Instrumenting a Virtual Reality Headset to Monitor Changes in Electroencephalograms of PTSD Patients During Multisensory Immersion
Bibliographic record
Abstract
Virtual reality (VR) has emerged as a promising tool to help treat posttraumatic stress disorder (PTSD) symptoms, as well as help patients manage their anxiety. More recently, multisensory immersive experiences involving audio-visual-olfactory stimuli have been shown to lead to improved relaxation states. Despite these advances, very little is still known about the psychophysiological changes resulting from these interventions, and outcomes need to be monitored via questionnaires and interviews at the end of the intervention. In this article, we propose to instrument a VR headset with several biosensors to allow for the tracking of neural changes throughout the intervention, as well as track the progress of different neuromarkers, namely powers across the five conventional electroencephalogram (EEG) frequency subbands computed at the frontal, central, parietal, and occipital areas of the brain. In total, 20 participants diagnosed with PTSD by their medical doctors took part in the experiment and underwent a 12-session multisensory nature immersion protocol. We show the changes that were observed for those who benefited and those who did not benefit from the intervention, leading to insights on potential new markers of intervention outcomes that could save patients and medical professionals time and resources. The proposed headset also allowed for changes in arousal states and EEG patterns to be tracked, thus providing additional insights on the disorder, as well as the effects of the intervention on patient symptoms.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".