Effects of neurofeedback therapy in healthy young subjects
Bibliographic record
Abstract
PURPOSE: Neurofeedback refers to a form of operant conditioning of electrical brain activity, in which desirable brain activity is rewarded and undesirable brain activity is inhibited. The research team aimed to examine the efficacy of neurofeedback therapy on electroencephalogram (EEG) for heart rate, electrocardiogram (ECG) and galvanic skin resistance (GSR) parameters in a healthy young male population. METHODS: Forty healthy young male subjects aged between 18 to 30 years participated in this study. Neurofeedback application of one session was made with bipolar electrodes placed on T3 and T4 (temporal 3 and 4) regions and with reference electrode placed on PF1 (prefrontal 1). Electroencephalogram (EEG), electrocardiogram (ECG) and galvanic skin resistance (GSR) were assessed during Othmer neurofeedback application of one session to regulate slow wave activity for forty minutes thorough the session. Data assessed before neurofeedback application for 5 minutes and during neurofeedback application of 30 minutes and after neurofeedback application for 5 minutes throughout the session of 40 minutes. Means for each 5 minutes, that is to say, a total 8 data points for each subjects over 40 minutes, were assessed. RESULTS: Galvanic skin resistance increased and heart rate decreased after neurofeedback therapy. Beta activity in EEG increased and alfa activity decreased after neurofeedback therapy. CONCLUSIONS: These results suggest that neurofeedback can be used to restore sympathovagal imbalances. Also, it may be accepted as a preventive therapy for psychological and neurological problems.
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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".