Does musical training affect neuro-cognition of emotions? An EEG study with Indian Classical Instrumental Music
Bibliographic record
Abstract
Music evokes a variety of emotions, irrespective of its genre, timbre, and tempo. Indian classical music (ICM), the age-old tradition of the sub-continent is no exception and instrumental music forms one broad section of ICM. In the present study, we have tried to compare the neural responses of music practitioners and non-musicians using audio clips of one of the most popular string instruments of ICM, Sarod. From an extensive audience response survey, a total of 8 Sarod clips having maximum arousal for happy and sad emotions were identified. The neuronal activities corresponding to these two emotional classes were assessed with EEG (Electroencephalography) experiments performed on 10 participants belonging to two categories – musicians and non-musicians. A robust nonlinear multifractal technique, MFDFA along with other associated features was applied to quantitatively measure the changes in different brain lobes for both categories of subjects. Detailed analysis showed, brain responses of musicians were characterized by higher complexity and coherence than non-musicians. Non-musicians showed dominance of smaller fluctuation ranges in their neural response signals. In essence, this study attempts to encapsulate and compare how prior musical training influences the brain responses of two basic musical emotions using musical clips of the Indian instrument, Sarod.
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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) | 0.001 | 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".