Improvement in peripherical visual attentional performance in professional soccer players following a single neurofeedback training
Bibliographic record
Abstract
Abstract The effectiveness of EEG-neurofeedback (EEG-NFB) in modulating cognition has been the subject of much research for several years, particularly in relation to attentional functions in healthy subjects and those with attentional deficits. However, its effectiveness on sports performance remains poorly studied and its use is not widely practised among athletes, notably because of its accessibility and questionable effectiveness. The aim of this study is to show that this technology can be accessible, and that Alpha EEG-NFB is immediately effective. Fifteen professional soccer players took part in this study. Using a novel EEG headset that can be installed in less than one minute, and new processing software, the players performed two peripherical attentional tasks before and after, immediately and one month, a single Alpha EEG-NFB training session. The results showed a significant effect on both tasks immediately after EEG-NFB training, with a benefit of more than 30% and this performance continued after one month (20%). This study, the first to use this headset and software, shows that the improvement in sports performance can be related to cognitive performance, especially peripherical visual attentional functions. Furthermore, it demonstrates that the use of the EEG-NFB is accessible and effective for high-level athletes.
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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) | 0.002 | 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".