Electrical Vestibular Stimulation in Humans: A Narrative Review
Bibliographic record
Abstract
BACKGROUND: In patients with bilateral vestibulopathy, the regular treatment options, such as medication, surgery, and/or vestibular rehabilitation, do not always suffice. Therefore, the focus in this field of vestibular research shifted to electrical vestibular stimulation (EVS) and the development of a system capable of artificially restoring the vestibular function. Key Message: Currently, three approaches are being investigated: vestibular co-stimulation with a cochlear implant (CI), EVS with a vestibular implant (VI), and galvanic vestibular stimulation (GVS). All three applications show promising results but due to conceptual differences and the experimental state, a consensus on which application is the most ideal for which type of patient is still missing. SUMMARY: Vestibular co-stimulation with a CI is based on "spread of excitation," which is a phenomenon that occurs when the currents from the CI spread to the surrounding structures and stimulate them. It has been shown that CI activation can indeed result in stimulation of the vestibular structures. Therefore, the question was raised whether vestibular co-stimulation can be functionally used in patients with bilateral vestibulopathy. A more direct vestibular stimulation method can be accomplished by implantation and activation of a VI. The concept of the VI is based on the technology and principles of the CI. Different VI prototypes are currently being evaluated regarding feasibility and functionality. So far, all of them were capable of activating different types of vestibular reflexes. A third stimulation method is GVS, which requires the use of surface electrodes instead of an implanted electrode array. However, as the currents are sent through the skull from one mastoid to the other, GVS is rather unspecific. It should be mentioned though, that the reported spread of excitation in both CI and VI use also seems to induce a more unspecific stimulation. Although all three applications of EVS were shown to be effective, it has yet to be defined which option is more desirable based on applicability and efficiency. It is possible and even likely that there is a place for all three approaches, given the diversity of the patient population who serves to gain from such technologies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".