Frequency-Dependent Modulation of tPBM on Mindfulness-Induced Brain Oscillations: A Randomized, Double-Blind, Sham-Controlled Crossover Study
Bibliographic record
Abstract
were randomly assigned to receive active tPBM at one of four pulsation frequencies (4 Hz, 10 Hz, 40 Hz, or 120 Hz).The study also included a sham condition (LEDs off) and a control condition (no device).A one-week interval separated each session.Following each session, participants completed the Mystical Experience Questionnaire (MEQ-30) to assess their subjective meditation experiences.Results: Thirteen participants completed all six sessions without adverse effects.Analysis using the general linear model revealed a significant impact of tPBM on MEQ-30 scores ( F(5,60) ¼ 4.03, p ¼ 0.0032).Post-hoc tests revealed that tPBM at 40 Hz (t¼3.05,p ¼ 0.006) and 120 Hz (t¼4.28,p ¼ 0.0004) significantly enhanced mystical experiences compared to the sham condition.While the sham condition resulted in higher scores (83.1±25.6)than the control condition (80.4±30.2), the result was not statistically different.Conclusions: This is the first randomized, sham-controlled, double-blind study to investigate the impact of PBM on meditation quality.The results showed that tPBM, particularly pulsing at higher frequencies, significantly enhances the mindfulness meditation experience.These results highlight tPBM's potential as an adjunct therapy to amplify the benefits of meditation for mental health and well-being.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".