The Effects of Heart Rate Variability Training on Sensorimotor Rhythm: A Pilot Study
Bibliographic record
Abstract
Heart rate variability (HRV) training and EEG Biofeedback are techniques used to improve neurological disorders in both clinical and optimal performance populations.HRV training uses combined respiration and heart rate biofeedback to achieve synchrony between the changes in breathing and heart rate.This specific signature of synchronization of breathing and heart rate changes appears to correlate with a relaxed state and cognitive clarity.HRV may provide a promising index for both physical and emotional stress.Improvements in mental processing (Thayer, Hansen, Saus-Rose, & Johnson, 2009) and emotional stability (Applehans & Lueken, 2006) have been demonstrated as a result of HRV training.A similar mental state is the target of EEG biofeedback training when parameters are set to increase sensorimotor rhythm (SMR).SMR is usually trained using the frequency band 12-15 Hz.These frequencies are called SMR only when they are produced across the sensorimotor strip (C3, Cz, C4).In other locations, 12-15 Hz is simply called beta.SMR production has been closely linked to a state of calm, relaxed focus (Sterman, 1996).This article proposes that HRV training may be associated with increased levels of SMR.Preliminary data have been collected for 40 clients.Twenty clients were athletes training to improve performance, and 20 clients were from a clinical population aiming to increase SMR as a part of their program.A 3-min sample of EEG baseline data was compared to a 3-min sample of EEG data collected during HRV training.Mean microvolt values were collected for SMR during both the baseline recording and during the HRV training.T-test results show that there was a statistically significant increase in SMR during HRV training as compared to baseline (p < .001).This suggests that increased HRV leads to increases in production of SMR.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".