A Quantitative Electroencephalographic Study of Meditation and Binaural Beat Entrainment
Bibliographic record
Abstract
OBJECTIVES: The study objective was to determine the quantitative electroencephalographic correlates of meditation, as well as the effects of hindering (15 Hz) and facilitative (7 Hz) binaural beats on the meditative process. DESIGN: The study was a mixed design, with experience of the subject as the primary between-subject measure and power of the six classic frequency bands (δ, θ, low α, high α, β, γ), neocortical lobe (frontal, temporal, parietal, occipital), hemisphere (left, right), and condition (meditation only, meditation with 7-Hz beats, meditation with 15-Hz beats) as the within-subject measures. LOCATION: The study was conducted at Laurentian University in Sudbury, Ontario, Canada. SUBJECTS: The subjects comprised novice (mean of 8 months experience) and experienced (mean of 18 years experience) meditators recruited from local meditation groups. INTERVENTION: Experimental manipulation included application of hindering and facilitative binaural beats to the meditative process. RESULTS: Experienced meditators displayed increased left temporal lobe δ power when the facilitative binaural beats were applied, whereas the effect was not observed for the novice subjects in this condition. When the hindering binaural beats were introduced, the novice subjects consistently displayed more γ power than the experienced subjects over the course of their meditation, relative to baseline. CONCLUSIONS: Based on the results of this study, novice meditators were not able to maintain certain levels of θ power in the occipital regions when hindering binaural beats were presented, whereas when the facilitative binaural beats were presented, the experienced meditators displayed increased θ power in the left temporal lobe. These results suggest that the experienced meditators have developed techniques over the course of their meditation practice to counter hindering environmental stimuli, whereas the novice meditators have not yet developed those techniques.
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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".