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

TitleNormalization of Pain-Evoked Neural Reponses Using Spontaneous EEG Improves the Performance of EEG-Based Cross-Individual Pain Prediction
Authors
Issue Date2016
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

 

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.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.relation.ispartofFrontiers in Computational Neuroscience-
dc.titleNormalization of Pain-Evoked Neural Reponses 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.identifier.doi10.3389/fncom.2016.00031-
dc.identifier.hkuros270127-
dc.identifier.volume10-
dc.identifier.spageArticle 31-
dc.identifier.epageArticle 31-

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