Meta-Review on Brain-Computer Interface (BCI) in the Metaverse
Bibliographic record
Abstract
This article presents a comprehensive meta-review of the intersection between Brain-Computer Interface (BCI) technologies and the Metaverse, emphasizing the enhancement of immersive experiences through VR, AR, MR, XR, Digital Twin, and haptic interfaces. The study classifies BCI devices into wearable and non-wearable categories, with a focus on their applications in robotics. It explores BCI user feedback mechanisms and their impact on medical and non-medical settings, including personalized rehabilitation and immersive gaming. The review introduces two frameworks for leveraging the Metaverse to navigate multisensory integration between BCI and assistive devices. Applications such as VR therapies for stroke patients and neuro-responsive multiplayer gaming environments showcase the potential of BCIs to enhance Metaverse interactions. To the best of our knowledge, this is the first meta-review on the integration of BCI and the Metaverse, identifying key challenges and research gaps, and serves as a foundational reference for future research and development in this interdisciplinary field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".