Effect of game-based EEG neurofeedback training on improvement of cognitive function
Bibliographic record
Abstract
Objective: To observe the effect of game-based EEG neurofeedback system on improvement of cognitive function in the patients with cognitive impairment. Methods: Fifty-two patients with cognitive impairment, mainly memory decline, were included, and the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment Scale (MoCA) and Alzheimer′s Disease Assessment Scale-Cognitive section(ADAS-cog) were conducted in the patients to evaluate cognitive impairment. Five days later, each patient was given 30-min EEG neural feedback training, once a day for 10 consecutive days. The EEG was detected before and after training, and MMSE, MoCA and ADAS-cog scores were also evaluated after training. Results: The scores of MMSE, MoCA and ADAS-cog scales after training were all higher(26.06±2.95, 21.88±3.94, 12.15±5.15) than those before training (23.10±2.82, 18.63±4.10, 14.76±5.30) (P<0.05). Before training, the scores of memory on MMSE, MoCA and ADAS-cog scales were 1.55±0.77, 1.33±1.28, 4.35±1.11, respectively, while the above scores increased to 2.16±0.80, 2.29±1.34, 3.93±1.30(P<0.001) after training. The EEG after training showed that the complexity of EEG was improved than that before training, mainly in the left frontal lobe. Conclusions: The game-based EEG neurofeedback system training can significantly improve cognitive function and EEG complexity in the left prefrontal lobe.
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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".