Effects of Electroencephalographic Biofeedback Therapy on Depression Level, Sleep Quality and Cognitive Function in Patients With Non-Demented Vascular Cognitive Impairment
Bibliographic record
Abstract
OBJECTIVE: This study aimed to investigate the effects of electroencephalographic biofeedback (EEG-BF) treatment on cognitive function, sleep quality, anxiety and depression levels and quality of life in patients with vascular cognitive impairment-no dementia (VCI-ND). METHODS: This study was a retrospective study that included a total of 128 patients diagnosed with VCI-ND at the Affiliated Hospital of North Sichuan Medical College from July 2022 to July 2024. The patients were divided into an EEG-BF group and a control group in accordance with whether they received EEG-BF treatment or not. Both groups received standard vascular risk factor management. The EEG-BF group separately received EEG-BF intervention two times a week for 12 weeks. Propensity score matching (PSM) was used to perform 1:1 nearest-neighbour matching between the two groups with respect to baseline characteristics. The matching variables included age; education; place of residence; family income; type of health insurance; number of underlying diseases; and pre-intervention scores on the Montreal Cognitive Assessment (MoCA), Pittsburgh Sleep Quality Index (PSQI), Self-rating Anxiety Scale (SAS), and Self-rating Depression Scale (SDS). The main outcome measures were the PSQI, MoCA, SAS, 36-item Short-Form Questionnaire (SF-36) and SDS before and after treatment. RESULTS: After PSM, the baseline covariates between the two groups were well balanced, with no significant differences. The Love plot showed a significant decrease in standardised differences in covariates after matching. After 12 weeks of intervention, the EEG-BF group was significantly better than the control group in terms of MoCA scores (p = 0.013), SAS scores (p = 0.002), SDS scores (p = 0.004) and some of the SF-36 dimensions, and the within-group before and after comparisons was statistically different (p < 0.05). The sleep quality of the EEG-BF group improved after treatment, whereas that of the control group exhibited no notable variation before and after the intervention (p > 0.05). CONCLUSION: EEG-BF may help improve cognitive function, sleep quality, emotional state and life quality in patients with VCI-ND, offering a promising individualised non-pharmacological intervention for this population. Future multicentre, prospective studies are needed to further validate its prolonged therapeutic effect and neuromodulatory mechanisms.
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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) | 0.000 | 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".