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Conference Paper: Brain-computer interface technique for electro-acupuncture stimulation control
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TitleBrain-computer interface technique for electro-acupuncture stimulation control
 
AuthorsMing, D2
Bai, Y2
Liu, X2
An, X2
Qi, H2
Wan, B2
Hu, Y1
Luk, KDK1
 
KeywordsBrain-computer interface
Electro-acupuncture stimulation
Event related desynchronization
Imaginary movement
Mahalanobis classifier
 
Issue Date2010
 
PublisherIEEE.
 
CitationThe 2010 IEEE International Conference on Virtual Environments Human-Computer Interfaces and Measurement Systems (VECIMS), Taranto, Apulia, Italy, 6-8 September 2010. In Proceedings of the IEEE-VECIMS, , 2010, p. 33-37 [How to Cite?]
DOI: http://dx.doi.org/10.1109/VECIMS.2010.5609342
 
AbstractElectro-acupuncture stimulation (EAS) technique applies the electrical nerve stimulation therapy on traditional acupuncture points to restore the muscle tension. The rapid rise and development of brain-computer interface (BCI) technology makes the thought-control of EAS possible. This paper designed a new BCI-controls-EAS (BCICEAS) system by using event related desynchronization (ERD) of EEG signal evoked by imaginary movement. The Fisher parameters were extracted from feature frequency bands of EEG and classified into EAS control commands by Mahalanobis Classifier. A feedback training technique was introduced to enhance the signal feature through a visual feedback interface with a virtual liquid column, which height varied along with EEG power spectral feature. Experimental results demonstrated the validity of the proposed method, including the effective improvement of feedback training on signal feature and reliable control of EAS. It is hoped the BCICEAS can explore a new way for EAS system design and help people who sufferers with severe movement dysfunction. © 2010 IEEE.
 
ISBN978-1-4244-5905-6
 
DOIhttp://dx.doi.org/10.1109/VECIMS.2010.5609342
 
ReferencesReferences in Scopus
 
DC FieldValue
dc.contributor.authorMing, D
 
dc.contributor.authorBai, Y
 
dc.contributor.authorLiu, X
 
dc.contributor.authorAn, X
 
dc.contributor.authorQi, H
 
dc.contributor.authorWan, B
 
dc.contributor.authorHu, Y
 
dc.contributor.authorLuk, KDK
 
dc.date.accessioned2011-10-28T02:57:58Z
 
dc.date.available2011-10-28T02:57:58Z
 
dc.date.issued2010
 
dc.description.abstractElectro-acupuncture stimulation (EAS) technique applies the electrical nerve stimulation therapy on traditional acupuncture points to restore the muscle tension. The rapid rise and development of brain-computer interface (BCI) technology makes the thought-control of EAS possible. This paper designed a new BCI-controls-EAS (BCICEAS) system by using event related desynchronization (ERD) of EEG signal evoked by imaginary movement. The Fisher parameters were extracted from feature frequency bands of EEG and classified into EAS control commands by Mahalanobis Classifier. A feedback training technique was introduced to enhance the signal feature through a visual feedback interface with a virtual liquid column, which height varied along with EEG power spectral feature. Experimental results demonstrated the validity of the proposed method, including the effective improvement of feedback training on signal feature and reliable control of EAS. It is hoped the BCICEAS can explore a new way for EAS system design and help people who sufferers with severe movement dysfunction. © 2010 IEEE.
 
dc.description.naturepublished_or_final_version
 
dc.description.otherThe 2010 IEEE International Conference on Virtual Environments Human-Computer Interfaces and Measurement Systems (VECIMS), Taranto, Apulia, Italy, 6-8 September 2010. In Proceedings of the IEEE-VECIMS, , 2010, p. 33-37
 
dc.identifier.citationThe 2010 IEEE International Conference on Virtual Environments Human-Computer Interfaces and Measurement Systems (VECIMS), Taranto, Apulia, Italy, 6-8 September 2010. In Proceedings of the IEEE-VECIMS, , 2010, p. 33-37 [How to Cite?]
DOI: http://dx.doi.org/10.1109/VECIMS.2010.5609342
 
dc.identifier.doihttp://dx.doi.org/10.1109/VECIMS.2010.5609342
 
dc.identifier.epage37
 
dc.identifier.hkuros197086
 
dc.identifier.isbn978-1-4244-5905-6
 
dc.identifier.openurl
 
dc.identifier.scopuseid_2-s2.0-78650122411
 
dc.identifier.spage33
 
dc.identifier.urihttp://hdl.handle.net/10722/142877
 
dc.languageeng
 
dc.publisherIEEE.
 
dc.relation.ispartofVECIMS 2010 - 2010 IEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurement Systems, Proceedings
 
dc.relation.referencesReferences in Scopus
 
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License
 
dc.rightsIEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurements Systems Proceedings. Copyright © IEEE.
 
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
 
dc.subjectElectro-acupuncture stimulation
 
dc.subjectEvent related desynchronization
 
dc.subjectImaginary movement
 
dc.subjectMahalanobis classifier
 
dc.titleBrain-computer interface technique for electro-acupuncture stimulation control
 
dc.typeConference_Paper
 
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<description.abstract>Electro-acupuncture stimulation (EAS) technique applies the electrical nerve stimulation therapy on traditional acupuncture points to restore the muscle tension. The rapid rise and development of brain-computer interface (BCI) technology makes the thought-control of EAS possible. This paper designed a new BCI-controls-EAS (BCICEAS) system by using event related desynchronization (ERD) of EEG signal evoked by imaginary movement. The Fisher parameters were extracted from feature frequency bands of EEG and classified into EAS control commands by Mahalanobis Classifier. A feedback training technique was introduced to enhance the signal feature through a visual feedback interface with a virtual liquid column, which height varied along with EEG power spectral feature. Experimental results demonstrated the validity of the proposed method, including the effective improvement of feedback training on signal feature and reliable control of EAS. It is hoped the BCICEAS can explore a new way for EAS system design and help people who sufferers with severe movement dysfunction. &#169; 2010 IEEE.</description.abstract>
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Author Affiliations
  1. The University of Hong Kong
  2. Tianjin University