Influence of the Number and Location of Recording Contacts on the Selectivity of a Nerve Cuff Electrode
Bibliographic record
Abstract
A 56-contact matrix nerve cuff electrode (seven rings with eight contacts each) was used to obtain recordings from the rat sciatic nerve, which were then discriminated as originating from one of three fascicles (tibial, peroneal, and sural branches). The influence of the number and location of the recording contacts on the classification accuracy was studied. The performance of a classifier was shown to be superior when data was available from all 56 contacts, compared to when only the eight contacts of the middle ring were used (as in previously proposed multicontact tripolar cuff designs). By examining the performance variations as contacts were included one at a time (in order of decreasing positive impact on performance), it was further shown that the matrix configuration could outperform the single-ring configuration with only a small number of contacts. We can therefore conclude that the performance improvement is not due to the sheer number of contacts, but rather to the possibility of selecting the most informative locations around the nerve. The results could have important implications for the design and use of multicontact nerve cuff electrodes.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".