The Effect of an Integrated Neurofeedback and Biofeedback Training Intervention on Ice Hockey Shooting Performance
Bibliographic record
Abstract
The purpose of this study was to investigate the effectiveness of a sensorimotor rhythm (SMR) neurofeedback training (NFT) and biofeedback training (BFT) intervention on ice hockey shooting performance. Specifically, the purpose was to examine (a) whether an NFT/BFT program could improve ice hockey shooting performance, (b) whether the implementation of an SMR-NFT intervention leads to neurological adaptations during performance, and (c) whether such neurological changes account for improvement in shooting performance. Using a longitudinal stratified random control design, results demonstrated that while both SMR-NFT/BFT and control groups improved performance, the rate of improvement for the SMR-NFT/BFT group was significantly higher than the control. Participants in the SMR-NFT/BFT group demonstrated the ability to significantly increase SMR power from pre- to postintervention in the lab. However, no significant changes in SMR power were found during shooting performance. This result may be suggestive of differing cortical activity present during motor-skill preparation.
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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) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".