Investigating the Role of Having an Avatar in Virtual Reality on Pain Alleviation and Embodiment in Patients With Pain Using Electroencephalogram: A Neuroimaging Protocol
Bibliographic record
Abstract
Chronic Pain (CP) is prevalent in industrialized countries and stands among the top 10 causes of disability. Given the widespread problems of pharmacological treatments such as opioids, a need to find alternative therapeutic approaches has emerged. Virtual Reality (VR) has shown potential as a non-pharmacological alternative for controlling pain over the past 20 years. The effectiveness of VR has been demonstrated in treating CP, and it has been suggested that VR’s analgesic effects may be associated with the Sense of Embodiment (SoE): the sensation of being inside, having and controlling a virtual body in VR. Studies have shown correlations among brain signals, reported pain and a SoE, and correlations have been observed between using an avatar in VR and pain alleviation among CP patients. However, little has been published about the changes in brain physiology associated with having an avatar in VR, and current published studies present methodological issues. Defining a proper methodology to investigate the underlying brain mechanisms of pain, a SoE associated with having an avatar in VR, and its effect on reducing pain in CP patients is key to the emerging field of VR-analgesia. Here, we propose an intervention trial design (test/intervention/test) to evaluate the effects of having a virtual avatar in VR on pain levels and SoE in CP patients using Electroencephalogram (EEG) recordings. Resting-state EEG recordings, perceived pain levels, and SoE scores will be collected before and after the VR intervention. Patients diagnosed with CP will be recruited from local pain clinics and pseudo-randomly assigned to one of two groups—with or without an avatar. Patients will experience a 10-min VR intervention built to treat CP while their EEG signals are recorded. In articulating the study procedure, we propose a framework for future studies that explores the mechanisms of VR-analgesia in patients with chronic pain.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".