A study on the combined effect of Trataka and Kapalbhati on EEG waves
Bibliographic record
Abstract
Shatkarma, a set of six cleansing yogic techniques stated in Hatha Yoga is known to have positive effects on metabolism, sympathetic nervous system, digestive disorder, relaxation etc. Trataka and Kapalbhati are two distinct yogic practices in shatkriya, Kapalbhati is an active breathing technique that can energize the body, while trataka involves steady concentration and may be more calming. But combining Trataka and Kapalbhati can offer a holistic approach to cleansing, energizing, and focusing the body and mind. The present study looks to examine the combined effect of kapalbhati and trataka on the human central nervous system using EEG (Electroencephalography) brain waves using robust nonlinear scientific analysis techniques. The study is based on a primary working hypothesis: Combined practice of the two yogic techniques- trataka and kapalbhati, can enhance the concentration, attention span in comparison to individual practice. For this, 3 participants (Male, Age= 21-25 Years, SD= 2.5 years) were chosen, who underwent a protocol of approx 20 mins of Trataka and Kapalbhati, during which EEG response was monitored continuously. EEG or Electroencephalography is a brain imaging technique which measures the neuro-electrical responses originating from different lobes of the human brain during the process of yogic practice of Trataka and Kapalbhati. The concentration levels of the participants were monitored from the recorded EEG signals in an experimental block of 2 mins each using a nonlinear analysis technique called Multifractal Detrended Fluctuation Analysis (MFDFA). The scaling exponent generated from the MFDFA technique is used as a parameter with which the benefits of the combined yogic practice can be monitored for each participant in a scientific manner. The present study is a pilot one which tries to quantitatively assess the beneficial effects of trataka and kapalbhati using state of the art robust neuro-scientific methods. The increase in multifractal width during the combined practice is a signature of the increased concentration and attention based activities in human brain.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".