Virtual Reality and EEG-Based Intelligent Agent in Older Adults With Subjective Cognitive Decline: A Feasibility Study for Effects on Emotion and Cognition
Bibliographic record
Abstract
Objectives: Immersive virtual reality has tremendous potential to improve cognition in populations with cognitive impairment. We conducted a feasibility and proof-of-concept study to assess the potential of virtual reality and electroencephalography, with or without an intelligent agent, that adapts the presented material to the emotions elicited by the environment. Method: Older adults with subjective cognitive decline recruited from the community received a virtual reality-based intervention taking place in one of two virtual environments, a train (Part 1, N = 19) or a music theatre, complemented by the intelligent agent (Part 2, N = 19). A comparative control group (N = 19) receiving no intervention was also included. All participants completed measures of affect and cognition before and after the intervention. The intervention groups completed measures of cybersickness and user experience after the intervention. Results: Participants did not suffer from increased cybersickness following either intervention. They also reported a positive to highly positive user experience concerning the following aspects: attractivity, hedonic quality-identity and hedonic quality-stimulation. The measures of affect showed no pre-post change when comparing either intervention to the control condition. However, a reduction of negative affect was observed following the train intervention for participants with a high self-reported negative affect at baseline. Finally, there was a significant improvement in working memory when comparing either intervention group to the control condition. Conclusion: Our results support the feasibility and tolerability of the technology, and a positive impact on cognition, paving the way for a larger-scale randomized clinical trial to confirm efficacy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".