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Conference Paper: PCA-SIR: a new nonlinear supervised dimension reduction method with application to pain prediction from EEG

TitlePCA-SIR: a new nonlinear supervised dimension reduction method with application to pain prediction from EEG
Authors
Issue Date2015
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001963
Citation
The 7th International IEEE/EMBS Conference on Neural Engineering (NER 2015), Montpellier, France, 22-24 April 2015. In Conference Proceedings, 2015, p. 1004-1007 How to Cite?
AbstractDimension reduction is critical in identifying a small set of discriminative features that are predictive of behavior or cognition from high-dimensional neuroimaging data, such as EEG and fMRI. In the present study, we proposed a novel nonlinear supervised dimension reduction technique, named PCA-SIR (Principal Component Analysis and Sliced Inverse Regression), for analyzing high-dimensional EEG time-course data. Compared with conventional dimension reduction methods used for EEG, such as PCA and partial least-squares (PLS), the PCA-SIR method can make use of nonlinear relationship between class labels (i.e., behavioral or cognitive parameters) and predictors (i.e., EEG samples) to achieve the effective dimension reduction (e.d.r.) directions. We applied the new PCA-SIR method to predict the subjective pain perception (at a level ranging from 0 to 10) from single-trial laser-evoked EEG time courses. Experimental results on 96 subjects showed that reduced features by PCA-SIR can lead to significantly higher prediction accuracy than those by PCA and PLS. Therefore, PCA-SIR could be a promising supervised dimension reduction technique for multivariate pattern analysis of high-dimensional neuroimaging data. © 2015 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/214828
ISBN

 

DC FieldValueLanguage
dc.contributor.authorTu, Y-
dc.contributor.authorHung, YS-
dc.contributor.authorHu, L-
dc.contributor.authorZhang, Z-
dc.date.accessioned2015-08-21T11:57:49Z-
dc.date.available2015-08-21T11:57:49Z-
dc.date.issued2015-
dc.identifier.citationThe 7th International IEEE/EMBS Conference on Neural Engineering (NER 2015), Montpellier, France, 22-24 April 2015. In Conference Proceedings, 2015, p. 1004-1007-
dc.identifier.isbn978-1-4673-6389-1-
dc.identifier.urihttp://hdl.handle.net/10722/214828-
dc.description.abstractDimension reduction is critical in identifying a small set of discriminative features that are predictive of behavior or cognition from high-dimensional neuroimaging data, such as EEG and fMRI. In the present study, we proposed a novel nonlinear supervised dimension reduction technique, named PCA-SIR (Principal Component Analysis and Sliced Inverse Regression), for analyzing high-dimensional EEG time-course data. Compared with conventional dimension reduction methods used for EEG, such as PCA and partial least-squares (PLS), the PCA-SIR method can make use of nonlinear relationship between class labels (i.e., behavioral or cognitive parameters) and predictors (i.e., EEG samples) to achieve the effective dimension reduction (e.d.r.) directions. We applied the new PCA-SIR method to predict the subjective pain perception (at a level ranging from 0 to 10) from single-trial laser-evoked EEG time courses. Experimental results on 96 subjects showed that reduced features by PCA-SIR can lead to significantly higher prediction accuracy than those by PCA and PLS. Therefore, PCA-SIR could be a promising supervised dimension reduction technique for multivariate pattern analysis of high-dimensional neuroimaging data. © 2015 IEEE.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001963-
dc.relation.ispartofInternational IEEE/EMBS Conference on Neural Engineering (CNE)-
dc.rightsInternational IEEE/EMBS Conference on Neural Engineering (CNE). Copyright © IEEE.-
dc.rights©2015 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.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titlePCA-SIR: a new nonlinear supervised dimension reduction method with application to pain prediction from EEG-
dc.typeConference_Paper-
dc.identifier.emailHung, YS: yshung@hkucc.hku.hk-
dc.identifier.emailZhang, Z: zhangzg@hku.hk-
dc.identifier.authorityHung, YS=rp00220-
dc.identifier.authorityZhang, Z=rp01565-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/NER.2015.7146796-
dc.identifier.scopuseid_2-s2.0-84940379088-
dc.identifier.hkuros249760-
dc.identifier.spage1004-
dc.identifier.epage1007-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 150929-

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