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Record W2153120778 · doi:10.1109/tnsre.2007.906962

A Haptic Force Feedback Device for Virtual Reality-fMRI Experiments

2007· article· en· W2153120778 on OpenAlexaff
Lisa M. Di Diodato, Richard Mraz, Shawn Baker, Simon J. Graham

Bibliographic record

VenueIEEE Transactions on Neural Systems and Rehabilitation Engineering · 2007
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsBaycrest HospitalUniversity of TorontoHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsHaptic technologyVirtual realityFunctional magnetic resonance imagingComputer scienceVirtual imageBrain–computer interfaceHuman–computer interactionSimulationArtificial intelligencePsychologyNeuroscienceElectroencephalography

Abstract

fetched live from OpenAlex

Simulation of real-world tasks using virtual reality (VR) and measurement of associated neural activity by functional magnetic resonance imaging (fMRI) have potential utility in research and clinical stroke applications. However, development of fMRI-compatible sensory feedback technology is required. Presented here is the development of a prototype force feedback device for VR-fMRI. Experiments validated device performance in terms of force output, interaction bandwidth, transmission delay, and fMRI-compatibility. A subsequent VR-fMRI experiment involved six participants touching a virtual object and verified modulation of brain activity with force feedback versus no force feedback. This device may facilitate further experiments to clarify the effect of haptics in VR, and may be adapted for characterizing brain function and behaviour associated with stroke-related hand paresis.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.667
Threshold uncertainty score0.615

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.268
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations29
Published2007
Admission routes1
Has abstractyes

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