Transfer Learning for Brain-Computer Interfaces: A Euclidean Space Data Alignment Approach
Objective: This paper targets a major challenge in developing practical electroencephalogram (EEG)-based brain-computer interfaces (BCIs): how to cope with individual differences so that better learning performance can be obtained for a new subject, with minimum or even no subject-specific data? Met...
Saved in:
| Published in: | IEEE transactions on biomedical engineering Vol. 67; no. 2; pp. 399 - 410 |
|---|---|
| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
United States
IEEE
01.02.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects: | |
| 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: This paper targets a major challenge in developing practical electroencephalogram (EEG)-based brain-computer interfaces (BCIs): how to cope with individual differences so that better learning performance can be obtained for a new subject, with minimum or even no subject-specific data? Methods: We propose a novel approach to align EEG trials from different subjects in the Euclidean space to make them more similar, and hence improve the learning performance for a new subject. Our approach has three desirable properties: first, it aligns the EEG trials directly in the Euclidean space, and any signal processing, feature extraction, and machine learning algorithms can then be applied to the aligned trials; second, its computational cost is very low; and third, it is unsupervised and does not need any label information from the new subject. Results: Both offline and simulated online experiments on motor imagery classification and event-related potential classification verified that our proposed approach outperformed a state-of-the-art Riemannian space data alignment approach, and several approaches without data alignment. Conclusion: The proposed Euclidean space EEG data alignment approach can greatly facilitate transfer learning in BCIs. Significance: Our proposed approach is effective, efficient, and easy to implement. It could be an essential pre-processing step for EEG-based BCIs. |
|---|---|
| AbstractList | This paper targets a major challenge in developing practical electroencephalogram (EEG)-based brain-computer interfaces (BCIs): how to cope with individual differences so that better learning performance can be obtained for a new subject, with minimum or even no subject-specific data?OBJECTIVEThis paper targets a major challenge in developing practical electroencephalogram (EEG)-based brain-computer interfaces (BCIs): how to cope with individual differences so that better learning performance can be obtained for a new subject, with minimum or even no subject-specific data?We propose a novel approach to align EEG trials from different subjects in the Euclidean space to make them more similar, and hence improve the learning performance for a new subject. Our approach has three desirable properties: first, it aligns the EEG trials directly in the Euclidean space, and any signal processing, feature extraction, and machine learning algorithms can then be applied to the aligned trials; second, its computational cost is very low; and third, it is unsupervised and does not need any label information from the new subject.METHODSWe propose a novel approach to align EEG trials from different subjects in the Euclidean space to make them more similar, and hence improve the learning performance for a new subject. Our approach has three desirable properties: first, it aligns the EEG trials directly in the Euclidean space, and any signal processing, feature extraction, and machine learning algorithms can then be applied to the aligned trials; second, its computational cost is very low; and third, it is unsupervised and does not need any label information from the new subject.Both offline and simulated online experiments on motor imagery classification and event-related potential classification verified that our proposed approach outperformed a state-of-the-art Riemannian space data alignment approach, and several approaches without data alignment.RESULTSBoth offline and simulated online experiments on motor imagery classification and event-related potential classification verified that our proposed approach outperformed a state-of-the-art Riemannian space data alignment approach, and several approaches without data alignment.The proposed Euclidean space EEG data alignment approach can greatly facilitate transfer learning in BCIs.CONCLUSIONThe proposed Euclidean space EEG data alignment approach can greatly facilitate transfer learning in BCIs.Our proposed approach is effective, efficient, and easy to implement. It could be an essential pre-processing step for EEG-based BCIs.SIGNIFICANCEOur proposed approach is effective, efficient, and easy to implement. It could be an essential pre-processing step for EEG-based BCIs. This paper targets a major challenge in developing practical electroencephalogram (EEG)-based brain-computer interfaces (BCIs): how to cope with individual differences so that better learning performance can be obtained for a new subject, with minimum or even no subject-specific data? We propose a novel approach to align EEG trials from different subjects in the Euclidean space to make them more similar, and hence improve the learning performance for a new subject. Our approach has three desirable properties: first, it aligns the EEG trials directly in the Euclidean space, and any signal processing, feature extraction, and machine learning algorithms can then be applied to the aligned trials; second, its computational cost is very low; and third, it is unsupervised and does not need any label information from the new subject. Both offline and simulated online experiments on motor imagery classification and event-related potential classification verified that our proposed approach outperformed a state-of-the-art Riemannian space data alignment approach, and several approaches without data alignment. The proposed Euclidean space EEG data alignment approach can greatly facilitate transfer learning in BCIs. Our proposed approach is effective, efficient, and easy to implement. It could be an essential pre-processing step for EEG-based BCIs. Objective : This paper targets a major challenge in developing practical electroencephalogram (EEG)-based brain–computer interfaces (BCIs): how to cope with individual differences so that better learning performance can be obtained for a new subject, with minimum or even no subject-specific data? Methods : We propose a novel approach to align EEG trials from different subjects in the Euclidean space to make them more similar, and hence improve the learning performance for a new subject. Our approach has three desirable properties: first, it aligns the EEG trials directly in the Euclidean space, and any signal processing, feature extraction, and machine learning algorithms can then be applied to the aligned trials; second, its computational cost is very low; and third, it is unsupervised and does not need any label information from the new subject. Results : Both offline and simulated online experiments on motor imagery classification and event-related potential classification verified that our proposed approach outperformed a state-of-the-art Riemannian space data alignment approach, and several approaches without data alignment. Conclusion : The proposed Euclidean space EEG data alignment approach can greatly facilitate transfer learning in BCIs. Significance : Our proposed approach is effective, efficient, and easy to implement. It could be an essential pre-processing step for EEG-based BCIs. |
| Author | Wu, Dongrui He, He |
| Author_xml | – sequence: 1 givenname: He orcidid: 0000-0002-9118-2449 surname: He fullname: He, He email: hehe91@hust.edu.cn organization: Key Laboratory of Image Processing and Intelligent Control, Ministry of Education, and also with the School of Artificial Intelligence and AutomationHuazhong University of Science and Technology – sequence: 2 givenname: Dongrui orcidid: 0000-0002-7153-9703 surname: Wu fullname: Wu, Dongrui email: drwu@hust.edu.cn organization: Key Laboratory of Image Processing and Intelligent Control, Ministry of Education, and also with the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31034407$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kctu1DAUhi1URKeFB0BIyBIbNhl8jW1202GASoNYMGzYWI5zXFIlTmonC94ej2bKogs2tnz8_ef2X6GLOEZA6DUla0qJ-XC4-bZbM0LNmhnKDRXP0IpKqSsmOb1AK0Korgwz4hJd5XxfnkKL-gW65JRwIYhaoV-H5GIOkPAeXIpdvMNhTPgmuS5W23GYlrn83cZyBuchf8QbvFt837XgIv4xlRj-5GaHN313FweIM95MUxqd__0SPQ-uz_DqfF-jn593h-3Xav_9y-12s688F2auTNsAC6GGhgdpRMNa4pySXshGEm10LSjxBkA3smU81FpRpbTTtZREaKb4NXp_ylvKPiyQZzt02UPfuwjjki1jVJW5CTcFffcEvR-XFEt3lnEhJRVU8UK9PVNLM0Brp9QNLv2xj1srgDoBPo05JwjWd7ObuzHOZW-9pcQe_bFHf-zRH3v2pyjpE-Vj8v9p3pw0HQD847UitFaG_wVRWJhZ |
| CODEN | IEBEAX |
| CitedBy_id | crossref_primary_10_3390_app15052742 crossref_primary_10_1109_TNSRE_2024_3391936 crossref_primary_10_1016_j_patcog_2023_110015 crossref_primary_10_1109_TNSRE_2023_3259730 crossref_primary_10_1109_TNNLS_2023_3243339 crossref_primary_10_1109_TNSRE_2023_3321640 crossref_primary_10_1109_TNSRE_2022_3219418 crossref_primary_10_26599_BSA_2022_9050001 crossref_primary_10_3390_mi13060927 crossref_primary_10_1109_TNSRE_2023_3327740 crossref_primary_10_1109_TNSRE_2025_3595379 crossref_primary_10_1109_TBME_2020_2969839 crossref_primary_10_1016_j_neucom_2024_129239 crossref_primary_10_3389_fnins_2021_733546 crossref_primary_10_1109_TBME_2021_3115799 crossref_primary_10_1109_TCDS_2022_3193731 crossref_primary_10_3389_fnins_2023_1274320 crossref_primary_10_1088_1741_2552_ad0859 crossref_primary_10_1109_TNSRE_2024_3457504 crossref_primary_10_1109_TNSRE_2022_3220884 crossref_primary_10_1111_coin_12451 crossref_primary_10_3389_fnins_2025_1635588 crossref_primary_10_1016_j_neunet_2022_06_008 crossref_primary_10_1016_j_jneumeth_2020_108855 crossref_primary_10_1007_s11042_024_18648_4 crossref_primary_10_1109_COMST_2024_3396847 crossref_primary_10_1109_TNSRE_2023_3321414 crossref_primary_10_1109_TNSRE_2024_3359191 crossref_primary_10_1080_24725579_2025_2492555 crossref_primary_10_1109_TNSRE_2024_3356916 crossref_primary_10_3389_fnhum_2022_1049985 crossref_primary_10_1109_JSEN_2023_3265688 crossref_primary_10_1016_j_bspc_2023_105867 crossref_primary_10_1016_j_neucom_2023_01_087 crossref_primary_10_1016_j_compbiomed_2022_106220 crossref_primary_10_1088_1741_2552_ac4852 crossref_primary_10_1109_TNSRE_2023_3243257 crossref_primary_10_3390_biomimetics10040225 crossref_primary_10_1109_TNSRE_2020_3023761 crossref_primary_10_2478_pjmpe_2024_0016 crossref_primary_10_1093_pnasnexus_pgae076 crossref_primary_10_1016_j_inffus_2025_103501 crossref_primary_10_1093_nsr_nwaf086 crossref_primary_10_1109_TIM_2024_3451593 crossref_primary_10_1016_j_bspc_2024_106311 crossref_primary_10_1088_1741_2552_ad4f18 crossref_primary_10_1109_MCI_2022_3199622 crossref_primary_10_1109_TBME_2023_3303289 crossref_primary_10_1088_1741_2552_acd95d crossref_primary_10_1016_j_neucom_2024_129010 crossref_primary_10_1109_TNSRE_2022_3225878 crossref_primary_10_1109_TNSRE_2023_3241301 crossref_primary_10_1016_j_bspc_2023_105293 crossref_primary_10_1088_1741_2552_ad593b crossref_primary_10_1016_j_compbiomed_2025_110675 crossref_primary_10_1109_JBHI_2024_3402324 crossref_primary_10_1109_TBME_2024_3474049 crossref_primary_10_1109_TCE_2025_3573773 crossref_primary_10_3390_app13106283 crossref_primary_10_1007_s11571_021_09676_z crossref_primary_10_1109_JBHI_2025_3525577 crossref_primary_10_1109_TAI_2021_3098253 crossref_primary_10_1109_TCDS_2021_3090217 crossref_primary_10_3389_fnbot_2022_958052 crossref_primary_10_1016_j_sna_2025_116572 crossref_primary_10_1016_j_bspc_2025_107706 crossref_primary_10_1016_j_bspc_2025_107948 crossref_primary_10_1109_JBHI_2024_3463737 crossref_primary_10_1016_j_compeleceng_2024_109680 crossref_primary_10_3390_electronics14142853 crossref_primary_10_1007_s11432_022_3548_2 crossref_primary_10_1088_1742_6596_3079_1_012067 crossref_primary_10_1109_TNSRE_2023_3323902 crossref_primary_10_1007_s11571_023_09940_4 crossref_primary_10_1038_s41598_022_14026_y crossref_primary_10_1109_TCSS_2022_3184818 crossref_primary_10_1016_j_neuroimage_2025_121243 crossref_primary_10_1109_TBME_2024_3432934 crossref_primary_10_1109_TNSRE_2024_3382226 crossref_primary_10_1088_1741_2552_ad1f7a crossref_primary_10_1186_s12859_024_06024_w crossref_primary_10_1109_TNSRE_2019_2945794 crossref_primary_10_3389_fnhum_2022_951591 crossref_primary_10_3389_fnins_2021_779231 crossref_primary_10_1109_TVT_2023_3290660 crossref_primary_10_3389_fncom_2024_1431815 crossref_primary_10_1016_j_jneumeth_2022_109642 crossref_primary_10_1088_1741_2552_ad3eb5 crossref_primary_10_1109_TNNLS_2023_3269512 crossref_primary_10_1109_ACCESS_2024_3351204 crossref_primary_10_1088_1741_2552_ad0a01 crossref_primary_10_1088_1741_2552_ac1ed2 crossref_primary_10_1109_TII_2024_3450010 crossref_primary_10_1109_JBHI_2022_3218453 crossref_primary_10_1016_j_bspc_2023_105556 crossref_primary_10_3389_fnhum_2023_1175399 crossref_primary_10_1109_TNSRE_2021_3059166 crossref_primary_10_1007_s11571_025_10337_8 crossref_primary_10_1109_TIM_2024_3476618 crossref_primary_10_3389_fnins_2024_1360709 crossref_primary_10_1016_j_compbiomed_2022_106267 crossref_primary_10_3389_fnhum_2022_1068165 crossref_primary_10_1088_2057_1976_ad90e8 crossref_primary_10_1109_TNSRE_2023_3265304 crossref_primary_10_1016_j_eswa_2025_129248 crossref_primary_10_1049_ell2_12275 crossref_primary_10_1109_JAS_2022_106004 crossref_primary_10_1109_TNSRE_2021_3099908 crossref_primary_10_1109_TNSRE_2024_3358491 crossref_primary_10_3390_s22062241 crossref_primary_10_1088_1741_2552_ac9a01 crossref_primary_10_1007_s11517_023_02967_z crossref_primary_10_1088_1741_2552_ad3986 crossref_primary_10_1016_j_cmpb_2021_106150 crossref_primary_10_1016_j_jneumeth_2024_110332 crossref_primary_10_1016_j_knosys_2025_114205 crossref_primary_10_1016_j_eswa_2023_121612 crossref_primary_10_3390_s24248127 crossref_primary_10_1016_j_neuroimage_2023_120209 crossref_primary_10_1007_s11571_023_09936_0 crossref_primary_10_3390_s24217080 crossref_primary_10_1016_j_bspc_2025_107756 crossref_primary_10_1109_MSP_2021_3134629 crossref_primary_10_1109_TCDS_2022_3209801 crossref_primary_10_1109_TCBB_2021_3052811 crossref_primary_10_1007_s11571_022_09821_2 crossref_primary_10_1016_j_neunet_2023_06_005 crossref_primary_10_1109_ACCESS_2022_3147461 crossref_primary_10_1109_TNSRE_2024_3451010 crossref_primary_10_3390_app112311252 crossref_primary_10_1109_TNNLS_2022_3202569 crossref_primary_10_1088_1741_2552_ad152f crossref_primary_10_1371_journal_pone_0263641 crossref_primary_10_1109_TBME_2021_3105912 crossref_primary_10_1109_TNSRE_2022_3174821 crossref_primary_10_1109_TCDS_2024_3370261 crossref_primary_10_1109_TNSRE_2020_2985996 crossref_primary_10_1177_14759217221110441 crossref_primary_10_3389_fnhum_2024_1447662 crossref_primary_10_3390_app15010392 crossref_primary_10_1109_TNSRE_2024_3355434 crossref_primary_10_1016_j_engappai_2025_110340 crossref_primary_10_1016_j_neunet_2025_107741 crossref_primary_10_1109_TIM_2024_3420350 crossref_primary_10_3390_brainsci15010075 crossref_primary_10_1109_TNSRE_2024_3445115 crossref_primary_10_3389_fnhum_2023_1143027 crossref_primary_10_1109_TCDS_2020_3007453 crossref_primary_10_1109_TNSRE_2020_2980299 crossref_primary_10_1109_TCDS_2023_3338460 crossref_primary_10_1109_TFUZZ_2019_2930022 crossref_primary_10_3389_fnins_2022_865201 crossref_primary_10_3390_brainsci15080877 crossref_primary_10_1016_j_bspc_2022_104435 crossref_primary_10_1109_ACCESS_2020_3048683 crossref_primary_10_1109_TCDS_2023_3314155 crossref_primary_10_1080_10447318_2025_2464097 crossref_primary_10_1109_TASE_2025_3604283 crossref_primary_10_1016_j_neucom_2025_130206 crossref_primary_10_1088_1741_2552_adb998 crossref_primary_10_3389_fpsyt_2021_837149 crossref_primary_10_1177_00202940221105092 crossref_primary_10_1016_j_aei_2022_101729 crossref_primary_10_1016_j_neucom_2023_126659 crossref_primary_10_3389_fnhum_2024_1421922 crossref_primary_10_3390_smartcities6030065 crossref_primary_10_1109_JBHI_2020_3025865 crossref_primary_10_3390_brainsci13020240 crossref_primary_10_1016_j_bspc_2022_104454 crossref_primary_10_1007_s11760_025_04532_7 crossref_primary_10_1109_TIM_2023_3341121 crossref_primary_10_1109_TNSRE_2022_3191869 crossref_primary_10_3390_app15148036 crossref_primary_10_1016_j_neunet_2024_106351 crossref_primary_10_3389_fnsys_2021_578875 crossref_primary_10_1016_j_eswa_2025_127312 crossref_primary_10_1016_j_eswa_2024_124673 crossref_primary_10_3390_electronics13061033 crossref_primary_10_1016_j_bspc_2024_106044 crossref_primary_10_1016_j_bspc_2024_107132 crossref_primary_10_1016_j_procs_2023_12_037 crossref_primary_10_1109_TNSRE_2022_3211881 crossref_primary_10_1016_j_bspc_2024_106046 crossref_primary_10_1016_j_bspc_2024_107015 crossref_primary_10_1007_s11517_024_03036_9 crossref_primary_10_1016_j_jneumeth_2022_109593 crossref_primary_10_1016_j_neunet_2025_107516 crossref_primary_10_3389_fnins_2022_863359 crossref_primary_10_1007_s10489_024_05662_0 crossref_primary_10_3389_fncom_2021_737324 crossref_primary_10_3389_fnins_2024_1402154 crossref_primary_10_1186_s12938_020_00765_4 crossref_primary_10_3390_s21062173 crossref_primary_10_1155_2022_3893866 crossref_primary_10_1063_5_0054912 crossref_primary_10_1109_JIOT_2024_3431233 crossref_primary_10_3389_fnhum_2021_643386 crossref_primary_10_1016_j_magmed_2025_100038 crossref_primary_10_3233_JIFS_237890 crossref_primary_10_1016_j_eswa_2024_125089 crossref_primary_10_1109_TCDS_2021_3137530 crossref_primary_10_3390_molecules28031069 crossref_primary_10_1109_TNSRE_2023_3241846 crossref_primary_10_1109_JBHI_2020_3002329 crossref_primary_10_1016_j_neucom_2024_127805 crossref_primary_10_1109_JPROC_2023_3277471 crossref_primary_10_1088_1741_2552_ad2710 crossref_primary_10_3389_fnins_2024_1271831 crossref_primary_10_1016_j_jneumeth_2022_109489 crossref_primary_10_1109_TETCI_2024_3359097 crossref_primary_10_1016_j_eswa_2024_123492 crossref_primary_10_1109_TNSRE_2023_3285309 crossref_primary_10_1109_ACCESS_2020_3016700 crossref_primary_10_1016_j_eswa_2025_128783 crossref_primary_10_1109_JBHI_2024_3491096 crossref_primary_10_1088_1741_2552_ad430d crossref_primary_10_1016_j_neucom_2020_09_017 crossref_primary_10_1016_j_bspc_2023_105155 crossref_primary_10_1088_1741_2552_ac93b4 crossref_primary_10_1088_1741_2552_addd49 crossref_primary_10_1016_j_neucom_2024_127944 crossref_primary_10_1016_j_compbiomed_2022_106420 crossref_primary_10_1186_s40708_025_00267_w crossref_primary_10_1109_TNSRE_2023_3236372 crossref_primary_10_31083_j_jin2312218 crossref_primary_10_3389_fnhum_2020_00103 crossref_primary_10_1016_j_bbe_2024_11_003 crossref_primary_10_1093_nsr_nwaa233 crossref_primary_10_1109_TNSRE_2022_3207494 crossref_primary_10_1007_s10489_025_06612_0 crossref_primary_10_1109_TBME_2022_3168570 crossref_primary_10_1088_1741_2552_ad5fbd crossref_primary_10_1109_TNSRE_2022_3204540 crossref_primary_10_1109_TBME_2020_3045720 crossref_primary_10_3389_fnhum_2021_706270 crossref_primary_10_1016_j_neunet_2023_08_008 crossref_primary_10_1109_JBHI_2024_3395910 crossref_primary_10_1109_ACCESS_2022_3178100 crossref_primary_10_1080_09544828_2024_2326396 crossref_primary_10_1016_j_knosys_2025_113074 crossref_primary_10_1016_j_engappai_2022_105581 crossref_primary_10_3389_fnhum_2023_1243750 crossref_primary_10_1016_j_eswa_2024_125452 crossref_primary_10_1109_TNNLS_2020_3010780 crossref_primary_10_1016_j_bspc_2022_104540 crossref_primary_10_1109_TNSRE_2023_3252610 crossref_primary_10_3390_make7030092 crossref_primary_10_1016_j_jneumeth_2025_110483 crossref_primary_10_3389_fnins_2020_629572 crossref_primary_10_1016_j_bspc_2024_106081 crossref_primary_10_1109_TCDS_2021_3082648 |
| Cites_doi | 10.1161/01.CIR.101.23.e215 10.3390/s120201211 10.1109/THMS.2016.2608931 10.1145/1961189.1961199 10.1109/ICASSP.2010.5495183 10.1109/SMC.2016.7844284 10.1109/MCI.2015.2501545 10.1109/TFUZZ.2017.2688423 10.1109/JPROC.2012.2184830 10.1016/S1388-2457(98)00038-8 10.1371/journal.pone.0178498 10.1109/TKDE.2009.191 10.1007/978-3-319-70096-0_83 10.1109/MSP.2008.4408441 10.1007/978-3-540-27816-0_8 10.1109/MC.2012.107 10.1007/BF01129656 10.1109/TNSRE.2016.2627016 10.1109/TBME.2017.2742541 10.1016/j.neuroimage.2007.01.051 10.1109/TNSRE.2016.2544108 10.1016/S0378-3758(00)00115-4 10.1109/LSP.2009.2022557 10.1109/TBME.2011.2172210 10.1109/TBME.2013.2253608 10.1109/SMC.2017.8122610 10.1109/MC.2008.432 10.1109/TNSRE.2017.2699784 10.1109/TFUZZ.2016.2633379 10.1016/S1388-2457(02)00057-3 10.1088/1741-2560/11/3/035005 10.1109/TBME.2009.2012869 10.2307/2283970 10.1109/86.895946 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020 |
| DBID | 97E RIA RIE AAYXX CITATION CGR CUY CVF ECM EIF 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.2019.2913914 |
| DatabaseName | IEEE All-Society Periodicals Package (ASPP) 2005–Present 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 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 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 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 MEDLINE 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 | 410 |
| ExternalDocumentID | 31034407 10_1109_TBME_2019_2913914 8701679 |
| Genre | orig-research Journal Article |
| 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 CGR CUY CVF ECM EIF NPM RIG 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-9dbe2ff6eb3f594b2d0aa75c45b508986410c9ee8b5d23f6871778a8655048273 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 310 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000510903800008&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 11:35:25 EDT 2025 Mon Jun 30 10:17:36 EDT 2025 Thu Apr 03 06:57:06 EDT 2025 Sat Nov 29 05:34:23 EST 2025 Tue Nov 18 22:38:10 EST 2025 Wed Aug 27 02:29:44 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 2 |
| 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-9dbe2ff6eb3f594b2d0aa75c45b508986410c9ee8b5d23f6871778a8655048273 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0002-9118-2449 0000-0002-7153-9703 |
| PMID | 31034407 |
| PQID | 2345514173 |
| PQPubID | 85474 |
| PageCount | 12 |
| ParticipantIDs | ieee_primary_8701679 proquest_miscellaneous_2217484039 crossref_citationtrail_10_1109_TBME_2019_2913914 proquest_journals_2345514173 pubmed_primary_31034407 crossref_primary_10_1109_TBME_2019_2913914 |
| PublicationCentury | 2000 |
| PublicationDate | 2020-02-01 |
| PublicationDateYYYYMMDD | 2020-02-01 |
| PublicationDate_xml | – month: 02 year: 2020 text: 2020-02-01 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 | 2020 |
| 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 | ref35 ref13 ref34 ref37 ref15 ref36 ref14 van der maaten (ref33) 2008; 9 ref11 ref1 ref39 ref17 ref38 ref16 ref19 utgoff (ref32) 1986; 2 ref18 gretton (ref10) 0 congedo (ref7) 2013 bishop (ref3) 2006 holm (ref12) 1979; 6 barachant (ref2) 2014 ref24 ref23 ref26 ref25 ref20 ref42 ref41 ref22 ref21 ref43 ref28 ref27 ref29 ref8 ref9 ref4 ref6 ref5 ref40 sun (ref31) 0 sugiyama (ref30) 0 |
| References_xml | – ident: ref9 doi: 10.1161/01.CIR.101.23.e215 – year: 2013 ident: ref7 article-title: A new generation of brain-computer interface based on Riemannian geometry publication-title: arXiv 1310 8115 – ident: ref23 doi: 10.3390/s120201211 – ident: ref37 doi: 10.1109/THMS.2016.2608931 – ident: ref6 doi: 10.1145/1961189.1961199 – ident: ref20 doi: 10.1109/ICASSP.2010.5495183 – ident: ref16 doi: 10.1109/SMC.2016.7844284 – ident: ref13 doi: 10.1109/MCI.2015.2501545 – ident: ref38 doi: 10.1109/TFUZZ.2017.2688423 – ident: ref18 doi: 10.1109/JPROC.2012.2184830 – ident: ref22 doi: 10.1016/S1388-2457(98)00038-8 – ident: ref21 doi: 10.1371/journal.pone.0178498 – ident: ref24 doi: 10.1109/TKDE.2009.191 – ident: ref11 doi: 10.1007/978-3-319-70096-0_83 – ident: ref4 doi: 10.1109/MSP.2008.4408441 – ident: ref8 doi: 10.1007/978-3-540-27816-0_8 – start-page: 1433 year: 0 ident: ref30 article-title: Direct importance estimation with model selection and its application to covariate shift adaptation publication-title: Proc 32nd Annu Conf Adv Neural Inf Process Syst – ident: ref34 doi: 10.1109/MC.2012.107 – ident: ref17 doi: 10.1007/BF01129656 – ident: ref42 doi: 10.1109/TNSRE.2016.2627016 – volume: 6 start-page: 65 year: 1979 ident: ref12 article-title: A simple sequentially rejective multiple test procedure publication-title: Scand J Statist – volume: 2 start-page: 107 year: 1986 ident: ref32 article-title: Shift of bias for inductive concept learning publication-title: Machine Learning An Artificial Intelligence Approach – ident: ref43 doi: 10.1109/TBME.2017.2742541 – start-page: 2058 year: 0 ident: ref31 article-title: Return of frustratingly easy domain adaptation publication-title: Proc 30th AAAI Conf Artif Intell – ident: ref5 doi: 10.1016/j.neuroimage.2007.01.051 – ident: ref40 doi: 10.1109/TNSRE.2016.2544108 – ident: ref29 doi: 10.1016/S0378-3758(00)00115-4 – ident: ref14 doi: 10.1109/LSP.2009.2022557 – ident: ref1 doi: 10.1109/TBME.2011.2172210 – ident: ref28 doi: 10.1109/TBME.2013.2253608 – volume: 9 start-page: 2579 year: 2008 ident: ref33 article-title: Visualizing data using t-SNE publication-title: J Mach Learn Res – ident: ref36 doi: 10.1109/SMC.2017.8122610 – ident: ref25 doi: 10.1109/MC.2008.432 – year: 2006 ident: ref3 publication-title: Pattern Recognition and Machine Learning – ident: ref41 doi: 10.1109/TNSRE.2017.2699784 – ident: ref39 doi: 10.1109/TFUZZ.2016.2633379 – year: 2014 ident: ref2 article-title: A plug & play P300 BCI using information geometry publication-title: arXiv 1409 0107 – ident: ref35 doi: 10.1016/S1388-2457(02)00057-3 – ident: ref15 doi: 10.1088/1741-2560/11/3/035005 – start-page: 513 year: 0 ident: ref10 article-title: A kernel method for the two-sample-problem publication-title: Proc Adv Neural Inf Process Syst – ident: ref27 doi: 10.1109/TBME.2009.2012869 – ident: ref19 doi: 10.2307/2283970 – ident: ref26 doi: 10.1109/86.895946 |
| SSID | ssj0014846 |
| Score | 2.6958494 |
| Snippet | Objective: This paper targets a major challenge in developing practical electroencephalogram (EEG)-based brain-computer interfaces (BCIs): how to cope with... This paper targets a major challenge in developing practical electroencephalogram (EEG)-based brain-computer interfaces (BCIs): how to cope with individual... Objective : This paper targets a major challenge in developing practical electroencephalogram (EEG)-based brain–computer interfaces (BCIs): how to cope with... |
| SourceID | proquest pubmed crossref ieee |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 399 |
| SubjectTerms | Algorithms Alignment Brain Brain-computer interface Brain-Computer Interfaces Classification Computational neuroscience Computer Simulation Covariance matrices data alignment Data processing Databases, Factual EEG Electroencephalography Electroencephalography - classification Euclidean geometry Euclidean space Event-related potentials Evoked Potentials - physiology Feature extraction Humans Image classification Imagination - physiology Interfaces Learning algorithms Machine Learning Machine learning algorithms Mental task performance Microsoft Windows Riemannian geometry Signal processing Signal Processing, Computer-Assisted Task analysis Transfer learning |
| Title | Transfer Learning for Brain-Computer Interfaces: A Euclidean Space Data Alignment Approach |
| URI | https://ieeexplore.ieee.org/document/8701679 https://www.ncbi.nlm.nih.gov/pubmed/31034407 https://www.proquest.com/docview/2345514173 https://www.proquest.com/docview/2217484039 |
| Volume | 67 |
| WOSCitedRecordID | wos000510903800008&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/eLvHCXMwlV1Na9wwEB2SUEp76EfSj23ToEJPpU4kWbak3jbthl4SCk1h6cXI8igsLN6y2e3vr0bWmhzaQm8Gy7LxjNA8zcx7AO-U5AFbXxeqFbZQEnnhlBCFM8p2vDJ1cG0Sm9BXV2Y-t1_34MPYC4OIqfgMT-ky5fK7ld_SUdlZ9C3KGuzDvtZ66NUaMwbKDE05XMQFLK3KGUzB7dn1-eWMirjsqSQSTEFaPCSvpRSJyN7ZjpK-yt9DzbTlXDz-v499Ao9yaMmmgy88hT3sD-HhHcLBQ7h_mVPpR_Aj7VIB1yxTrN6wGL-yc5KMKHZaDywdGAYq2_rIpmy29ctFh65n3yLURvbZbRybLhc3qaSATTM_-TP4fjG7_vSlyEILhS-V3RS2a1GGUEdgHSqrWtlx53TlVdXG-I0I3AX3FtG0VSfLUEeQpbVx1NPKiUa0fA4H_arHl8A8keHEGAexVRH5GRNKFyOmWgeM27HgE-C7_934zEJOYhjLJqERbhuyVkPWarK1JvB-fOTnQMHxr8FHZIpxYLbCBI53Rm3yIr1tZKkoXhS6nMDb8XZcXpQzcT2utnEMQbYIgss4xYvBGca5dz706s_vfA0PJIHzVOJ9DAeb9RbfwD3_a7O4XZ9EH56bk-TDvwE76eg- |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lb9QwEB6VgigcePQBCwWMxAmR1nach7ltYasiuiskFqniEjnOuFppla22u_x-PI436gEq9RYpjhNlxvJ8npnvA_igJHdY2zxRtdCJksgTo4RITKl0w7Myd6YOYhPFZFJeXOgfW_Cp74VBxFB8hkd0GXL5zcKu6ajs2PsWZQ3uwf1MKSm6bq0-Z6DKri2HC7-EpVYxhym4Pp6ejEdUxqWPJNFgClLjIYEtpUhG9saGFBRW_h9shk3n9OndPvcZPInBJRt23vActrDdhcc3KAd34eE4JtP34HfYpxwuWSRZvWQ-gmUnJBqRbNQeWDgydFS49ZkN2Wht57MGTct-erCN7KtZGTaczy5DUQEbRobyffh1Opp-OUui1EJiU6VXiW5qlM7lHlq7TKtaNtyYIrMqq30ERxTugluNWNZZI1OXe5hVFKWhrlZORKLpAWy3ixZfArNEh-OjHMRaeexXli41PmbKC4d-QxZ8AHzzvysbechJDmNeBTzCdUXWqshaVbTWAD72j1x1JBy3Dd4jU_QDoxUGcLgxahWX6XUlU0URoyjSAbzvb_sFRlkT0-Ji7ccQaPMwOPVTvOicoZ9740Ov_v3Od7BzNh2fV-ffJt9fwyNJUD0UfB_C9mq5xjfwwP5Zza6Xb4Mn_wUAH-qd |
| 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=Transfer+Learning+for+Brain-Computer+Interfaces%3A+A+Euclidean+Space+Data+Alignment+Approach&rft.jtitle=IEEE+transactions+on+biomedical+engineering&rft.au=He%2C+He&rft.au=Wu%2C+Dongrui&rft.date=2020-02-01&rft.issn=1558-2531&rft.eissn=1558-2531&rft.volume=67&rft.issue=2&rft.spage=399&rft_id=info:doi/10.1109%2FTBME.2019.2913914&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 |