File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: ICA-SVM combination algorithm for identification of motor imagery potentials

TitleICA-SVM combination algorithm for identification of motor imagery potentials
Authors
Ming, DSun, CCheng, LBai, YLiu, XAn, XQi, HWan, BHu, YLuk, K
KeywordsBrain-computer interface (BCI)
ERD/ERS coefficient
Event-related desynchronization/synchronous (ERD/ERS)
Independent component analysis (ICA)
Power spectral density (PSD)
Support vector machine (SVM)
Issue Date2010
PublisherIEEE.
Citation
The 2010 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA), Taranto, Apulia, Italy, 6-8 September 2010. In Conference Proceedings, 2010, p. 92-96 How to Cite?
DOI: http://dx.doi.org/10.1109/CIMSA.2010.5611755
AbstractMental tasks such as motor imagery in synchronization with a cue which result event related desynchronization (ERD) and event related synchronization (ERS) are usually studied in brain-computer interface (BCI) system. In this paper we analyze and classify the ERD/ERS response evoked by the motor imagery of left hand, right hand, foot and tongue. The signals were spatially filtered by Independent Component Analysis (ICA) before calculating the power spectral density (PSD) for related electrodes, and then the Support Vector Machine (SVM) was adopted to recognise the different imagery pattern according to ERD/ERS feature for the signals. The results showed that the combination of ICA-based signal extraction algorithm and SVM-based classification method was an effective tool for the identification of motor imagery potentials, with the highest accuracy rate of 91.4% and 77.6% for the lowest. © 2010 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/142879
ISBN
978-1-4244-7230-7
References
References in Scopus

 

DC FieldValueLanguage
dc.contributor.authorMing, Den_HK
dc.contributor.authorSun, Cen_HK
dc.contributor.authorCheng, Len_HK
dc.contributor.authorBai, Yen_HK
dc.contributor.authorLiu, Xen_HK
dc.contributor.authorAn, Xen_HK
dc.contributor.authorQi, Hen_HK
dc.contributor.authorWan, Ben_HK
dc.contributor.authorHu, Yen_HK
dc.contributor.authorLuk, Ken_HK
dc.date.accessioned2011-10-28T02:57:59Z-
dc.date.available2011-10-28T02:57:59Z-
dc.date.issued2010en_HK
dc.identifier.citationThe 2010 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA), Taranto, Apulia, Italy, 6-8 September 2010. In Conference Proceedings, 2010, p. 92-96en_US
dc.identifier.isbn978-1-4244-7230-7en_US
dc.identifier.urihttp://hdl.handle.net/10722/142879-
dc.description.abstractMental tasks such as motor imagery in synchronization with a cue which result event related desynchronization (ERD) and event related synchronization (ERS) are usually studied in brain-computer interface (BCI) system. In this paper we analyze and classify the ERD/ERS response evoked by the motor imagery of left hand, right hand, foot and tongue. The signals were spatially filtered by Independent Component Analysis (ICA) before calculating the power spectral density (PSD) for related electrodes, and then the Support Vector Machine (SVM) was adopted to recognise the different imagery pattern according to ERD/ERS feature for the signals. The results showed that the combination of ICA-based signal extraction algorithm and SVM-based classification method was an effective tool for the identification of motor imagery potentials, with the highest accuracy rate of 91.4% and 77.6% for the lowest. © 2010 IEEE.en_HK
dc.languageengen_US
dc.publisherIEEE.-
dc.relation.ispartofProceedings of IEEE International Conference on Computational Intelligence for Measurement Systems & Applications, CIMSA 2010en_HK
dc.rights©2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.subjectBrain-computer interface (BCI)en_HK
dc.subjectERD/ERS coefficienten_HK
dc.subjectEvent-related desynchronization/synchronous (ERD/ERS)en_HK
dc.subjectIndependent component analysis (ICA)en_HK
dc.subjectPower spectral density (PSD)en_HK
dc.subjectSupport vector machine (SVM)en_HK
dc.titleICA-SVM combination algorithm for identification of motor imagery potentialsen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=978-1-4244-7230-7&volume=&spage=92&epage=&date=2010&atitle=ICA-SVM+combination+algorithm+for+identification+of+motor+imagery+potentialsen_US
dc.identifier.emailHu, Y:yhud@hku.hken_HK
dc.identifier.emailLuk, K:hcm21000@hku.hken_HK
dc.identifier.authorityHu, Y=rp00432en_HK
dc.identifier.authorityLuk, K=rp00333en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/CIMSA.2010.5611755en_HK
dc.identifier.scopuseid_2-s2.0-78649543244en_HK
dc.identifier.hkuros197089en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-78649543244&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage92en_HK
dc.identifier.epage96en_HK
dc.description.otherThe 2010 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA), Taranto, Apulia, Italy, 6-8 September 2010. In Proceedings of IEEE-CIMSA, 2010, p. 92-96-
dc.identifier.scopusauthoridMing, D=9745824400en_HK
dc.identifier.scopusauthoridSun, C=36648145900en_HK
dc.identifier.scopusauthoridCheng, L=35780976700en_HK
dc.identifier.scopusauthoridBai, Y=35108689200en_HK
dc.identifier.scopusauthoridLiu, X=35109400600en_HK
dc.identifier.scopusauthoridAn, X=35975600200en_HK
dc.identifier.scopusauthoridQi, H=7202348852en_HK
dc.identifier.scopusauthoridWan, B=7102316798en_HK
dc.identifier.scopusauthoridHu, Y=7407116091en_HK
dc.identifier.scopusauthoridLuk, K=7201921573en_HK
dc.customcontrol.immutablesml 170512 amended-

Export via OAI-PMH Interface in XML Formats

OR

Export to Other Non-XML Formats