Effects of Gandharva Veda Music on Mood States, Health, and Brain Functioning
Bibliographic record
Abstract
Background and Objectives: This paper explores effects of Gandharva Veda music--traditional North Indian music-- on emotional and physical well-being. Hypothesis: Gandharva Veda music balances the mind and body. Materials and Methods: A professional Gandharvan performed 16 live and two online concerts. Mood states were assessed with the Profile of Mood states (POMS) at the live concerts, emotional and physiological balance were assessed during the online concerts using a traditional Ayurveda diagnostic technique called “pulse diagnosis,” and the EEG of a single Gandharvan was recorded while she played a raga. Results: In the first study (n=1,800), Gandharva Veda music led to significant decreases of negative emotions and significant increases of positive emotions. In the second study, 78 Ayurveda students reported significantly higher levels of vibrant health and significant lowers level of physiological blockages after the ragas. In the last study, high theta1 and alpha1 coherence patterns were seen both during meditation practice and when the Gandharvan played the ragas, along with higher 12 – 50 Hz coherence during the ragas. Discussion: The responses on the POMS and pulse reading support the prediction that Gandharva Veda music creates a healthy influence for the listener. Also, the coexistence of higher alpha1 coherence, a marker of inner silence, and higher 12-50 Hz coherence, a marker of goal-oriented performance suggests that playing Gandharva Veda music may culture inner silence as the performer plays complex musical passages. Conclusion: Future research is warranted to explore the application of Gandharva Veda music across a wider population of healthy and clinical patients.
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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 | 0.000 |
| 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) | 0.004 | 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".