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Conference Paper: Spontaneous EEG-based normalization of pain-evoked neural responses: effect on improving the accuracy of pain prediction

TitleSpontaneous EEG-based normalization of pain-evoked neural responses: effect on improving the accuracy of pain prediction
Authors
KeywordsCross-individual
Normalization
Pain prediction
Pain-evoked EEG
Spontaneous EEG
Issue Date2016
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6598376
Citation
The 2016 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA 2016), Budapest, Hungary, 27-28 July 2016. In Conference Proceedings, 2016, p. 1-4 How to Cite?
AbstractEEG-based pain assessment methods has been widely accepted in recent years. However, performance of cross-individual prediction degraded considerably due to the substantial inter-individual variability in pain-evoked EEG responses. This study aims to improve the accuracy of cross-individual pain prediction via reducing the inter-individual variability. Motivated by our finding that an individual's pain-evoked EEG responses is significantly correlated with his/her spontaneous EEG in terms of magnitude, we proposed a normalization method for pain-evoked EEG responses using one's spontaneous EEG to reduce the inter-individual variability. Continuous prediction for pain trials using spontaneous-EEG-normalized magnitudes of evoked EEG responses as features was developed. Results show that the proposed normalization strategy can effectively reduce the inter-individual variability in pain-evoked responses and lead to a higher prediction accuracy. © 2016 IEEE.
DescriptionTechnical Papers - Session 4: Computational Intelligence for Medical and Bioengineering Applications
Persistent Identifierhttp://hdl.handle.net/10722/232281
ISBN

 

DC FieldValueLanguage
dc.contributor.authorBai, Y-
dc.contributor.authorHu, Y-
dc.contributor.authorZhang, Z-
dc.date.accessioned2016-09-20T05:28:56Z-
dc.date.available2016-09-20T05:28:56Z-
dc.date.issued2016-
dc.identifier.citationThe 2016 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA 2016), Budapest, Hungary, 27-28 July 2016. In Conference Proceedings, 2016, p. 1-4-
dc.identifier.isbn978-146739759-9-
dc.identifier.urihttp://hdl.handle.net/10722/232281-
dc.descriptionTechnical Papers - Session 4: Computational Intelligence for Medical and Bioengineering Applications-
dc.description.abstractEEG-based pain assessment methods has been widely accepted in recent years. However, performance of cross-individual prediction degraded considerably due to the substantial inter-individual variability in pain-evoked EEG responses. This study aims to improve the accuracy of cross-individual pain prediction via reducing the inter-individual variability. Motivated by our finding that an individual's pain-evoked EEG responses is significantly correlated with his/her spontaneous EEG in terms of magnitude, we proposed a normalization method for pain-evoked EEG responses using one's spontaneous EEG to reduce the inter-individual variability. Continuous prediction for pain trials using spontaneous-EEG-normalized magnitudes of evoked EEG responses as features was developed. Results show that the proposed normalization strategy can effectively reduce the inter-individual variability in pain-evoked responses and lead to a higher prediction accuracy. © 2016 IEEE.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6598376-
dc.relation.ispartofIEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications Proceedings-
dc.rightsIEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications Proceedings. Copyright © IEEE.-
dc.rights©2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.subjectCross-individual-
dc.subjectNormalization-
dc.subjectPain prediction-
dc.subjectPain-evoked EEG-
dc.subjectSpontaneous EEG-
dc.titleSpontaneous EEG-based normalization of pain-evoked neural responses: effect on improving the accuracy of pain prediction-
dc.typeConference_Paper-
dc.identifier.emailBai, Y: tsdwx56@hku.hk-
dc.identifier.emailHu, Y: yhud@hku.hk-
dc.identifier.emailZhang, Z: zhangzg@hku.hk-
dc.identifier.authorityHu, Y=rp00432-
dc.identifier.authorityZhang, Z=rp01565-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1109/CIVEMSA.2016.7524316-
dc.identifier.scopuseid_2-s2.0-84984656672-
dc.identifier.hkuros263975-
dc.identifier.spage1-
dc.identifier.epage4-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 160923-

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