A Review on Implantable Neuroelectrodes
Bibliographic record
Abstract
The efficacy of every neuromodulation modality depends upon the characteristics of the electrodes used to stimulate the chosen target. The geometrical, chemical, mechanical and physical configuration of electrodes used in neurostimulation affects several performance attributes like stimulation efficiency, selectivity, tissue response, etc. The efficiency of stimulation in relation to electrode impedance is influenced by the electrode material and/or its geometry. The nature of the electrode material determines the charge transfer across the electrode-tissue interface, which also relates to neuronal tissue damage. Electrode morphology or configuration pattern can facilitate the modulation of extracellular electric field (field shaping). This enables selective activation of neurons and minimizes side effects. Biocompatibility and biostability of the electrode materials or electrode coating have a role in glial formation and tissue damage. Mechanical and electrochemical stability (corrosion resistance) determines the long-term efficacy of any neuromodulation technique. Here, a review of electrodes typically used for implantable neuromodulation is discussed. Factors affecting the performance of electrodes like stimulation efficiency, selectivity and tissue responses to the electrode-tissue interface are discussed. Technological advancements to improve electrode characteristics are also included.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".