Effects on Mood and EEG States After Meditation in Augmented Reality With and Without Adjunctive Neurofeedback
Bibliographic record
Abstract
Research and design of virtual reality technologies with mental-health focused applications has increased dramatically in recent years. However, the applications and psychological outcomes of augmented reality (AR) technologies still remain to be widely explored and evaluated. This is particularly true for the use of AR for the self-management of stress, anxiety, and mood. In the current study, we examined the impact of a brief open heart meditation AR experience on participants with moderate levels of anxiety and/or depression. Using a randomized between-group design subjects participated in the AR experience or the AR experience plus frontal gamma asymmetry neurofeedback integrated into the experience. Self-reported mood state and resting-state EEG were recorded before and after the AR intervention for both groups. Participants also reported on engagement and perceived use of the experience as a stress and coping tool. EEG activity was analyzed as a function of the frontal, midline, and parietal scalp regions, and with sLORETA current source density estimates of anterior cingulate and insular cortical regions of interest. Results demonstrated that both versions of the AR meditation significantly reduced negative mood and increased positive mood. The changes in resting state EEG were also comparable between groups, with some trending differences observed, in line with existing research on open heart and other loving-kindness and compassion-based meditations. Engagement was favorable for both versions of the AR experience, with higher levels of engagement reported with the addition of neurofeedback. These results provide early support for the therapeutic potential of AR-integrated meditations as a tool for the self-regulation of mood and emotion, and sets the stage for more research and development into health and wellness-promoting AR applications.
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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) | 0.002 | 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".