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Record W2995231476 · doi:10.1152/jn.00276.2019

Development of a conversion model between mechanical and electrical vestibular stimuli

2019· article· en· W2995231476 on OpenAlexafffund
A. Chen, Navid Khosravi‐Hashemi, Calvin Kuo, J. K. G. Kramer, Jean Blouin

Bibliographic record

VenueJournal of Neurophysiology · 2019
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsInternational Collaboration On Repair DiscoveriesUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsVestibular systemNeuroscienceCommunicationPsychology

Abstract

fetched live from OpenAlex

The vestibular end-organs encode for linear and angular head accelerations in space contributing to our internal representation of self-motion. Activation of the vestibular system with transmastoid electrical current has recently grown in popularity; however, a direct relationship between electrically evoked and mechanically evoked vestibular responses remains elusive in humans. We have developed and tested a mechanical-to-electrical vestibular stimulus conversion model incorporating physiological activation of primary vestibular afferents identified in nonhuman primates. We compared ocular torsional responses between mechanical (chair rotation) and model-derived electrical (binaural-bipolar) stimuli in separate experiments for an angular velocity step change (±10 deg/s over 1 s, ±4-mA peak amplitude; n = 10) and multisine angular velocities (±10 deg/s, 9.7 mA peak to peak, 0.05–1 Hz; n = 5), respectively. Perception of whole body rotation ( n = 18) to our step-change stimuli was also evaluated. Ocular torsional slow-phase velocity responses between stimulation types were similar (paired two one-sided tests of equivalence: multiple P < 0.002; one-sample t test: P = 0.178) and correlated (Pearson’s coefficient: multiple P < 0.001). Bootstrap analysis of perceived angular velocity likewise showed similarity in perceptual decay dynamics. These data suggest that central processing between stimuli was similar, and our vestibular stimulus conversion model with a conversion factor of ∼0.4 mA per deg/s for an angular velocity step change can generate electrical stimuli that replicates dynamic vestibular activation elicited by mechanical whole body rotations. This proposed vestibular conversion model represents an initial framework for using electrical stimuli to generate mechanically equivalent activation of primary vestibular afferents for use in biomedical applications and immersive reality technologies. NEW & NOTEWORTHY With the growing popularity of electrical vestibular stimulation in biomedical and immersive reality applications, a direct conversion model between electrical and mechanical vestibular stimuli is needed. We developed a model to generate electrical stimuli mimicking the physiological activation of vestibular afferents evoked by mechanical rotations. Ocular and perceptual responses evoked by mechanical and model-derived electrical stimuli were similar, thus providing a critical first step toward generation of electrically induced vestibular responses that have a realistic mechanical equivalent.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.042
GPT teacher head0.265
Teacher spread0.223 · 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 source (direct Gemma or distilled Codex), 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

Citations13
Published2019
Admission routes2
Has abstractyes

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