Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/80433
Title: Normalization of pain-evoked neural reponses using spontaneous EEG improves the performance of EEG-based cross-individual pain prediction
Authors: Hung, Yeung Sam
Zhang, Zhiguo
Huang, Gan
Tu, Yiheng
Tan, Ao
Bai, Yanru
Keywords: Pain-evoked EEG
Spontaneous EEG
DRNTU::Engineering::Chemical engineering
Issue Date: 2016
Source: Bai, Y., Huang, G., Tu, Y., Tan, A., Hung, Y. S., & Zhang, Z. (2016). Normalization of Pain-Evoked Neural Reponses Using Spontaneous EEG Improves the Performance of EEG-Based Cross-Individual Pain Prediction. Frontiers in Computational Neuroscience, 10, 31-. doi:10.3389/fncom.2016.00031
Series/Report no.: Frontiers in Computational Neuroscience
Abstract: An 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.
URI: https://hdl.handle.net/10356/80433
http://hdl.handle.net/10220/46549
DOI: 10.3389/fncom.2016.00031
Rights: © 2016 Bai, Huang, Tu, Tan, Hung and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCBE Journal Articles

SCOPUSTM   
Citations 20

4
checked on Sep 5, 2020

WEB OF SCIENCETM
Citations 20

4
checked on Oct 16, 2020

Page view(s) 20

75
checked on Oct 19, 2020

Download(s) 20

26
checked on Oct 19, 2020

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.