An Empirical Study of Indian Classical Ragas Desh and Todi Structure and its Influence on Brain Waves
Bibliographic record
Abstract
Ancient Indian classical musical maestros affirmed that ragas influence emotions of a human being by changing the resonance of human body. Some ragas like darbari Canada, khaman and poorly are found to help in defusing mental tension, particularly in the case of hysterics. Raga malware Pacifies anger, excessive mental, excitement & mental instability, Raga Jai Jai anti have also been pound effective in curing mental disorders and calming the mind. Although it is required to verify this raga correlation systematically. By survey, it has been seen that no schemes have demonstrated yet. The proposed research presented in this paper is aimed to discover the science behind phonetics of raga and its effects on nerve system. This research is one step to explore scientifically the ancient way of alternative medicine i.e. raga therapy, which is a need of the day since current advances in technology and rising workload on a human being is accompanied by stress. This research focuses on to study the influence of Indian classical ragas structure on the human body while the person is listening and experiencing an emotion in it by capturing EEG signals. The brainwave signals database will be collected and analyze. This research work addresses these objectives and aims to present a strong case which will help medical practitioners like a psychiatrist, to treat the patient by injecting music stimulus.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".