Predicting Pain: Electroencephalography Signatures of Neural Integration During Experimental Tonic Thermal Pain.
The 4 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
- [1] § Methods › Functional Connectivity and CFC ↔ plotAccuracyGroup.m, the whole file · a weak match · score 0.83 · PhaCon, PhaCou, PowCon, PowCou, power connectivity, phase connectivity
- [2] § Results › Prediction Accuracy ↔ plotAccuracyGroup.m, the whole file · a weak match · score 0.81 · PhaCon, PhaCou, PowCon, PowCou, prediction accuracy, power connectivity
- [3] § Methods › Machine Learning Approach › Classification Procedure ↔ binaryClassTestMix.m, lines 43–164 · score 0.57 · cross validation, feature selection, fitcsvm, fold, NCA, model
- [4] § Methods › Machine Learning Approach › Feature Selection Based on Neighbourhood Component Analysis (NCA) ↔ binaryClassTestMix.m, lines 43–164 · score 0.51 · feature selection, cross validation, NCA, weight, binary, classification
Paper
Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC
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The authors' code
MATLAB · 46 lines · 1.8 KB · MIT · 2 matches
- % Reorganize the data to fit the catplot function
- file = readtable("/Users/yiyuan/OneDrive - University of Essex/PG Research/0A_Journal/202004_unknown Journal_Probability Score/1_Results/0_Final Results/Results_tables/AccuracyData.xls");
- accuracy = table2array(file(:,1));
- length = table2array(file(:,2));
- feature = table2array(file(:,3));
- classification = table2array(file(:,4));
- classes = ['OvsC';'HvsW';'OvsH';'OvsW';'CvsH';'CvsW'];
- timeLengthLabel = {'1';'2.5';'5';'10'};
- timeLength = [1 2.5 5 10];
- xs=.4/4; %the best half width for a 'group or catagory' is .4 and there are 4 big states
- width=xs*.85;% width of each box with a small gap between subgroups
- sgxs=[-0.3 -0.1 0.1 0.3];% SubGroups X Shift (twice the half width)
- cc={[0.2 0.2 0.2],[0.4 0.4 0.4],[.6 .6 .6], [.8 .8 .8]};% subgroups color (blue orange red)
- XTickLabels = {'Phase Connectivity','Power Connectivity','Phase CFC','Power CFC'};
- XTick = {'PhaCon','PowCon','PhaCou','PowCou'};
- close all
- figure
- for classIndex = 1:6
- subplot(3,2,classIndex)
- class = classes(classIndex,:);
- title(class)
- classRefIndex = ismember(classification,class);
- featureClass = feature(classRefIndex);
- for ii=1:4
- lengthRefIndex = ismember(length,timeLength(ii));
- for jj=1:4
- featureRefIndex = ismember(feature,XTickLabels{jj});
- accuracyPlot = 100*accuracy(featureRefIndex&lengthRefIndex&classRefIndex);
- h1{ii}=whisker_boxplot(jj+sgxs(ii),accuracyPlot, cc{ii},'width',width);
- end
- end
- set(gca,'XTick',1:4,'XTickLabels',XTick)
- ylim([0 100])
- xlabel('Feature Type','FontWeight','bold')
- ylabel('Prediction Accuracy (%)','FontWeight','bold')
- end
- lgd = legend([h1{1}(1) h1{2}(1) h1{3}(1) h1{4}(1)],timeLengthLabel,'Orientation','horizontal');
- set(lgd,'Units','normalized')
- title(lgd,'Trial Length (s)')
- set( findall(gcf, '-property', 'fontsize'), 'fontsize', 12)
plotAccuracyGroup.m at commit 5036c1f, under MIT · at the source
Overview
- School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK
- Department of Neurological Surgery and UCSF Weill Institute for Neurosciences, University of California, San Francisco, California, USA
- Department of Psychology and Centre for Brain Science, University of Essex, Colchester, UK
Abstract
Background: The identification of reliable neural signatures for pain remains a critical challenge in both clinical and experimental settings. While electroencephalography (EEG) provides a promising avenue for pain assessment, it remains unclear whether phase‐ or power‐based neural integration drives pain‐state discrimination. This study investigates functional connectivity and cross‐frequency coupling (CFC) as candidate signatures of neural integration for pain prediction.
Methods: We recorded 62‐channel EEG data from 36 healthy participants across experimental conditions, including tonic thermal pain, non‐painful warm stimulation and resting states. Functional connectivity within the alpha band and CFC across delta, theta, alpha and low‐beta bands was computed using phase‐ and power‐based measures. Machine learning models were trained to classify pain from non‐painful conditions, with prediction accuracy serving as an index of neural integration performance.
Results: Phase‐based features outperformed power‐based features in tonic thermal pain prediction, which indicated a dominant role for phase synchrony. The strongest topographical signatures are the connectivity involving frontal and occipital regions driven by enhanced alpha‐phase connectivity or theta‐alpha/
Conclusions: Our findings demonstrate that phase‐based neural integration outperforms power‐based integration in characterising tonic pain, and that the inclusion of amplitude information actively reduces discriminability. Phase‐based measures of functional connectivity in the alpha band and cross‐frequency coupling with other low‐frequency oscillations may serve as candidate EEG signatures of pain, offering a data‐driven framework with potential applications in research and clinical contexts.
Significance Statement: This study introduces an EEG‐based framework for tonic pain prediction that integrates multiple neural signatures of integration. High classification accuracy between painful and non‐painful states provides new machine learning–driven insight into phase‐based neural integration, reflected in functional connectivity and CFC. The findings broaden theoretical perspectives on pain processing within machine learning approaches and underscore clinically relevant potential for developing reliable, noninvasive tools to improve pain assessment.
Reproduced under the paper's license (CC BY), from the paper cited above.
Repository
Its files are read in the Code ↔ Paper reader above, with 4 matches between paragraphs and lines of code.
han-yy/NeuralMarkerPain
5036c1fbb5c940780e1e1d6f796724c91894e502, 23 November 2023Availability: 1 check, the latest on 27 September 2026: the link answers
- 27 September 2026: the link answers
48 files
- EEGfilter.m, MATLAB, 49 lines
- FeatureExtra.m, MATLAB, 40 lines
- FinalTestMix.m, MATLAB, 90 lines
- Main.m, MATLAB, 36 lines
- MultiClassControl.m, MATLAB, 117 lines
- PlotAccuracy.m, MATLAB, 32 lines
- PlotPhaConAccuracy.m, MATLAB, 48 lines
- PlotTime.m, MATLAB, 30 lines
- binaryClass.m, MATLAB, 113 lines
- binaryClassCFC.m, MATLAB, 95 lines
- binaryClassTest.m, MATLAB, 145 lines
- binaryClassTestMix.m, MATLAB, 164 lines, 2 matches
- getBinaryClassifiers.m, MATLAB, 11 lines
- getBinaryClassifiersCFC.
m , MATLAB, 16 lines - getBinaryClassifiersTest
.m , MATLAB, 11 lines - getBinaryClassifiersTest
Mix.m , MATLAB, 13 lines - getBinaryClassifiersTest
Time.m , MATLAB, 11 lines - getCOHCFC.m, MATLAB, 66 lines
- getCOHCFCchannelTime.m, MATLAB, 44 lines
- getCOHchannelTime.m, MATLAB, 38 lines
- getConnMap.m, MATLAB, 54 lines
- getEEGmap.m, MATLAB, 59 lines
- getEEGmapCFC.m, MATLAB, 39 lines
- getFeatureLabel.m, MATLAB, 202 lines
- getISPCCFC.m, MATLAB, 63 lines
- getISPCCFCchannelTime.m, MATLAB, 34 lines
- getISPCchannelTime.m, MATLAB, 39 lines
- getNumWeights.m, MATLAB, 44 lines
- innerCOHBinclass.m, MATLAB, 46 lines
- innerISPCBinclass.m, MATLAB, 65 lines
- interCFCBinclass.m, MATLAB, 42 lines
- interCOHBinclass.m, MATLAB, 66 lines
- interISPCBinclass.m, MATLAB, 79 lines
- multiClassCFC.m, MATLAB, 94 lines
- multiClassPar.m, MATLAB, 117 lines
- plotAccuracyGroup.m, MATLAB, 46 lines, 2 matches
- plotConnMap.m, MATLAB, 35 lines
- plotFeatureRatio.m, MATLAB, 64 lines
- plotFinalConnMap.m, MATLAB, 87 lines
- plotNodes.m, MATLAB, 16 lines
- plotPhaConnCurve.m, MATLAB, 48 lines
- plotPhaseConnMap.m, MATLAB, 125 lines
- plotRunTime.m, MATLAB, 48 lines
- plotTimeOnly.m, MATLAB, 44 lines
- plotTopo.m, MATLAB, 92 lines
- swapTest.m, MATLAB, 31 lines
- LICENSE, License, 21 lines
- README.md, Text, 2 lines
The paper's code and data availability statement is in the Data section.
Tracing map
Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.
What the map holds:
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Data
Datasets cited
Data Availability Statement
All analysis code (preprocessing, feature extraction, NCA feature selection, SVM classification and visualisation) is publicly available at https://
Reproduced under the paper's license (CC BY), from the paper cited above.
Versions
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Version 2, 28 September 2026
- Publisher: n/a → Wiley
Version 1, 27 September 2026: the first record
Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 12 MeSH terms, 50 references.
Cite
This paper
Han, Y., Valentini, E., & Halder, S. (2026). Predicting Pain: Electroencephalography Signatures of Neural Integration During Experimental Tonic Thermal Pain. European journal of pain (London, England), 30(6), e70313. https://
BibTeX
@article{han2026predicti
author = {Han, Yiyuan and Valentini, Elia and Halder, Sebastian},
title = {{Predicting Pain: Electroencephalography Signatures of Neural Integration During Experimental Tonic Thermal Pain}},
journal = {European journal of pain (London, England)},
year = {2026},
month = jul,
volume = {30},
number = {6},
pages = {e70313},
publisher = {Wiley},
issn = {1090-3801},
doi = {10.1002/
url = {https://
pmid = {42377977},
pmcid = {PMC13317760}
}
RIS
TY - JOUR
AU - Han, Yiyuan
AU - Valentini, Elia
AU - Halder, Sebastian
TI - Predicting Pain: Electroencephalography Signatures of Neural Integration During Experimental Tonic Thermal Pain
T2 - European journal of pain (London, England)
J2 - Eur J Pain
PY - 2026
DA - 2026/
VL - 30
IS - 6
SP - e70313
SN - 1090-3801
PB - Wiley
DO - 10.1002/
UR - https://
LA - en
ER -
CSL-JSON
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"container-title": "European journal of pain (London, England)",
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"given": "Yiyuan"
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"given": "Sebastian"
}
],
"container-title-short":
"volume": "30",
"issue": "6",
"page": "e70313",
"DOI": "10.1002/
"PMID": "42377977",
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"ISSN": "1090-3801",
"publisher": "Wiley",
"URL": "https://
"language": "en",
"issued": {
"date-parts": [
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2026,
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]
}
}
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