Finding Biomarkers of Virtual Reality-Induced Avatar Embodiment Priming in EEG Signals Recorded During a Motor Imagery Brain-Computer Interface Training
Bibliographic record
Abstract
Motor imagery (MI) frameworks have a long history of being used in different motor training applications. Since the emergence of brain-computer interfaces (BCI), MI techniques have been integrated into BCI frameworks to enable controlling external devices through interpreting neural signals into executable commands. Virtual reality (VR) has the capacity to introduce novel ways of improving the performance of MI-BCI trainings through enabling embodiment prior to and during the tasks. While the performance improvements achieved by using VR-based embodiment during the MI training has been investigated previously, the effects of VR-based motor priming prior to the training needs to be further addressed. This study uses machine learning (ML) to find out whether or not VR-induced avatar embodiment before the actual MI-BCI training is capable to make significant differences in electroencephalography (EEG) signals recorded from the users. Detecting the most relevant features which best represent such differences enables the introduction of biomarkers of VR-based motor priming embodiment in MI-BCI applications. Relating these biomarkers to the specific neurophysiological functions which facilitate MI can help in design and development of MI-BCI applications with improved accuracy and performance.
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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 | 0.003 |
| 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) | 0.002 | 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".