Virtual electrodes generated by focused penta‐polar current stimulation for neuromodulation
Bibliographic record
Abstract
Virtual electrodes in neuromodulation can provide more delicate stimulation patterns with a limited number of physical electrodes in a confined area. Many researchers successfully verified the effectiveness of virtual electrodes in clinical trials, using current steering, which modulates electric fields produced by multi‐polar stimulation. Still, it is questioned how these virtual electrodes are really generated in an electrolyte, especially in two dimensions. In order to answer this question, this work analyses the virtual electrode generation by comparing finite element analysis and in vitro evaluation of penta‐polar stimulation. Penta‐polar stimulation was realised using custom‐designed integrated circuits and electrodes with an individual diameter of 450 μm and a centre‐to‐centre spacing of 800 μm. The customised test setup of electric field measurement estimated (i) the focused electric fields by the penta‐polar stimulation and (ii) the optimum distance from the electrodes, at which virtual electrodes were most effectively generated. Compared with mono‐polar stimulation, the penta‐polar stimulation showed 0.594 and 0.545 times smaller electric field distribution areas, respectively, at a distance from the electrodes of 100 μm. Furthermore, the virtual electrodes showed the best performance at a distance from the electrodes of 150 μm, while the distance varied from 13 to 250 μm.
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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 | 0.000 |
| 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 | 0.001 |
| 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".