The effects of stochastic monopolar galvanic vestibular stimulation on human postural sway
Bibliographic record
Abstract
Galvanic vestibular stimulation (GVS) is a technique in which small currents are delivered transcutaneously to the afferent nerve endings of the vestibular system through electrodes placed over the mastoid bones. The applied current alters the firing rates of the peripheral vestibular afferents, causing a shift in a standing subject's vestibular perception and a corresponding postural sway. Previously, we showed that in subjects who are facing forward, stochastic bipolar binaural GVS leads to coherent stochastic mediolateral postural sway. The goal of this pilot study was to extend that work and to test the hypothesis that in subjects who are facing forward, stochastic monopolar binaural GVS leads to coherent stochastic anteroposterior postural sway. Stochastic monopolar binaural GVS was applied to ten healthy young subjects. Twenty-four trials, each containing a different galvanic input stimulus from among eight different frequency ranges, were conducted on each subject. Postural sway was evaluated through analysis of the center-of-pressure (COP) displacements under each subject's feet. Spectral analysis was performed on the galvanic stimuli and the COP displacement time series to calculate the coherence spectra. Significant coherence was found between the galvanic input signal and the anteroposterior COP displacement in some of the trials (i.e., at least one) in nine of the ten subjects. In general, the coherence values were highest for the mid-range frequencies that were tested, and lowest for the low- and high-range frequencies. However, the coherence values we obtained were lower than those we previously reported for stochastic bipolar binaural GVS and mediolateral sway. These differences may be due to fundamental characteristics of the vestibular system such as lower sensitivity to symmetric changes in afferent firing dynamics, and/or differences between the biomechanics of anteroposterior and mediolateral sway.
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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 | 0.001 |
| 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) | 0.001 | 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".