Classifying the multiplicity of the EEG source models using sphere-shaped support vector Machines
Support vector machines (SVMs) are learning algorithms derived from statistical learning theory, and originally designed to solve binary classification problems. How to effectively extend SVMs for multiclass classification problems is still an ongoing research issue. In this paper, a sphere-shaped S...
Gespeichert in:
| Veröffentlicht in: | IEEE transactions on magnetics 2005-05, Vol.41 (5), p.1912-1915 |
|---|---|
| Hauptverfasser: | , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 1915 |
|---|---|
| container_issue | 5 |
| container_start_page | 1912 |
| container_title | IEEE transactions on magnetics |
| container_volume | 41 |
| creator | Qing Wu, Qing Wu Xueqin Shen, Xueqin Shen Ying Li, Ying Li Guizhi Xu, Guizhi Xu Weili Yan, Weili Yan Guoya Dong, Guoya Dong Qingxin Yang, Qingxin Yang |
| description | Support vector machines (SVMs) are learning algorithms derived from statistical learning theory, and originally designed to solve binary classification problems. How to effectively extend SVMs for multiclass classification problems is still an ongoing research issue. In this paper, a sphere-shaped SVM for multiclass problems is presented. Compared with the classical plane-shaped SVMs, the number of convex quadratic programming problems and the number of variables in each programming are smaller. Such SVM classifier is applied to the electroencephalogram (EEG) source localization problem, and the multiplicity of source models is determined according to the potentials recorded on the scalp. Experimental results indicate that the sphere-shaped SVM based classifier is an effective and promising approach for this task. |
| doi_str_mv | 10.1109/TMAG.2005.846231 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_ieee_primary_1430997</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>1430997</ieee_id><sourcerecordid>27975952</sourcerecordid><originalsourceid>FETCH-LOGICAL-c383t-e903b3129880defccd223aa8c4142dca1572608b1d916f0bb95349dc647f4d253</originalsourceid><addsrcrecordid>eNp90U2LFDEQBuAgCo6rd8FLI6inHiufnRyXYZwVdvGynkMmXe1k6eluU93C_HszzsKChz2FpJ4qqLyMveew5hzc1_u7691aAOi1VUZI_oKtuFO8BjDuJVsBcFs7ZdRr9obooVyV5rBiYdMHotSd0vCrmg9YHZd-TlOfYppP1dj9e9tudxWNS46lPLbYU7XQ2dN0wIw1HcKEbUXLNI15rv5gnMdc3YV4SAPSW_aqCz3hu8fziv38tr3f3NS3P3bfN9e3dZRWzjU6kHvJhbMWWuxibIWQIdiouBJtDFw3woDd89Zx08F-77RUro1GNZ1qhZZX7Mtl7pTH3wvS7I-JIvZ9GHBcyFtnhNK2cUV-flaKxjXaaVHgx__gQ_mFoWzhrWmUdkKaguCCYh6JMnZ-yukY8slz8Odo_Dkaf47GX6IpLZ8e5waKoe9yGGKipz5jFUiA4j5cXELEp7KS4Fwj_wL5Ypbk</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>867459236</pqid></control><display><type>article</type><title>Classifying the multiplicity of the EEG source models using sphere-shaped support vector Machines</title><source>IEEE Electronic Library (IEL)</source><creator>Qing Wu, Qing Wu ; Xueqin Shen, Xueqin Shen ; Ying Li, Ying Li ; Guizhi Xu, Guizhi Xu ; Weili Yan, Weili Yan ; Guoya Dong, Guoya Dong ; Qingxin Yang, Qingxin Yang</creator><creatorcontrib>Qing Wu, Qing Wu ; Xueqin Shen, Xueqin Shen ; Ying Li, Ying Li ; Guizhi Xu, Guizhi Xu ; Weili Yan, Weili Yan ; Guoya Dong, Guoya Dong ; Qingxin Yang, Qingxin Yang</creatorcontrib><description>Support vector machines (SVMs) are learning algorithms derived from statistical learning theory, and originally designed to solve binary classification problems. How to effectively extend SVMs for multiclass classification problems is still an ongoing research issue. In this paper, a sphere-shaped SVM for multiclass problems is presented. Compared with the classical plane-shaped SVMs, the number of convex quadratic programming problems and the number of variables in each programming are smaller. Such SVM classifier is applied to the electroencephalogram (EEG) source localization problem, and the multiplicity of source models is determined according to the potentials recorded on the scalp. Experimental results indicate that the sphere-shaped SVM based classifier is an effective and promising approach for this task.</description><identifier>ISSN: 0018-9464</identifier><identifier>EISSN: 1941-0069</identifier><identifier>DOI: 10.1109/TMAG.2005.846231</identifier><identifier>CODEN: IEMGAQ</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Brain modeling ; Computer science ; Cross-disciplinary physics: materials science; rheology ; EEG source model ; Electroencephalography ; Electromagnetic fields ; Electromagnetic modeling ; Exact sciences and technology ; Magnetism ; Materials science ; multiclass classification ; Other topics in materials science ; Physics ; Predictive models ; Quadratic programming ; Scalp ; sphere classifier ; support vector machine ; Support vector machine classification ; Support vector machines</subject><ispartof>IEEE transactions on magnetics, 2005-05, Vol.41 (5), p.1912-1915</ispartof><rights>2005 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2005</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c383t-e903b3129880defccd223aa8c4142dca1572608b1d916f0bb95349dc647f4d253</citedby><cites>FETCH-LOGICAL-c383t-e903b3129880defccd223aa8c4142dca1572608b1d916f0bb95349dc647f4d253</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/1430997$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,314,780,784,789,790,796,23930,23931,25140,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/1430997$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=16840300$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Qing Wu, Qing Wu</creatorcontrib><creatorcontrib>Xueqin Shen, Xueqin Shen</creatorcontrib><creatorcontrib>Ying Li, Ying Li</creatorcontrib><creatorcontrib>Guizhi Xu, Guizhi Xu</creatorcontrib><creatorcontrib>Weili Yan, Weili Yan</creatorcontrib><creatorcontrib>Guoya Dong, Guoya Dong</creatorcontrib><creatorcontrib>Qingxin Yang, Qingxin Yang</creatorcontrib><title>Classifying the multiplicity of the EEG source models using sphere-shaped support vector Machines</title><title>IEEE transactions on magnetics</title><addtitle>TMAG</addtitle><description>Support vector machines (SVMs) are learning algorithms derived from statistical learning theory, and originally designed to solve binary classification problems. How to effectively extend SVMs for multiclass classification problems is still an ongoing research issue. In this paper, a sphere-shaped SVM for multiclass problems is presented. Compared with the classical plane-shaped SVMs, the number of convex quadratic programming problems and the number of variables in each programming are smaller. Such SVM classifier is applied to the electroencephalogram (EEG) source localization problem, and the multiplicity of source models is determined according to the potentials recorded on the scalp. Experimental results indicate that the sphere-shaped SVM based classifier is an effective and promising approach for this task.</description><subject>Brain modeling</subject><subject>Computer science</subject><subject>Cross-disciplinary physics: materials science; rheology</subject><subject>EEG source model</subject><subject>Electroencephalography</subject><subject>Electromagnetic fields</subject><subject>Electromagnetic modeling</subject><subject>Exact sciences and technology</subject><subject>Magnetism</subject><subject>Materials science</subject><subject>multiclass classification</subject><subject>Other topics in materials science</subject><subject>Physics</subject><subject>Predictive models</subject><subject>Quadratic programming</subject><subject>Scalp</subject><subject>sphere classifier</subject><subject>support vector machine</subject><subject>Support vector machine classification</subject><subject>Support vector machines</subject><issn>0018-9464</issn><issn>1941-0069</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2005</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNp90U2LFDEQBuAgCo6rd8FLI6inHiufnRyXYZwVdvGynkMmXe1k6eluU93C_HszzsKChz2FpJ4qqLyMveew5hzc1_u7691aAOi1VUZI_oKtuFO8BjDuJVsBcFs7ZdRr9obooVyV5rBiYdMHotSd0vCrmg9YHZd-TlOfYppP1dj9e9tudxWNS46lPLbYU7XQ2dN0wIw1HcKEbUXLNI15rv5gnMdc3YV4SAPSW_aqCz3hu8fziv38tr3f3NS3P3bfN9e3dZRWzjU6kHvJhbMWWuxibIWQIdiouBJtDFw3woDd89Zx08F-77RUro1GNZ1qhZZX7Mtl7pTH3wvS7I-JIvZ9GHBcyFtnhNK2cUV-flaKxjXaaVHgx__gQ_mFoWzhrWmUdkKaguCCYh6JMnZ-yukY8slz8Odo_Dkaf47GX6IpLZ8e5waKoe9yGGKipz5jFUiA4j5cXELEp7KS4Fwj_wL5Ypbk</recordid><startdate>20050501</startdate><enddate>20050501</enddate><creator>Qing Wu, Qing Wu</creator><creator>Xueqin Shen, Xueqin Shen</creator><creator>Ying Li, Ying Li</creator><creator>Guizhi Xu, Guizhi Xu</creator><creator>Weili Yan, Weili Yan</creator><creator>Guoya Dong, Guoya Dong</creator><creator>Qingxin Yang, Qingxin Yang</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope><scope>L7M</scope><scope>7TK</scope></search><sort><creationdate>20050501</creationdate><title>Classifying the multiplicity of the EEG source models using sphere-shaped support vector Machines</title><author>Qing Wu, Qing Wu ; Xueqin Shen, Xueqin Shen ; Ying Li, Ying Li ; Guizhi Xu, Guizhi Xu ; Weili Yan, Weili Yan ; Guoya Dong, Guoya Dong ; Qingxin Yang, Qingxin Yang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c383t-e903b3129880defccd223aa8c4142dca1572608b1d916f0bb95349dc647f4d253</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Brain modeling</topic><topic>Computer science</topic><topic>Cross-disciplinary physics: materials science; rheology</topic><topic>EEG source model</topic><topic>Electroencephalography</topic><topic>Electromagnetic fields</topic><topic>Electromagnetic modeling</topic><topic>Exact sciences and technology</topic><topic>Magnetism</topic><topic>Materials science</topic><topic>multiclass classification</topic><topic>Other topics in materials science</topic><topic>Physics</topic><topic>Predictive models</topic><topic>Quadratic programming</topic><topic>Scalp</topic><topic>sphere classifier</topic><topic>support vector machine</topic><topic>Support vector machine classification</topic><topic>Support vector machines</topic><toplevel>online_resources</toplevel><creatorcontrib>Qing Wu, Qing Wu</creatorcontrib><creatorcontrib>Xueqin Shen, Xueqin Shen</creatorcontrib><creatorcontrib>Ying Li, Ying Li</creatorcontrib><creatorcontrib>Guizhi Xu, Guizhi Xu</creatorcontrib><creatorcontrib>Weili Yan, Weili Yan</creatorcontrib><creatorcontrib>Guoya Dong, Guoya Dong</creatorcontrib><creatorcontrib>Qingxin Yang, Qingxin Yang</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Neurosciences Abstracts</collection><jtitle>IEEE transactions on magnetics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Qing Wu, Qing Wu</au><au>Xueqin Shen, Xueqin Shen</au><au>Ying Li, Ying Li</au><au>Guizhi Xu, Guizhi Xu</au><au>Weili Yan, Weili Yan</au><au>Guoya Dong, Guoya Dong</au><au>Qingxin Yang, Qingxin Yang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Classifying the multiplicity of the EEG source models using sphere-shaped support vector Machines</atitle><jtitle>IEEE transactions on magnetics</jtitle><stitle>TMAG</stitle><date>2005-05-01</date><risdate>2005</risdate><volume>41</volume><issue>5</issue><spage>1912</spage><epage>1915</epage><pages>1912-1915</pages><issn>0018-9464</issn><eissn>1941-0069</eissn><coden>IEMGAQ</coden><abstract>Support vector machines (SVMs) are learning algorithms derived from statistical learning theory, and originally designed to solve binary classification problems. How to effectively extend SVMs for multiclass classification problems is still an ongoing research issue. In this paper, a sphere-shaped SVM for multiclass problems is presented. Compared with the classical plane-shaped SVMs, the number of convex quadratic programming problems and the number of variables in each programming are smaller. Such SVM classifier is applied to the electroencephalogram (EEG) source localization problem, and the multiplicity of source models is determined according to the potentials recorded on the scalp. Experimental results indicate that the sphere-shaped SVM based classifier is an effective and promising approach for this task.</abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/TMAG.2005.846231</doi><tpages>4</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 0018-9464 |
| ispartof | IEEE transactions on magnetics, 2005-05, Vol.41 (5), p.1912-1915 |
| issn | 0018-9464 1941-0069 |
| language | eng |
| recordid | cdi_ieee_primary_1430997 |
| source | IEEE Electronic Library (IEL) |
| subjects | Brain modeling Computer science Cross-disciplinary physics: materials science rheology EEG source model Electroencephalography Electromagnetic fields Electromagnetic modeling Exact sciences and technology Magnetism Materials science multiclass classification Other topics in materials science Physics Predictive models Quadratic programming Scalp sphere classifier support vector machine Support vector machine classification Support vector machines |
| title | Classifying the multiplicity of the EEG source models using sphere-shaped support vector Machines |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-05T11%3A43%3A33IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Classifying%20the%20multiplicity%20of%20the%20EEG%20source%20models%20using%20sphere-shaped%20support%20vector%20Machines&rft.jtitle=IEEE%20transactions%20on%20magnetics&rft.au=Qing%20Wu,%20Qing%20Wu&rft.date=2005-05-01&rft.volume=41&rft.issue=5&rft.spage=1912&rft.epage=1915&rft.pages=1912-1915&rft.issn=0018-9464&rft.eissn=1941-0069&rft.coden=IEMGAQ&rft_id=info:doi/10.1109/TMAG.2005.846231&rft_dat=%3Cproquest_RIE%3E27975952%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=867459236&rft_id=info:pmid/&rft_ieee_id=1430997&rfr_iscdi=true |