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Record W4372080683 · doi:10.1016/j.brs.2023.03.035

Evaluation of EEG as a reliable measure of pain: an experimental pain study

2023· article· en· W4372080683 on OpenAlexaboutno aff
Charlotte Ide-Walters, Trevor Thompson

Bibliographic record

VenueBrain stimulation · 2023
Typearticle
Languageen
FieldNeuroscience
TopicEEG and Brain-Computer Interfaces
Canadian institutionsnot available
Fundersnot available
KeywordsElectroencephalographyNeurofeedbackPhysical therapyMcGill Pain QuestionnairePsychologyQuantitative electroencephalographyPain and pleasureAudiologyPhysical medicine and rehabilitationAnesthesiaMedicineVisual analogue scalePsychiatryNeurosciencePleasure

Abstract

fetched live from OpenAlex

Abstract Research is promising regarding the potential of Electroencephalography (EEG) for pain assessment, and this could have a positive impact for people unable to communciate their pain (pre-verbal children/people with cognitive decline). However, limitations regarding reliability and controls, limit conclusions. The aims of this research were to examine the potential of EEG to assess pain, by studying the reliability to measure pain across two acute pain experiences (somatic and ischemic) controlling for individual variability between the two pain experiences. Cold/somatic (cold pressor) and exercise/ischemic (submaximum effort tourniquet technique/SETT) methods of pain induction were applied in healthy participants (n=30), while EEG was continuously recorded (64-channel). Pain ratings were recorded (numerical rating scale/NRS/0-10; McGill Pain Questionnaire/SF-MPQ/0-45). MATLAB/Brainstorm, was used to apply source localization techniques to study differences between baseline and two pain types, for Delta (2-4Hz), Theta (5-7Hz), Alpha (8-12Hz), and Beta (13-29Hz). Average frequency power for each frequency was computed and the relationship between EEG frequencies and pain ratings were assessed. T-tests (FDR correction in Brainstorm/MATLAB) revealed significant differences between both pain conditions compared to the baseline for all four frequencies in primarily the central, parietal, and frontal regions. Correlations of subjective pain ratings (MPQ) with frequencies found significant correlations with Alpha power (r = .53, p = .001) for cold/somatic pain only. No other reliable associations were observed.View Large Image Figure ViewerDownload Hi-res image Download (PPT) Overall, results suggest promise for the utility of EEG to measure pain, specifically Alpha activity, with an effect size of .53, indicating a good relationship between Alpha activity and pain. Some inconsistency in the results found means the reliability of EEG to measure different pain experienced is not supported, and definitive conclusions cannot be made at this stage. This has implications for researchers and clinicians in pain, and neurological based interventions for pain (e.g., neurofeedback). Research Category and Technology and Methods Basic Research: 15. Electroencephalography (EEG) Keywords: Electroencephalography (EEG), Ischemic pain, Somatic pain, Experimental pain

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.634
Threshold uncertainty score0.434

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.115
GPT teacher head0.377
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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