Optimizing the design of bipolar nerve cuff electrodes for improved recording of peripheral nerve activity
Bibliographic record
Abstract
OBJECTIVE: Differential measurement of efferent and afferent peripheral nerve activity offers a promising means of improving the clinical utility of implantable neuroprostheses. The tripolar nerve cuff electrode has historically served as the gold standard for achieving high signal-to-noise ratios (SNRs) of the recordings. However, the symmetrical geometry of this electrode array (i.e. electrically-shorted side contacts) precludes it from measuring electrical signals that can be used to obtain directional information. In this study, we investigated the feasibility of using a bipolar nerve cuff electrode to achieve high-SNR of peripheral nerve activity. APPROACH: A finite element model was implemented to investigate the effects of electrode design parameters-electrode length, electrode edge length (EEL), and a conductive shielding layer (CSL)-on simulated single fiber action potentials (SFAP) and also artifact noise signals (ANS). MAIN RESULTS: Our model revealed that the EEL was particularly effective in increasing the peak-to-peak amplitude of the SFAP (319%) and reducing the common mode ANS (67%) of the bipolar cuff electrode. By adding a CSL to the bipolar cuff electrode, the SNR was found to be 65.2% greater than that of a conventional tripolar cuff electrode. In vivo experiments in anesthetized rats confirmed that a bipolar cuff electrode can achieve a SNR that is 38% greater than that achieved by a conventional tripolar cuff electrode (p < 0.05). SIGNIFICANCE: The current study showed that bipolar nerve cuff electrodes can be designed to achieve SNR levels that are comparable to that of tripolar configuration. Further work is needed to confirm that these bipolar design parameters can be used to record bi-directional neural activity in a physiological setting.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".