Electroencephalography Neuromarkers to Predict the Response of a Multisensory Virtual Reality Nature Immersion Intervention for Patients Diagnosed with Post-Traumatic Stress Disorder
Bibliographic record
Abstract
Immersive virtual reality (VR) applications rapidly expand across domains, including training, gaming, and healthcare. More recently, multisensory immersive experiences, including olfactory and haptic stimulation, have emerged and shown great promise, especially for interventions in well-being and mental health management. Multisensory experiences, however, are very subjective (e.g., one subject may like certain smells, while others do not), and recent results have suggested that some participants may not respond positively to the treatment. As multisensory VR interventions can be costly and time-consuming for both patients and clinicians, being able to find neuromarkers that predict intervention outcomes would be invaluable. Here, we aim to take the first steps in the development of a neuromarker to predict the response to a multisensory nature immersion VR intervention. A pilot experiment was performed with twenty patients diagnosed with post-traumatic stress disorder. Potential neuromarkers are extracted from electroencephalography (EEG) signals measured from an instrumented VR headset. We show that some EEG patterns start to differ between responders and non-responders as early as the fourth session, i.e., one-third of the way into the entire intervention. This suggests that neuromarkers to predict the outcomes of a multisensory VR immersion intervention may exist. These markers could be used not only to save time and resources for clinicians and patients but also to promote precision treatment where interventions are adjusted to each patient, maximizing success rates.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".