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- Publisher Website: 10.1109/CIVEMSA.2016.7524316
- Scopus: eid_2-s2.0-84984656672
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Conference Paper: Spontaneous EEG-based normalization of pain-evoked neural responses: effect on improving the accuracy of pain prediction
Title | Spontaneous EEG-based normalization of pain-evoked neural responses: effect on improving the accuracy of pain prediction |
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Authors | |
Keywords | Cross-individual Normalization Pain prediction Pain-evoked EEG Spontaneous EEG |
Issue Date | 2016 |
Publisher | IEEE. 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? |
Abstract | EEG-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. |
Description | Technical Papers - Session 4: Computational Intelligence for Medical and Bioengineering Applications |
Persistent Identifier | http://hdl.handle.net/10722/232281 |
ISBN |
DC Field | Value | Language |
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dc.contributor.author | Bai, Y | - |
dc.contributor.author | Hu, Y | - |
dc.contributor.author | Zhang, Z | - |
dc.date.accessioned | 2016-09-20T05:28:56Z | - |
dc.date.available | 2016-09-20T05:28:56Z | - |
dc.date.issued | 2016 | - |
dc.identifier.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 | - |
dc.identifier.isbn | 978-146739759-9 | - |
dc.identifier.uri | http://hdl.handle.net/10722/232281 | - |
dc.description | Technical Papers - Session 4: Computational Intelligence for Medical and Bioengineering Applications | - |
dc.description.abstract | EEG-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.language | eng | - |
dc.publisher | IEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6598376 | - |
dc.relation.ispartof | Proceedings of IEEE International Conference on Computational Intelligence & Virtual Environments for Measurement Systems & Applications, CIVEMSA 2016 | - |
dc.rights | IEEE 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.subject | Cross-individual | - |
dc.subject | Normalization | - |
dc.subject | Pain prediction | - |
dc.subject | Pain-evoked EEG | - |
dc.subject | Spontaneous EEG | - |
dc.title | Spontaneous EEG-based normalization of pain-evoked neural responses: effect on improving the accuracy of pain prediction | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Bai, Y: tsdwx56@hku.hk | - |
dc.identifier.email | Hu, Y: yhud@hku.hk | - |
dc.identifier.email | Zhang, Z: zhangzg@hku.hk | - |
dc.identifier.authority | Hu, Y=rp00432 | - |
dc.identifier.authority | Zhang, Z=rp01565 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/CIVEMSA.2016.7524316 | - |
dc.identifier.scopus | eid_2-s2.0-84984656672 | - |
dc.identifier.hkuros | 263975 | - |
dc.identifier.spage | 1 | - |
dc.identifier.epage | 4 | - |
dc.publisher.place | United States | - |
dc.customcontrol.immutable | sml 160923 | - |