A Data Glove with Tactile Feedback for fMRI of Virtual Reality Experiments
Bibliographic record
Abstract
Virtual reality (VR) technology is increasingly recognized as a useful tool for the assessment and rehabilitation of neurologic and psychiatric disorders. The hope that VR can accurately mimic real-life events is also of great interest in basic neuroscience, to identify the brain activity that underlies complex behavior by combining VR with techniques such as functional magnetic resonance imaging (fMRI). Toward these applications, in this study we designed and validated an fMRI-compatible data glove with a built-in vibratory stimulus device for tactile feedback during VR experiments. A simple VR-fMRI experiment was performed at 3.0 Tesla on four young healthy adults involving touching a virtual object with and without tactile feedback. The usefulness of the data glove was subsequently assessed using a series of questionnaires, behavioral performance, and the resulting activation images. Questionnaire scores indicated positive opinions with respect to the data glove, the tactile feedback, and the experimental paradigm. All subjects felt comfortable in the scanner during the VR experiment and were able to perform all aspects of the tasks successfully and with reasonable accuracy. In addition, activation maps showed the anticipated modulations in motor, somatosensory, and parietal cortex. These results support that tactile feedback enhances the realism of virtual hand-object interactions, and that the tactile data glove is suitable for use in other VR-fMRI research applications (e.g., VR physical therapy for stroke recovery).
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".