Effects of EEG Biofeedback Therapy on Negative Symptoms, Social Functioning, and Cognitive Function in Patients with Schizophrenia
Bibliographic record
Abstract
Objective: To observe the effects of conventional psychiatric medication combined with EEG biofeedback therapy on negative symptoms, social functioning, and cognitive functioning in patients with schizophrenia. Methods: Patients with schizophrenia hospitalized at Xuancheng Fourth People’s Hospital between December 2023 and December 2024 were selected as study subjects. Assessments were conducted using a general demographic questionnaire, the Scale for the Assessment of Negative Symptoms (SANS), the Activities of Daily Living (ADL) scale, the Social Disability Screening Schedule (SDSS), and the Montreal Cognitive Assessment (MoCA) scale before the intervention, as well as at 4 weeks, 8 weeks, and 12 weeks after the intervention. Results: The MoCA scores of the two groups showed statistically significant differences in time effect, group effect, and time × group interaction effect (F=79.76, 6.43, 8.52, respectively; p<0.01). The SANS, ADL, and SDSS scores of both groups demonstrated statistically significant time effects (F=42.83, 13.79, 17.92, respectively; all p<0.01), but no statistically significant group effects (F=0.05, 2.28, 0.25, respectively; all p>0.05). However, the SANS, ADL, and SDSS scores showed statistically significant time × group interaction effects (F=25.86, 13.28, 14.90, respectively; all p<0.01). Further simple effect analysis revealed that at 8 and 12 weeks of intervention, the observation group had significantly lower SANS, ADL, and SDSS scores compared to the control group, with statistically significant differences (p<0.01). Conclusion: Conventional psychiatric medication combined with EEG biofeedback therapy can improve negative symptoms, daily living ability, social functioning, and cognitive functioning in patients with schizophrenia.
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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) | 0.001 | 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".