{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T14:29:42Z","timestamp":1776436182614,"version":"3.51.2"},"reference-count":17,"publisher":"Wiley","license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004751","name":"Department of S and T for Social Development","doi-asserted-by":"publisher","award":["2014BAF08B01"],"award-info":[{"award-number":["2014BAF08B01"]}],"id":[{"id":"10.13039\/501100004751","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004751","name":"Department of S and T for Social Development","doi-asserted-by":"publisher","award":["IRT1203"],"award-info":[{"award-number":["IRT1203"]}],"id":[{"id":"10.13039\/501100004751","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002338","name":"Ministry of Education of the People\u2019s Republic of China","doi-asserted-by":"publisher","award":["2014BAF08B01"],"award-info":[{"award-number":["2014BAF08B01"]}],"id":[{"id":"10.13039\/501100002338","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002338","name":"Ministry of Education of the People\u2019s Republic of China","doi-asserted-by":"publisher","award":["IRT1203"],"award-info":[{"award-number":["IRT1203"]}],"id":[{"id":"10.13039\/501100002338","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational and Mathematical Methods in Medicine"],"published-print":{"date-parts":[[2018]]},"abstract":"<jats:p>Motor-imagery-based brain-computer interfaces (BCIs) commonly use the common spatial pattern (CSP) as preprocessing step before classification. The CSP method is a supervised algorithm. Therefore a lot of time-consuming training data is needed to build the model. To address this issue, one promising approach is transfer learning, which generalizes a learning model can extract discriminative information from other subjects for target classification task. To this end, we propose a transfer kernel CSP (TKCSP) approach to learn a domain-invariant kernel by directly matching distributions of source subjects and target subjects. The dataset IVa of BCI Competition III is used to demonstrate the validity by our proposed methods. In the experiment, we compare the classification performance of the TKCSP against CSP, CSP for subject-to-subject transfer (CSP SJ-to-SJ), regularizing CSP (RCSP), stationary subspace CSP (ssCSP), multitask CSP (mtCSP), and the combined mtCSP and ssCSP (ss + mtCSP) method. The results indicate that the superior mean classification performance of TKCSP can achieve 81.14%, especially in case of source subjects with fewer number of training samples. Comprehensive experimental evidence on the dataset verifies the effectiveness and efficiency of the proposed TKCSP approach over several state-of-the-art methods.<\/jats:p>","DOI":"10.1155\/2018\/9871603","type":"journal-article","created":{"date-parts":[[2018,3,18]],"date-time":"2018-03-18T19:37:04Z","timestamp":1521401824000},"page":"1-9","source":"Crossref","is-referenced-by-count":68,"title":["Transfer Kernel Common Spatial Patterns for Motor Imagery Brain-Computer Interface Classification"],"prefix":"10.1155","volume":"2018","author":[{"given":"Mengxi","family":"Dai","sequence":"first","affiliation":[{"name":"Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing 100191, China"},{"name":"Key Laboratory of Precision Opto-Mechatronics Technology, Ministry of Education, School of Instrumentation Science and Opto-Electronics Engineering, Beihang University, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3998-5989","authenticated-orcid":true,"given":"Dezhi","family":"Zheng","sequence":"additional","affiliation":[{"name":"Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing 100191, China"},{"name":"Key Laboratory of Precision Opto-Mechatronics Technology, Ministry of Education, School of Instrumentation Science and Opto-Electronics Engineering, Beihang University, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shucong","family":"Liu","sequence":"additional","affiliation":[{"name":"Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing 100191, China"},{"name":"Key Laboratory of Precision Opto-Mechatronics Technology, Ministry of Education, School of Instrumentation Science and Opto-Electronics Engineering, Beihang University, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengju","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing 100191, China"},{"name":"Key Laboratory of Precision Opto-Mechatronics Technology, Ministry of Education, School of Instrumentation Science and Opto-Electronics Engineering, Beihang University, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2008.431"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2003.1166626"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1016\/S1388-2457(02)00057-3"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1155\/2016\/4941235"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1109\/tkde.2009.191"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2014.2373376"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2009.2022557"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1155\/2011\/217987"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2013.2253608"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2011.10.024"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1109\/86.895946"},{"key":"16","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2560\/12\/3\/036004"},{"key":"20","doi-asserted-by":"publisher","DOI":"10.1109\/tit.2007.911294"},{"key":"21","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2013.2271378"},{"key":"23","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2011.2178556"},{"key":"25","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2006.875642"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1109\/tbme.2010.2082539"}],"container-title":["Computational and Mathematical Methods in Medicine"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cmmm\/2018\/9871603.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cmmm\/2018\/9871603.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cmmm\/2018\/9871603.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2018,3,18]],"date-time":"2018-03-18T19:37:52Z","timestamp":1521401872000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/cmmm\/2018\/9871603\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"references-count":17,"alternative-id":["9871603","9871603"],"URL":"https:\/\/doi.org\/10.1155\/2018\/9871603","relation":{},"ISSN":["1748-670X","1748-6718"],"issn-type":[{"value":"1748-670X","type":"print"},{"value":"1748-6718","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018]]}}}