MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification

Objective: Advances in the motor imagery (MI)-based brain-computer interfaces (BCIs) allow control of several applications by decoding neurophysiological phenomena, which are usually recorded by electroencephalography (EEG) using a non-invasive technique. Despite significant advances in MI-based BCI...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE transactions on biomedical engineering Vol. 69; no. 6; pp. 2105 - 2118
Main Authors:	Autthasan, Phairot, Chaisaen, Rattanaphon, Sudhawiyangkul, Thapanun, Rangpong, Phurin, Kiatthaveephong, Suktipol, Dilokthanakul, Nat, Bhakdisongkhram, Gun, Phan, Huy, Guan, Cuntai, Wilaiprasitporn, Theerawit
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.06.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	autoencoder (AE) Brain-computer interfaces (BCIs) Calibration Classification deep metric learning (DML) EEG Electroencephalography Feature extraction Human-computer interface Image classification Interfaces Learning Measurement Mental task performance motor imagery (MI) Motor skill learning multi-task learning Multitasking Representations Task analysis Training
ISSN:	0018-9294, 1558-2531, 1558-2531
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	Objective: Advances in the motor imagery (MI)-based brain-computer interfaces (BCIs) allow control of several applications by decoding neurophysiological phenomena, which are usually recorded by electroencephalography (EEG) using a non-invasive technique. Despite significant advances in MI-based BCI, EEG rhythms are specific to a subject and various changes over time. These issues point to significant challenges to enhance the classification performance, especially in a subject-independent manner. Methods: To overcome these challenges, we propose MIN2Net, a novel end-to-end multi-task learning to tackle this task. We integrate deep metric learning into a multi-task autoencoder to learn a compact and discriminative latent representation from EEG and perform classification simultaneously. Results: This approach reduces the complexity in pre-processing, results in significant performance improvement on EEG classification. Experimental results in a subject-independent manner show that MIN2Net outperforms the state-of-the-art techniques, achieving an F1-score improvement of 6.72% and 2.23% on the SMR-BCI and OpenBMI datasets, respectively. Conclusion: We demonstrate that MIN2Net improves discriminative information in the latent representation. Significance: This study indicates the possibility and practicality of using this model to develop MI-based BCI applications for new users without calibration.
AbstractList	Advances in the motor imagery (MI)-based brain-computer interfaces (BCIs) allow control of several applications by decoding neurophysiological phenomena, which are usually recorded by electroencephalography (EEG) using a non-invasive technique. Despite significant advances in MI-based BCI, EEG rhythms are specific to a subject and various changes over time. These issues point to significant challenges to enhance the classification performance, especially in a subject-independent manner.OBJECTIVEAdvances in the motor imagery (MI)-based brain-computer interfaces (BCIs) allow control of several applications by decoding neurophysiological phenomena, which are usually recorded by electroencephalography (EEG) using a non-invasive technique. Despite significant advances in MI-based BCI, EEG rhythms are specific to a subject and various changes over time. These issues point to significant challenges to enhance the classification performance, especially in a subject-independent manner.To overcome these challenges, we propose MIN2Net, a novel end-to-end multi-task learning to tackle this task. We integrate deep metric learning into a multi-task autoencoder to learn a compact and discriminative latent representation from EEG and perform classification simultaneously.METHODSTo overcome these challenges, we propose MIN2Net, a novel end-to-end multi-task learning to tackle this task. We integrate deep metric learning into a multi-task autoencoder to learn a compact and discriminative latent representation from EEG and perform classification simultaneously.This approach reduces the complexity in pre-processing, results in significant performance improvement on EEG classification. Experimental results in a subject-independent manner show that MIN2Net outperforms the state-of-the-art techniques, achieving an F1-score improvement of 6.72% and 2.23% on the SMR-BCI and OpenBMI datasets, respectively.RESULTSThis approach reduces the complexity in pre-processing, results in significant performance improvement on EEG classification. Experimental results in a subject-independent manner show that MIN2Net outperforms the state-of-the-art techniques, achieving an F1-score improvement of 6.72% and 2.23% on the SMR-BCI and OpenBMI datasets, respectively.We demonstrate that MIN2Net improves discriminative information in the latent representation.CONCLUSIONWe demonstrate that MIN2Net improves discriminative information in the latent representation.This study indicates the possibility and practicality of using this model to develop MI-based BCI applications for new users without calibration.SIGNIFICANCEThis study indicates the possibility and practicality of using this model to develop MI-based BCI applications for new users without calibration. Advances in the motor imagery (MI)-based brain-computer interfaces (BCIs) allow control of several applications by decoding neurophysiological phenomena, which are usually recorded by electroencephalography (EEG) using a non-invasive technique. Despite significant advances in MI-based BCI, EEG rhythms are specific to a subject and various changes over time. These issues point to significant challenges to enhance the classification performance, especially in a subject-independent manner. To overcome these challenges, we propose MIN2Net, a novel end-to-end multi-task learning to tackle this task. We integrate deep metric learning into a multi-task autoencoder to learn a compact and discriminative latent representation from EEG and perform classification simultaneously. This approach reduces the complexity in pre-processing, results in significant performance improvement on EEG classification. Experimental results in a subject-independent manner show that MIN2Net outperforms the state-of-the-art techniques, achieving an F1-score improvement of 6.72% and 2.23% on the SMR-BCI and OpenBMI datasets, respectively. We demonstrate that MIN2Net improves discriminative information in the latent representation. This study indicates the possibility and practicality of using this model to develop MI-based BCI applications for new users without calibration. Objective: Advances in the motor imagery (MI)-based brain-computer interfaces (BCIs) allow control of several applications by decoding neurophysiological phenomena, which are usually recorded by electroencephalography (EEG) using a non-invasive technique. Despite significant advances in MI-based BCI, EEG rhythms are specific to a subject and various changes over time. These issues point to significant challenges to enhance the classification performance, especially in a subject-independent manner. Methods: To overcome these challenges, we propose MIN2Net, a novel end-to-end multi-task learning to tackle this task. We integrate deep metric learning into a multi-task autoencoder to learn a compact and discriminative latent representation from EEG and perform classification simultaneously. Results: This approach reduces the complexity in pre-processing, results in significant performance improvement on EEG classification. Experimental results in a subject-independent manner show that MIN2Net outperforms the state-of-the-art techniques, achieving an F1-score improvement of 6.72% and 2.23% on the SMR-BCI and OpenBMI datasets, respectively. Conclusion: We demonstrate that MIN2Net improves discriminative information in the latent representation. Significance: This study indicates the possibility and practicality of using this model to develop MI-based BCI applications for new users without calibration.
Author	Autthasan, Phairot Sudhawiyangkul, Thapanun Dilokthanakul, Nat Guan, Cuntai Rangpong, Phurin Chaisaen, Rattanaphon Kiatthaveephong, Suktipol Phan, Huy Wilaiprasitporn, Theerawit Bhakdisongkhram, Gun
Author_xml	– sequence: 1 givenname: Phairot orcidid: 0000-0002-9566-8382 surname: Autthasan fullname: Autthasan, Phairot organization: Bio-inspired Robotics and Neural Engineering Lab, School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology, Rayong, Thailand – sequence: 2 givenname: Rattanaphon orcidid: 0000-0003-1521-9956 surname: Chaisaen fullname: Chaisaen, Rattanaphon organization: Bio-inspired Robotics and Neural Engineering Lab, School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology, Rayong, Thailand – sequence: 3 givenname: Thapanun orcidid: 0000-0003-2486-9280 surname: Sudhawiyangkul fullname: Sudhawiyangkul, Thapanun organization: Bio-inspired Robotics and Neural Engineering Lab, School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology, Rayong, Thailand – sequence: 4 givenname: Phurin surname: Rangpong fullname: Rangpong, Phurin organization: Bio-inspired Robotics and Neural Engineering Lab, School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology, Rayong, Thailand – sequence: 5 givenname: Suktipol orcidid: 0000-0002-1776-2795 surname: Kiatthaveephong fullname: Kiatthaveephong, Suktipol organization: Bio-inspired Robotics and Neural Engineering Lab, School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology, Rayong, Thailand – sequence: 6 givenname: Nat orcidid: 0000-0001-7721-7564 surname: Dilokthanakul fullname: Dilokthanakul, Nat organization: Bio-inspired Robotics and Neural Engineering Lab, School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology, Rayong, Thailand – sequence: 7 givenname: Gun orcidid: 0000-0001-7434-9262 surname: Bhakdisongkhram fullname: Bhakdisongkhram, Gun organization: School of Physical Medicine and Rehabilitation, Institute of Medicine, Suranaree University of Technology, Thailand – sequence: 8 givenname: Huy orcidid: 0000-0003-4096-785X surname: Phan fullname: Phan, Huy organization: School of Electronic Engineering and Computer Science, Queen Mary University of London, U.K – sequence: 9 givenname: Cuntai orcidid: 0000-0002-0872-3276 surname: Guan fullname: Guan, Cuntai organization: School of Computer Science and Engineering, Nanyang Technological University, Singapore – sequence: 10 givenname: Theerawit orcidid: 0000-0003-4941-4354 surname: Wilaiprasitporn fullname: Wilaiprasitporn, Theerawit email: theerawit.w@vistec.ac.th organization: Bio-inspired Robotics and Neural Engineering Lab, School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology, Rayong, Thailand
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/34932469$$D View this record in MEDLINE/PubMed
BookMark	eNp9kc1uGyEURlGVqnHSPEBVqRqpm25wgOFvumutSWrJThZ11lOMLxHumHGBWeTti2M3iyy64Qo4B67ud4HOwhAAoQ-UTCklzfXq-7KdMsLotKa1opq_QRMqhMZM1PQMTQihGjes4efoIqVt2XLN5Tt0XvOmZlw2E_RrOb9jd5C_Vm3Y4DzgUqrl2GePVyb9rhZgYvDhsXJDrH6O6y3YjOdhA3soS8jVcsjlZr4zjxCfqra9rWa9Sck7b032Q3iP3jrTJ7g61Uv0cNOuZj_w4v52Pvu2wLY0k7Eh3CrgnAuuuF5bYNJqJ4nTshw6rhV3YAtApWJOCaGYpLJYTlAj1qy-RF-O7-7j8GeElLudTxb63gQYxtQVnKm61pwX9PMrdDuMMZTuCiUVoUrJplCfTtS43sGm20e_M_Gp-ze7AtAjYOOQUgT3glDSHfLpDvl0h3y6Uz7FUa8c6_PznHI0vv-v-fFoegB4-amRQlMp6r9_sZl1
CODEN	IEBEAX
CitedBy_id	crossref_primary_10_1109_TNSRE_2023_3339179 crossref_primary_10_1109_JBHI_2024_3450753 crossref_primary_10_1109_TIM_2022_3227604 crossref_primary_10_1109_TBCAS_2023_3234280 crossref_primary_10_1109_JBHI_2025_3546288 crossref_primary_10_1186_s13040_023_00336_y crossref_primary_10_1007_s10015_025_01024_2 crossref_primary_10_1016_j_neucom_2024_129239 crossref_primary_10_1016_j_neucom_2025_129911 crossref_primary_10_1109_TNSRE_2024_3457502 crossref_primary_10_1109_TETCI_2024_3425328 crossref_primary_10_1109_TNSRE_2025_3563416 crossref_primary_10_3390_electronics14132670 crossref_primary_10_1109_ACCESS_2024_3406736 crossref_primary_10_1109_JBHI_2023_3337072 crossref_primary_10_3390_s24123755 crossref_primary_10_1109_JBHI_2023_3238421 crossref_primary_10_1016_j_bspc_2023_105359 crossref_primary_10_1088_1361_6560_adcafa crossref_primary_10_1109_JBHI_2024_3498916 crossref_primary_10_3389_fnins_2023_1292724 crossref_primary_10_1109_JSEN_2022_3171808 crossref_primary_10_3389_fnins_2023_1122661 crossref_primary_10_3390_bioengineering11010030 crossref_primary_10_1109_TNSRE_2023_3255233 crossref_primary_10_1016_j_knosys_2023_110756 crossref_primary_10_1016_j_inffus_2025_103501 crossref_primary_10_1016_j_neunet_2024_106470 crossref_primary_10_1016_j_neuroimage_2022_119666 crossref_primary_10_1109_TBCAS_2022_3215962 crossref_primary_10_3389_fnhum_2024_1421922 crossref_primary_10_1016_j_bspc_2024_106311 crossref_primary_10_1016_j_bspc_2024_106156 crossref_primary_10_1109_TBME_2024_3361716 crossref_primary_10_1109_TNSRE_2023_3347579 crossref_primary_10_1109_JBHI_2024_3504604 crossref_primary_10_1016_j_knosys_2025_113315 crossref_primary_10_1007_s11042_025_20605_8 crossref_primary_10_1140_epjs_s11734_025_01606_y crossref_primary_10_3390_brainsci13071109 crossref_primary_10_1016_j_physa_2023_129010 crossref_primary_10_1109_TIM_2025_3598395 crossref_primary_10_1016_j_compbiomed_2024_109260 crossref_primary_10_3390_mi13091485 crossref_primary_10_3389_fnins_2024_1508747 crossref_primary_10_1109_TNNLS_2025_3538335 crossref_primary_10_1109_JIOT_2024_3402254 crossref_primary_10_1109_TIM_2024_3381720 crossref_primary_10_1109_JBHI_2025_3525577 crossref_primary_10_1109_TIM_2024_3384559 crossref_primary_10_1016_j_compbiomed_2024_108619 crossref_primary_10_1016_j_neucom_2024_128577 crossref_primary_10_1016_j_measurement_2022_112085 crossref_primary_10_3390_bdcc8120169 crossref_primary_10_1109_TASE_2025_3585778 crossref_primary_10_1007_s11571_025_10291_5 crossref_primary_10_1016_j_eswa_2023_121915 crossref_primary_10_1109_LSP_2024_3465348 crossref_primary_10_1016_j_neuroscience_2024_07_051 crossref_primary_10_3390_brainsci15050460 crossref_primary_10_1109_TBME_2022_3193277 crossref_primary_10_1109_TNSRE_2024_3395133 crossref_primary_10_1088_1741_2552_ad0c61 crossref_primary_10_1109_ACCESS_2024_3459866 crossref_primary_10_1016_j_eswa_2025_128678 crossref_primary_10_1109_TII_2024_3450010 crossref_primary_10_1109_TCDS_2023_3260084 crossref_primary_10_1109_RBME_2023_3296938 crossref_primary_10_1038_s41598_024_73755_4 crossref_primary_10_1016_j_brainres_2023_148673 crossref_primary_10_1145_3757732 crossref_primary_10_3390_bios14050211 crossref_primary_10_1007_s11571_025_10337_8 crossref_primary_10_1016_j_compbiomed_2023_107254 crossref_primary_10_1016_j_eswa_2025_129248 crossref_primary_10_3390_mti7100095 crossref_primary_10_3390_sym16111413 crossref_primary_10_1109_TNSRE_2023_3249831 crossref_primary_10_1109_JBHI_2023_3304646 crossref_primary_10_1109_JBHI_2024_3431230 crossref_primary_10_1007_s11042_024_19850_0 crossref_primary_10_48084_etasr_9945 crossref_primary_10_1016_j_artmed_2023_102738 crossref_primary_10_1109_JSEN_2025_3538100 crossref_primary_10_1109_JSEN_2025_3559438 crossref_primary_10_1109_TCSS_2024_3462823 crossref_primary_10_1016_j_neucom_2024_128889 crossref_primary_10_3389_fninf_2024_1345425 crossref_primary_10_1016_j_dsp_2022_103816 crossref_primary_10_1109_TBME_2022_3218116 crossref_primary_10_1109_JBHI_2024_3472097 crossref_primary_10_1109_JIOT_2022_3195777 crossref_primary_10_1109_TNSRE_2024_3499998 crossref_primary_10_1109_ACCESS_2023_3320561 crossref_primary_10_1109_TIM_2025_3548181 crossref_primary_10_1016_j_softx_2024_101787 crossref_primary_10_1007_s13534_023_00316_5 crossref_primary_10_1080_10255842_2023_2275244 crossref_primary_10_1109_TNSRE_2023_3299355 crossref_primary_10_1109_JSEN_2022_3205956
Cites_doi	10.1093/gigascience/giz002 10.1109/CVPR.2015.7298682 10.1002/hbm.23730 10.1109/10.552239 10.1109/TCDS.2019.2924648 10.3389/fnins.2012.00055 10.1109/TNSRE.2019.2923315 10.1109/CVPR.2017.145 10.3389/fnins.2013.00267 10.3389/fnins.2017.00226 10.1109/TBME.2010.2082539 10.1088/1741-2552/aace8c 10.1016/j.neunet.2020.12.013 10.1109/TNSRE.2016.2646763 10.1016/j.patcog.2017.12.002 10.1109/EMBC44109.2020.9175874 10.1109/TBME.2009.2026181 10.1109/TNNLS.2014.2302898 10.1109/TNNLS.2019.2946869 10.1016/j.cobme.2017.11.004 10.1109/JBHI.2014.2370646 10.1109/SPMB.2017.8257016 10.1162/NECO_a_00592 10.1109/TNSRE.2018.2864306 10.1109/TNSRE.2005.848627 10.1109/JBHI.2018.2832538 10.3389/fnins.2012.00039 10.1109/ACCESS.2019.2919143 10.1109/TPAMI.2021.3070057 10.1126/science.1127647 10.1109/JSEN.2020.3005968 10.1109/TETCI.2018.2829981 10.1109/86.895946 10.1109/JSEN.2019.2958210 10.1109/ACCESS.2020.3002459 10.1109/5.939829 10.1109/TNNLS.2021.3053576 10.1109/TNSRE.2018.2884641 10.1109/TBME.2016.2631620 10.1109/TNNLS.2018.2789927 10.1016/S1388-2457(99)00141-8 10.1109/TCYB.2019.2905157 10.1109/TNSRE.2020.2966826
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022
DBID	97E RIA RIE AAYXX CITATION NPM 7QF 7QO 7QQ 7SC 7SE 7SP 7SR 7TA 7TB 7U5 8BQ 8FD F28 FR3 H8D JG9 JQ2 KR7 L7M L~C L~D P64 7X8
DOI	10.1109/TBME.2021.3137184
DatabaseName	IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef 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 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 Biotechnology and BioEngineering Abstracts MEDLINE - Academic
DatabaseTitle	CrossRef PubMed 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 Materials Business File METADEX Biotechnology and BioEngineering Abstracts Computer and Information Systems Abstracts Professional Aerospace Database 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 - Academic PubMed Materials Research Database
Database_xml	– sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Medicine Engineering
EISSN	1558-2531
EndPage	2118
ExternalDocumentID	34932469 10_1109_TBME_2021_3137184 9658165
Genre	orig-research Journal Article
GrantInformation_xml	– fundername: The Office of the Permanent Secretary of the Ministry of Higher Education, Science, Research, and Innovation, Thailand grantid: RGNS63-252 – fundername: National Research Council of Thailand grantid: N41A640131 funderid: 10.13039/501100004704 – fundername: PTT Public Company Limited funderid: 10.13039/100012915 – fundername: SCB Public Company Limited – fundername: Thailand Science Research and Innovation; Thailand Science Research, and Innovation grantid: SRI62W1501 funderid: 10.13039/501100017170
GroupedDBID	--- -~X .55 .DC .GJ 0R~ 29I 4.4 53G 5GY 5RE 5VS 6IF 6IK 6IL 6IN 85S 97E AAJGR AARMG AASAJ AAWTH AAYJJ ABAZT ABJNI ABQJQ ABVLG ACGFO ACGFS ACIWK ACKIV ACNCT ACPRK ADZIZ AENEX AETIX AFFNX AFRAH AGQYO AGSQL AHBIQ AI. AIBXA AKJIK AKQYR ALLEH ALMA_UNASSIGNED_HOLDINGS ASUFR ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CHZPO CS3 DU5 EBS EJD F5P HZ~ H~9 IAAWW IBMZZ ICLAB IDIHD IEGSK IFIPE IFJZH IPLJI JAVBF LAI MS~ O9- OCL P2P RIA RIE RIL RNS TAE TN5 VH1 VJK X7M ZGI ZXP AAYXX CITATION NPM 7QF 7QO 7QQ 7SC 7SE 7SP 7SR 7TA 7TB 7U5 8BQ 8FD F28 FR3 H8D JG9 JQ2 KR7 L7M L~C L~D P64 7X8
ID	FETCH-LOGICAL-c349t-a04c7e44454748bce26c8f60f86444f4874fece441672f75572616a04f51a5b23
IEDL.DBID	RIE
ISICitedReferencesCount	116
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000799622400032&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0018-9294 1558-2531
IngestDate	Sun Sep 28 12:00:07 EDT 2025 Mon Jun 30 08:25:55 EDT 2025 Tue Sep 30 00:37:06 EDT 2025 Sat Nov 29 05:34:28 EST 2025 Tue Nov 18 22:38:10 EST 2025 Wed Aug 27 02:37:56 EDT 2025
IsPeerReviewed	true
IsScholarly	true
Issue	6
Language	English
License	https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c349t-a04c7e44454748bce26c8f60f86444f4874fece441672f75572616a04f51a5b23
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ORCID	0000-0001-7434-9262 0000-0003-4941-4354 0000-0002-0872-3276 0000-0002-9566-8382 0000-0001-7721-7564 0000-0003-2486-9280 0000-0002-1776-2795 0000-0003-4096-785X 0000-0003-1521-9956
PMID	34932469
PQID	2667017769
PQPubID	85474
PageCount	14
ParticipantIDs	proquest_miscellaneous_2612733844 crossref_primary_10_1109_TBME_2021_3137184 crossref_citationtrail_10_1109_TBME_2021_3137184 ieee_primary_9658165 pubmed_primary_34932469 proquest_journals_2667017769
PublicationCentury	2000
PublicationDate	2022-06-01
PublicationDateYYYYMMDD	2022-06-01
PublicationDate_xml	– month: 06 year: 2022 text: 2022-06-01 day: 01
PublicationDecade	2020
PublicationPlace	United States
PublicationPlace_xml	– name: United States – name: New York
PublicationTitle	IEEE transactions on biomedical engineering
PublicationTitleAbbrev	TBME
PublicationTitleAlternate	IEEE Trans Biomed Eng
PublicationYear	2022
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 ref12 ref15 ref14 ref53 ref52 ref10 ref17 ref16 ref19 ge (ref34) 0 ref18 rumelhart (ref40) 1986 ref51 ref45 ref48 ref47 ref41 ref44 ref43 maaten (ref50) 2008; 9 ref8 ref7 ref9 ref4 ref3 ref6 ref5 ref37 ref36 prannay (ref35) 2020; 33 ref31 ref30 ref33 ref32 ref2 ref1 ref39 ref38 um (ref49) 0 ref24 ref23 ref26 vincent (ref42) 2010; 11 ref20 ang (ref25) 0 ref22 ref21 ref28 steyrl (ref46) 0 ref27 ref29 hinton (ref11) 2006; 313
References_xml	– ident: ref33 doi: 10.1093/gigascience/giz002 – ident: ref16 doi: 10.1109/CVPR.2015.7298682 – ident: ref17 doi: 10.1002/hbm.23730 – ident: ref41 doi: 10.1109/10.552239 – ident: ref20 doi: 10.1109/TCDS.2019.2924648 – ident: ref45 doi: 10.3389/fnins.2012.00055 – volume: 9 start-page: 2579 year: 2008 ident: ref50 article-title: Visualizing data using t-SNE publication-title: J Mach Learn Res – ident: ref9 doi: 10.1109/TNSRE.2019.2923315 – ident: ref37 doi: 10.1109/CVPR.2017.145 – ident: ref48 doi: 10.3389/fnins.2013.00267 – ident: ref52 doi: 10.3389/fnins.2017.00226 – ident: ref28 doi: 10.1109/TBME.2010.2082539 – ident: ref19 doi: 10.1088/1741-2552/aace8c – ident: ref10 doi: 10.1016/j.neunet.2020.12.013 – ident: ref8 doi: 10.1109/TNSRE.2016.2646763 – ident: ref15 doi: 10.1016/j.patcog.2017.12.002 – ident: ref32 doi: 10.1109/EMBC44109.2020.9175874 – start-page: 269 year: 0 ident: ref34 article-title: Deep metric learning with hierarchical triplet loss publication-title: Proc Eur Conf Comput Vis – ident: ref27 doi: 10.1109/TBME.2009.2026181 – ident: ref2 doi: 10.1109/TNNLS.2014.2302898 – ident: ref12 doi: 10.1109/TNNLS.2019.2946869 – ident: ref1 doi: 10.1016/j.cobme.2017.11.004 – ident: ref36 doi: 10.1109/CVPR.2015.7298682 – volume: 33 start-page: 18661 year: 2020 ident: ref35 article-title: Supervised contrastive learning publication-title: Neural Inf Process Syst – ident: ref4 doi: 10.1109/JBHI.2014.2370646 – ident: ref38 doi: 10.1109/SPMB.2017.8257016 – ident: ref30 doi: 10.1162/NECO_a_00592 – ident: ref43 doi: 10.1109/TNSRE.2018.2864306 – ident: ref13 doi: 10.1109/TNSRE.2005.848627 – start-page: 216 year: 0 ident: ref49 article-title: Data augmentation of wearable sensor data for parkinson's disease monitoring using convolutional neural networks publication-title: Proc 19th ACM Int Conf Multimodal Interact – ident: ref31 doi: 10.1109/JBHI.2018.2832538 – ident: ref26 doi: 10.3389/fnins.2012.00039 – start-page: 318 year: 1986 ident: ref40 publication-title: Learning Internal Representations by Error Propagation – ident: ref23 doi: 10.1109/ACCESS.2019.2919143 – ident: ref53 doi: 10.1109/TPAMI.2021.3070057 – volume: 313 start-page: 504 year: 2006 ident: ref11 article-title: Reducing the dimensionality of data with neural networks publication-title: Science doi: 10.1126/science.1127647 – ident: ref6 doi: 10.1109/JSEN.2020.3005968 – ident: ref44 doi: 10.1109/TETCI.2018.2829981 – volume: 11 start-page: 3371 year: 2010 ident: ref42 article-title: Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion publication-title: J Mach Learn Res – start-page: 2390 year: 0 ident: ref25 article-title: Filter bank common spatial pattern (FBCSP) in brain-computer interface publication-title: Proc IEEE Int Joint Conf Neural Netw (IEEE World Congr Comput Intell – ident: ref24 doi: 10.1109/86.895946 – ident: ref3 doi: 10.1109/JSEN.2019.2958210 – ident: ref39 doi: 10.1109/ACCESS.2020.3002459 – ident: ref14 doi: 10.1109/5.939829 – ident: ref29 doi: 10.1109/TNNLS.2021.3053576 – ident: ref21 doi: 10.1109/TNSRE.2018.2884641 – start-page: 61-1 year: 0 ident: ref46 article-title: Motor imagery brain-computer interfaces: Random forests vs regularized LDA - Non-linear beats linear publication-title: Proc 6th Int Brain- Comput Interface Conf – ident: ref22 doi: 10.1109/TBME.2016.2631620 – ident: ref18 doi: 10.1109/TNNLS.2018.2789927 – ident: ref7 doi: 10.1016/S1388-2457(99)00141-8 – ident: ref51 doi: 10.1109/TCYB.2019.2905157 – ident: ref47 doi: 10.1109/CVPR.2015.7298682 – ident: ref5 doi: 10.1109/TNSRE.2020.2966826
SSID	ssj0014846
Score	2.6697705
Snippet	Objective: Advances in the motor imagery (MI)-based brain-computer interfaces (BCIs) allow control of several applications by decoding neurophysiological... Advances in the motor imagery (MI)-based brain-computer interfaces (BCIs) allow control of several applications by decoding neurophysiological phenomena, which...
SourceID	proquest pubmed crossref ieee
SourceType	Aggregation Database Index Database Enrichment Source Publisher
StartPage	2105
SubjectTerms	autoencoder (AE) Brain-computer interfaces (BCIs) Calibration Classification deep metric learning (DML) EEG Electroencephalography Feature extraction Human-computer interface Image classification Interfaces Learning Measurement Mental task performance motor imagery (MI) Motor skill learning multi-task learning Multitasking Representations Task analysis Training
Title	MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification
URI	https://ieeexplore.ieee.org/document/9658165 https://www.ncbi.nlm.nih.gov/pubmed/34932469 https://www.proquest.com/docview/2667017769 https://www.proquest.com/docview/2612733844
Volume	69
WOSCitedRecordID	wos000799622400032&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: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 1558-2531 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014846 issn: 0018-9294 databaseCode: RIE dateStart: 19640101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3db9QwDLe2aULwAGODcWObgsQTIqxJ03zsDVC3ncSdeDjQvZU2TRFitGjXQ-K_x0lz1R4AaU-tFCetYjv5OY5tgJes5FxhG62sMFTgKowqZWoqKpvUxucvD7Ewnz-o-Vwvl-bjFrweY2Gcc-HymXvjX4Mvv-7s2h-VnflEJUxm27CtlBxitUaPgdBDUE7CUIG5EdGDyRJztng3y9ES5AwN1BTXYl-LJxUIXIS_5nxrOwr1Vf4NNcOWc_Hobj-7Bw8jtCRvB1l4DFuu3YcHtxIO7sO9WXSlH8CX2XTO564_J3lb076j-CAhHJcuytV3EhOvfiWIagkuL_68hk7Hork9mXVorpPpD58E4zfJ80sSCmz6q0eB20_g00W-eH9FY7kFanE2elomwionhE_xJXRlHZdWNzJpNGIm0aBlIxpnkYBJxRuVZQqtL4m9moyVWcXTp7DTdq17BsRo20hdl4hNnJCZrZxjJWsqWQtnsjSbQLKZ9cLGXOS-JMZ1EWySxBSeZ4XnWRF5NoFXY5efQyKO_xEfeIaMhJEXEzjesLaIqroqEKEoXJaUNBN4MTajknnPSdm6bu1pGMK8VAsc-XAQiXHsjSQd_f2bz-E-9xET4eDmGHb6m7U7gV37q_-2ujlFSV7q0yDJfwDbO-o1
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lb9QwEB6VgigceHR5LBQwEqcK09ixnZgboJSu2EQcFtRbSBynQkCCulkk_j1jxxv1AJU4JZLHTuSZsb_xeGYAXrCK8wTbaG2EpgJXYVQp3VBRm6jRLn-5j4X5vEyKIj091R934OUUC2Ot9ZfP7Cv36n35TW827qjsyCUqYUpegatSCB6N0VqTz0CkY1hOxFCFuRbBh8kifbR6m2doC3KGJmqMq7GrxhMLhC7CXXS-sCH5Civ_Bpt-0zm-_X-_ewduBXBJ3ozScBd2bLcPNy-kHNyH63lwps_gS74oeGGH1yTrGjr0FB_EB-TSVbX-RkLq1TOCuJbgAuNObOhiKps7kLxHg50sfrg0GL9Jlr0nvsSmu3zk-X0PPh1nq3cnNBRcoAZnY6BVJExihXBJvkRaG8uVSVsVtSmiJtGibSNaa5CAqYS3iZQJ2l8Ke7WSVbLm8X3Y7frOPgSiU9OqtKkQnVihpKmtZRVra9UIq2Us5xBtZ700IRu5K4rxvfRWSaRLx7PS8awMPJvD4dTl55iK4zLimWPIRBh4MYeDLWvLoKzrEjFKggtTovQcnk_NqGbOd1J1tt84GoZAL04FjvxgFIlp7K0kPfr7N5_B3skqX5bLRfHhMdzgLn7CH-McwO5wvrFP4Jr5NXxdnz_18vwHrbHslA
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=MIN2Net%3A+End-to-End+Multi-Task+Learning+for+Subject-Independent+Motor+Imagery+EEG+Classification&rft.jtitle=IEEE+transactions+on+biomedical+engineering&rft.au=Autthasan%2C+Phairot&rft.au=Chaisaen%2C+Rattanaphon&rft.au=Sudhawiyangkul%2C+Thapanun&rft.au=Rangpong%2C+Phurin&rft.date=2022-06-01&rft.pub=The+Institute+of+Electrical+and+Electronics+Engineers%2C+Inc.+%28IEEE%29&rft.issn=0018-9294&rft.eissn=1558-2531&rft.volume=69&rft.issue=6&rft.spage=2105&rft_id=info:doi/10.1109%2FTBME.2021.3137184&rft.externalDBID=NO_FULL_TEXT
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0018-9294&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0018-9294&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0018-9294&client=summon