EEG Analysis of the Contribution of Music Therapy and Virtual Reality to the Improvement of Cognition in Alzheimer’s Disease
Bibliographic record
Abstract
Alzheimer’s disease is the most common form of dementia, affecting nearly 9.9 million new people every year. The disease provokes important memory and cognitive impairment, eventually causing individuals to forget their loved ones and rendering them completely dependent on their caretakers. Alzheimer’s patients typically experience more negative emotions, such as frustration and apathy, than healthy older adults. There is currently no cure for the disease. Our research group explores how the integration of virtual reality (VR) and an EEG-based intelligent agent in music therapy can alleviate psychological and cognitive symptoms of the disease. We propose a theory explaining how, through activation of the brain reward system, music can reduce negative emotions, increase positive emotions and as a result increase performance on cognitive tasks. The results of our experimental study concord with our theory: emotional states of participants are improved, as per recorded through EEG, and performances on memory tasks show improvement following the intervention. We believe that the combination of EEG brain assessment, VR and music therapy is a promising method for emotional states and cognitive symptoms of Alzheimer’s disease.
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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".