emerging trends in EEG signal processing: a systematic review publication-title: SN COMPUT SCI. doi: 10.1007/s42979-024-02773-w – volume: 30 start-page: 2126 year: 2022 ident: pone.0333805.ref034 article-title: A transformer-based approach combining deep learning network and spatial-temporal information for raw EEG classification publication-title: IEEE Trans Neural Syst Rehabil Eng. doi: 10.1109/TNSRE.2022.3194600 – volume: 31 start-page: 1208 year: 2023 ident: pone.0333805.ref050 article-title: Motor imagery EEG decoding based on multi-scale hybrid networks and feature enhancement publication-title: IEEE Trans Neural Syst Rehabil Eng. doi: 10.1109/TNSRE.2023.3242280 – volume: 30 year: 2017 ident: pone.0333805.ref036 article-title: Attention is all you need publication-title: Advances in neural information processing systems. – ident: pone.0333805.ref037 – ident: pone.0333805.ref014 doi: 10.1109/ICCP.2017.8116986 – volume: 398 start-page: 109953 year: 2023 ident: pone.0333805.ref046 article-title: Self-attention-based convolutional neural network and time-frequency common spatial pattern for enhanced motor imagery classification publication-title: J Neurosci Methods. doi: 10.1016/j.jneumeth.2023.109953 – volume: 69 start-page: 102826 year: 2021 ident: pone.0333805.ref022 article-title: Electroencephalography-based motor imagery classification using temporal convolutional network fusion publication-title: Biomedical Signal Processing and Control. doi: 10.1016/j.bspc.2021.102826 – ident: pone.0333805.ref025 doi: 10.1007/978-3-030-51935-3_11 – volume: 32 start-page: 1535 year: 2024 ident: pone.0333805.ref043 article-title: EISATC-fusion: inception self-attention temporal convolutional network fusion for motor imagery EEG decoding publication-title: IEEE Trans Neural Syst Rehabil Eng. doi: 10.1109/TNSRE.2024.3382226 – volume: 11 start-page: 117 issue: 2 year: 2007 ident: pone.0333805.ref012 article-title: Multiclass support vector machines for EEG-signals classification publication-title: IEEE Trans Inf Technol Biomed. doi: 10.1109/TITB.2006.879600 – ident: pone.0333805.ref003 doi: 10.1109/SMC.2018.00178 – volume: 20 start-page: 3496 issue: 12 year: 2020 ident: pone.0333805.ref018 article-title: Recognition of EEG signal motor imagery intention based on deep multi-view feature learning publication-title: Sensors (Basel). doi: 10.3390/s20123496 – ident: pone.0333805.ref023 doi: 10.1109/SMC42975.2020.9283028 – year: 2008 ident: pone.0333805.ref042 article-title: BCI competition 2008 –Graz data set B publication-title: Putz. – volume: 20 start-page: 1301 issue: 5 year: 2016 ident: pone.0333805.ref010 article-title: The removal of EOG artifacts from EEG signals using independent component analysis and multivariate empirical mode decomposition publication-title: IEEE J Biomed Health Inform. doi: 10.1109/JBHI.2015.2450196 – volume: 101 start-page: 542 year: 2019 ident: pone.0333805.ref052 article-title: Deep learning for EEG motor imagery classification based on multi-layer CNNs feature fusion publication-title: Future Generation Computer Systems. doi: 10.1016/j.future.2019.06.027 – volume: 12 start-page: 331 issue: 3 year: 2004 ident: pone.0333805.ref007 article-title: Conversion of EEG activity into cursor movement by a brain-computer interface (BCI) publication-title: IEEE Trans Neural Syst Rehabil Eng. doi: 10.1109/TNSRE.2004.834627 – volume: 34 start-page: 2350068 issue: 2 year: 2024 ident: pone.0333805.ref029 article-title: A few-shot transfer learning approach for motion intention decoding from electroencephalographic signals publication-title: Int J Neural Syst. doi: 10.1142/S0129065723500685 – volume: 26 start-page: 715 issue: 5 year: 2019 ident: pone.0333805.ref053 article-title: A convolutional recurrent attention model for subject-independent EEG signal analysis publication-title: IEEE Signal Process Lett. doi: 10.1109/LSP.2019.2906824 – volume: 114 start-page: 532 year: 2018 ident: pone.0333805.ref015 article-title: An end-to-end deep learning approach to MI-EEG signal classification for BCIs publication-title: Expert Systems with Applications. doi: 10.1016/j.eswa.2018.08.031 – ident: pone.0333805.ref019 doi: 10.1109/ICIEA.2019.8834381 – volume: 18 start-page: 1343249 year: 2024 ident: pone.0333805.ref051 article-title: Bidirectional feature pyramid attention-based temporal convolutional network model for motor imagery electroencephalogram classification publication-title: Front Neurorobot. doi: 10.3389/fnbot.2024.1343249 – volume: 79 start-page: 104066 year: 2023 ident: pone.0333805.ref024 article-title: A parallel multi-scale time-frequency block convolutional neural network based on channel attention module for motor imagery classification publication-title: Biomedical Signal Processing and Control. doi: 10.1016/j.bspc.2022.104066 – volume: 24 start-page: 2570 issue: 9 year: 2020 ident: pone.0333805.ref054 article-title: Motor imagery classification via temporal attention cues of graph embedded EEG signals publication-title: IEEE J Biomed Health Inform. doi: 10.1109/JBHI.2020.2967128 – volume: 15 start-page: 056013 issue: 5 year: 2018 ident: pone.0333805.ref021 article-title: EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces publication-title: J Neural Eng. doi: 10.1088/1741-2552/aace8c – volume: 32 start-page: 401 year: 2024 ident: pone.0333805.ref048 article-title: M-FANet: multi-feature attention convolutional neural network for motor imagery decoding publication-title: IEEE Trans Neural Syst Rehabil Eng. doi: 10.1109/TNSRE.2024.3351863 – volume: 27 start-page: 2365 issue: 5 year: 2023 ident: pone.0333805.ref028 article-title: AutoEncoder filter bank common spatial patterns to decode motor imagery from EEG publication-title: IEEE J Biomed Health Inform. doi: 10.1109/JBHI.2023.3243698 – volume: 14 start-page: 2238 issue: 3 year: 2023 ident: pone.0333805.ref032 article-title: TSception: capturing temporal dynamics and spatial asymmetry from EEG for emotion recognition publication-title: IEEE Trans Affective Comput. doi: 10.1109/TAFFC.2022.3169001 – volume: 31 start-page: 3988 year: 2023 ident: pone.0333805.ref045 article-title: A multi-domain convolutional neural network for EEG-based motor imagery decoding publication-title: IEEE Trans Neural Syst Rehabil Eng. doi: 10.1109/TNSRE.2023.3323325 – volume: 19 start-page: 2854 issue: 13 year: 2019 ident: pone.0333805.ref049 article-title: Motor imagery EEG classification using capsule networks publication-title: Sensors (Basel). doi: 10.3390/s19132854
SSID	ssj0053866
Score	2.4902673
Snippet	Motor imagery (MI) is a brain-computer interface (BCI) technology with the potential to change human life in the future. MI signals have been widely applied in...
SourceID	plos doaj proquest gale pubmed crossref
SourceType	Open Website Aggregation Database Index Database
StartPage	e0333805
SubjectTerms	Accuracy Algorithms Analysis Biochips Brain Brain research Brain-Computer Interfaces Classification Computational linguistics Computer applications Convolution Datasets Decoding Deep learning Electroencephalography - methods Human-computer interface Humans Imagery Imagination - physiology Implants Language processing Mental task performance Natural language interfaces Neural networks Neural Networks, Computer Neurology Prostheses Rehabilitation Smart buildings Technology application Temporal variations
SummonAdditionalLinks	– databaseName: Biological Science Database dbid: M7P link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9NAEF5B4MAFKI_WUGBBSMDBbbx-c0EFpYBESkVL1Zu1z6SSa4fYQeqP4D8zs14bggAhcYx3vFnvvO3Zbwh5qkzKlDS5z2KZ-xHnxs8SCOQilQkOHkqFtiXLyYf04CA7Pc0P3Qu3xpVV9jbRGmpVS3xHvgthRgTSBRH3q8UXH7tG4ddV10LjMrmCKAmhLd077C0x6HKSuONyYRrsOu7sLOpK74xDSM6wad1P7sii9g-2ebQo6-bPgad1QPs3_nfpN8l1F3rSvU5WNsglXd0iG065G_rcIVC_uE2-HU2nx9OXdFpDRk7PzhHn4oJOJm-pgmwVvR3l5Qz-op2fU6ych990foGHv6itUPQF9uuY00YjtLgoNcXqdiflsIZGl8ZHZE9ba0kdPlb5C1XVlajfIZ_3J8dv3vmub4Mv4yxrfabGTCZcBhr1O-OMx4JlJhAwylLJIeMSOgkyLeIITG0iuTRJIhUHe8OE5OFdMqqAR1uE5kKEUsVSKxlHwqQ8jxVMaTiXiWbGeMTv2VcsOniOwn6jSyGt6ba1QHYXjt0eeY08HmgRXNteqJezwulqIQy4cyWVyrEztzAi5VKJQAtYuzFSeOQRSkjRnVQdTESxlyXA4wwybo88sRQIsFFhBc-Mr5qmeP_x5B-Ijj6tET1zRKZulxy2qjs1Ac-EwF1rlNtrlGAm5NrwFspzvytN8UMq4c5ebn8__HgYxkmxKq_S9aqjgQgfolqPbHa6MexshIF8nI_v_X3y--Qaw47KEA2wcJuM2uVKPyBX5df2rFk-tOr8HSIXWMk priority: 102 providerName: ProQuest – databaseName: Public Library of Science dbid: FPL link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lb9NAEF6hwIELUF41FFgQEnBwidf22uZWUAJITaloqXqz9tlEcu0odpD6I_jPzNgbI1dEgqMzY3szO0959htCXmubMK1s5rNYZX4khPVTDolcpFMpIELpsB3JcnaYHB2l5-fZ8Z9C8doX_DAJ3juZ7i-r0uyPQyipELL0Jgs5xxau6fHhxvOC7XLujsdtu3MQflqU_t4Xj5ZFVW9PNNuAM737v0u9R-641JIedLqwQ26Y8j7ZccZb07cOYfrdA_LrZDY7nX2gswoqbrq4RByLKzqZfKYaqlGMZlQUF9Vq0cwvKXbGwzWdX-HhLtp2IPoS53HMaW0QOlwWhmL3utNiWENtCusjcmfbS0kd_lVxjavsWtAfkh_TyemnL76by-CrOE0bn-kxU1yowKD9poKJWLLUBhKoLFECKippeJAaGUfgSrkSynKutAB_wqQS4SMyKkFEu4RmUoZKx8poFUfSJiKLNTzSCqG4YdZ6xN9sV77s4Dfy9htcAmVLJ9YcpZ07aXvkI-5pz4vg2e0PsE25s8VcWgjXWmmd4eRtaWUilJaBkbB2a5X0yAvUiLw7idq7gPwg5VDwpVBRe-RVy4EAGiV26FyIdV3nX7-d_QPTyfcB0xvHZKtmJUBU3akI-E8IzDXg3BtwghtQA_Iu6u9GKnUOeXkE7hjIcOdGp_9OftmT8aHYdVeaat3xQAYPWatHHne20Es2wkQ9zsZPtr_3KbnNcFoyRHoW7pFRs1qbZ-SW-tks6tXz1nR_A6wyRwU priority: 102 providerName: Public Library of Science
Title	SMMTM: Motor imagery EEG decoding algorithm using a hybrid multi-branch separable convolutional self-attention temporal convolutional network
URI	https://www.ncbi.nlm.nih.gov/pubmed/41129590 https://www.proquest.com/docview/3264578096 https://www.proquest.com/docview/3264650064 https://doaj.org/article/bf123dcdd91249bfb7acdb1eb7caffcb http://dx.doi.org/10.1371/journal.pone.0333805
Volume	20
WOSCitedRecordID	wos001600295000050&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: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: DOA dateStart: 20060101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: M~E dateStart: 20060101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: P5Z dateStart: 20061201 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Agricultural Science Database customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: M0K dateStart: 20061201 isFulltext: true titleUrlDefault: https://search.proquest.com/agriculturejournals providerName: ProQuest – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: M7P dateStart: 20061201 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Engineering Database customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: M7S dateStart: 20061201 isFulltext: true titleUrlDefault: http://search.proquest.com providerName: ProQuest – providerCode: PRVPQU databaseName: Environmental Science Database customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: PATMY dateStart: 20061201 isFulltext: true titleUrlDefault: http://search.proquest.com/environmentalscience providerName: ProQuest – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: 7X7 dateStart: 20061201 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: Materials Science Database customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: KB. dateStart: 20061201 isFulltext: true titleUrlDefault: http://search.proquest.com/materialsscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Nursing & Allied Health Database customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: 7RV dateStart: 20061201 isFulltext: true titleUrlDefault: https://search.proquest.com/nahs providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: BENPR dateStart: 20061201 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Public Health Database customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: 8C1 dateStart: 20061201 isFulltext: true titleUrlDefault: https://search.proquest.com/publichealth providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database (ProQuest) customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: PIMPY dateStart: 20061201 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVATS databaseName: Public Library of Science (PLoS) Journals Open Access customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: FPL dateStart: 20060101 isFulltext: true titleUrlDefault: http://www.plos.org/publications/ providerName: Public Library of Science
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9NAEF5B4MAFtbwaWsKCkICD0_i95tZUCVStg5WUKHCx9tlESu0oTpD6I_jPzNhO1CAQHLiMZM_YcmZnZmeU2W8IeaNM6ChpIsvxZWR5nBuLBZDIeYoJDjuUcsuRLOOLcDBgk0mU3Br1hT1hFTxwpbhjYSC2KqlUhGOShREhl0rYWoSSGyMFRl_IejbFVBWDwYuDoD4o54b2cb0u7UWe6XbHhbIMx9Xd2ohKvP5tVG4s5nnx55Sz3Hr6e-RhnTPSk-pb98kdnT0i-7VXFvRdDR39_jH5MYrjy_gDjXMopensGgEqbmiv95EqKDNxm6J8fpUvZ6vpNcWWd7im0xs8tUXL1kJL4KCNKS00YoKLuabYll6bJ3xDoefGQkjOskmS1sBW81-ksqq3_An50u9dnn6y6oELlvQZW1mO6jgy4NLW6JiMO9wXDjO2AK4D-oZSSejAZlr4HsTIQHJpgkAqDoHCEZK7T0kjAxUfEBoJ4UrlS62k7wkT8shX8ErDuQy0Y0yTWBvtp4sKVyMt_1wLoR6p1JriaqX1ajVJF5doK4uo2OUNsJW0tpX0b7bSJC9xgdPqiOnWt9MTFkAlx6BUbpLXpQQiY2TYenPF10WRnn0e_4PQaLgj9LYWMvlqyUFV1XEH-E2IuLUjebQjCf4td9gHaI4brRQpJNwexFlgw5MbE_09-9WWjS_FdrpM5-tKBlJzSEeb5Fll2lvNepiB-1Hn-f_Q-CF54ODAZNjsHfeINFbLtX5B7svvq1mxbJG74XCMdBKWlAFlp3aL3Ov2BsmwVfo00H5yAfS82wYad86RhklJR0AT_xs8kZzFydeffKNakw
linkProvider	Directory of Open Access Journals
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEF6VgAQXoLwaKHRBIODgNl6_kRAqkNKoSajaEPVm9plUSu0QO6D8CP4Kv5EZPwJBgLj0wDHZycqefPPtjD0PQh4rEzAlTWQxT0aWy7mxQh8cOVeFgsMJpZxiJMuwG_T74clJdLhGvtW1MJhWWXNiQdQqlfiMfAfcDBfQBR73q-knC6dG4dvVeoRGCYsDvfgCIVv2svMW_t8njO21B2_2rWqqgCW9MMwtplpM-lzaGtEXcsY9wUJjC1hlgeQQDwjt26EWngtE4Esuje9LxcEamJDcgX0vkIvA4zamkAVHw5r5gTt8vyrPcwJ7p0LD9jRN9HbLgWAQh-T9dPwVUwKWZ0FjOkmzPzu6xYG3d-1_U9V1crVyreluaQvrZE0nN8h6RV4ZfVZ12H5-k3w97vUGvRe0l-bpjJ6eYR-PBW2331EF0Tie5pRPRnBL-fiMYmUAfKbjBRa30SID0xI4j2RMM42t08VEU8zer6wYriHTE2Nh59Iil5RW_b8mv0glZQr-LfLhXNRymzQSwMQGoZEQjlSe1Ep6rjABjzwFWxrOpa-ZMU1i1XCJp2X7kbh4BxlA2FaqNUZ4xRW8muQ1Ymopi83Diy_S2SiuuCgWBtwVJZWKcPK4MCLgUglbC7h2Y6Roki1EZFxW4i4pMN4NfcBU6LlRkzwqJLCBSIIZSiM-z7K48374D0LHRytCTyshk-YzDqoqq0LgnrAx2Yrk5ook0KBcWd5A-6m1ksU_rAB-WdvJ75cfLpdxU8w6THQ6L2UgggGvvUnulLa41KyLgYoXte7-ffMtcnl_0OvG3U7_4B65wnB6NHg-zNkkjXw21_fJJfk5P81mDwoqoeTjeRvkdznttmg
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEF6VgBAXoLwaKHRBIODgJlm_kRAqbQJRm1LRUvVm9plUSu0QJ6D8CP4Qv44Ze20IAsSlB472jlf2-JuXPQ9CHisTMiVN7DBfxo7HuXGiABw5T0WCg4VSbjGS5Xgv3N-PTk7igxXyraqFwbTKSicWilplEr-Rt8DN8ABd4HG3jE2LONjpvZp8cnCCFP5prcZplBDZ1YsvEL7lL_s78K6fMNbrHm2_deyEAUf6UTRzmGozGXDZ0YjEiDPuCxaZjoBVFkoOsYHQQSfSwvdAKQSSSxMEUnGQDCYkd2HfC-Ri6AKKsUp9u04vAT0SBLZUzw07LYuMzUmW6s22C4EhDsz7yRQWEwNqu9CYjLP8z05vYfx61_5ntl0nV63LTbdKGVklKzq9QVatUsvpM9t5-_lN8vVwMDgavKCDbJZN6ekZ9vdY0G73DVUQpaOVp3w8hEeajc4oVgzAMR0tsOiNFpmZjsA5JSOaa2ypLsaaYla_lW64h1yPjYMdTYscU2r7go1_oUrL1Pxb5MO5sOU2aaSAjzVCYyFcqXyplfQ9YUIe-wq2NJzLQDNjmsSpoJNMyrYkSfFvMoRwrmRrglBLLNSa5DXiq6bFpuLFiWw6TKyOSoQBN0ZJpWKcSC6MCLlUoqMF3LsxUjTJBqIzKSt0a9WYbEUB4CvyvbhJHhUU2FgkRawN-TzPk_67438gOny_RPTUEplsNuXAqrJaBJ4JG5YtUa4vUYJ6lEvLayhLFVfy5IdEwJWVzPx--WG9jJtiNmKqs3lJA5ENePNNcqeUy5qzHgYwfty--_fNN8hlkMNkr7-_e49cYThUGhwi5q6Txmw61_fJJfl5dppPHxRahZKP5y2P3wHof77D
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=SMMTM%3A+Motor+imagery+EEG+decoding+algorithm+using+a+hybrid+multi-branch+separable+convolutional+self-attention+temporal+convolutional+network&rft.jtitle=PloS+one&rft.au=DianGuo+Cao&rft.au=ZhenYuan+Yu&rft.au=Jinqiang+Wang&rft.au=Yuqiang+Wu&rft.date=2025-10-23&rft.pub=Public+Library+of+Science+%28PLoS%29&rft.eissn=1932-6203&rft.volume=20&rft.issue=10&rft_id=info:doi/10.1371%2Fjournal.pone.0333805&rft.externalDBID=DOA&rft.externalDocID=oai_doaj_org_article_bf123dcdd91249bfb7acdb1eb7caffcb
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1932-6203&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1932-6203&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1932-6203&client=summon