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Article: Normalization of Pain-Evoked Neural Responses Using Spontaneous EEG Improves the Performance of EEG-Based Cross-Individual Pain Prediction

TitleNormalization of Pain-Evoked Neural Responses Using Spontaneous EEG Improves the Performance of EEG-Based Cross-Individual Pain Prediction
Authors
Issue Date2016
PublisherFrontiers Research Foundation. The Journal's web site is located at http://www.frontiersin.org/computational_neuroscience
Citation
Frontiers in Computational Neuroscience, 2016, v. 10, p. Article 31 How to Cite?
AbstractAn effective physiological pain assessment method that complements the gold standard of self-report is highly desired in pain clinical research and practice. Recent studies have shown that pain-evoked electroencephalography (EEG) responses could be used as a readout of perceived pain intensity. Existing EEG-based pain assessment is normally achieved by cross-individual prediction (i.e., to train a prediction model from a group of individuals and to apply the model on a new individual), so its performance is seriously hampered by the substantial inter-individual variability in pain-evoked EEG responses. In this study, to reduce the inter-individual variability in pain-evoked EEG and to improve the accuracy of cross-individual pain prediction, we examined the relationship between pain-evoked EEG, spontaneous EEG, and pain perception on a pain EEG dataset, where a large number of laser pulses (>100) with a wide energy range were delivered. 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. In addition, a nonlinear relationship between the level of pain perception and pain-evoked EEG responses was obtained, which inspired us to further develop a new two-stage pain prediction strategy, a binary classification of low-pain and high-pain trials followed by a continuous prediction for high-pain trials only, both of which used spontaneous-EEG-normalized magnitudes of evoked EEG responses as features. Results show that the proposed normalization strategy can effectively reduce the inter-individual variability in pain-evoked responses, and the two-stage pain prediction method can lead to a higher prediction accuracy.
Persistent Identifierhttp://hdl.handle.net/10722/234593
ISSN
2015 Impact Factor: 2.653
2015 SCImago Journal Rankings: 1.355

 

DC FieldValueLanguage
dc.contributor.authorBai, Y-
dc.contributor.authorHuang, G-
dc.contributor.authorTU, Y-
dc.contributor.authorTAN, A-
dc.contributor.authorHung, YS-
dc.contributor.authorZhang, Z-
dc.date.accessioned2016-10-14T13:47:54Z-
dc.date.available2016-10-14T13:47:54Z-
dc.date.issued2016-
dc.identifier.citationFrontiers in Computational Neuroscience, 2016, v. 10, p. Article 31-
dc.identifier.issn1662-5188-
dc.identifier.urihttp://hdl.handle.net/10722/234593-
dc.description.abstractAn effective physiological pain assessment method that complements the gold standard of self-report is highly desired in pain clinical research and practice. Recent studies have shown that pain-evoked electroencephalography (EEG) responses could be used as a readout of perceived pain intensity. Existing EEG-based pain assessment is normally achieved by cross-individual prediction (i.e., to train a prediction model from a group of individuals and to apply the model on a new individual), so its performance is seriously hampered by the substantial inter-individual variability in pain-evoked EEG responses. In this study, to reduce the inter-individual variability in pain-evoked EEG and to improve the accuracy of cross-individual pain prediction, we examined the relationship between pain-evoked EEG, spontaneous EEG, and pain perception on a pain EEG dataset, where a large number of laser pulses (>100) with a wide energy range were delivered. 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. In addition, a nonlinear relationship between the level of pain perception and pain-evoked EEG responses was obtained, which inspired us to further develop a new two-stage pain prediction strategy, a binary classification of low-pain and high-pain trials followed by a continuous prediction for high-pain trials only, both of which used spontaneous-EEG-normalized magnitudes of evoked EEG responses as features. Results show that the proposed normalization strategy can effectively reduce the inter-individual variability in pain-evoked responses, and the two-stage pain prediction method can lead to a higher prediction accuracy.-
dc.languageeng-
dc.publisherFrontiers Research Foundation. The Journal's web site is located at http://www.frontiersin.org/computational_neuroscience-
dc.relation.ispartofFrontiers in Computational Neuroscience-
dc.rightsThis Document is Protected by copyright and was first published by Frontiers. All rights reserved. It is reproduced with permission.-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.titleNormalization of Pain-Evoked Neural Responses Using Spontaneous EEG Improves the Performance of EEG-Based Cross-Individual Pain Prediction-
dc.typeArticle-
dc.identifier.emailBai, Y: tsdwx56@hku.hk-
dc.identifier.emailHuang, G: huanggan@hku.hk-
dc.identifier.emailHung, YS: yshung@eee.hku.hk-
dc.identifier.authorityHung, YS=rp00220-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.3389/fncom.2016.00031-
dc.identifier.hkuros270127-
dc.identifier.volume10-
dc.identifier.spageArticle 31-
dc.identifier.epageArticle 31-
dc.publisher.placeSwitzerland-

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