Evaluation of high-density, multi-contact nerve cuffs for activation of grasp muscles in monkeys
Bibliographic record
Abstract
OBJECTIVE: The objective of this work was to evaluate whether nerve cuffs can selectively activate hand muscles for functional electrical stimulation (FES). FES typically involves identifying and implanting electrodes in many individual muscles, but nerve cuffs only require implantation at a single site around the nerve. This method is surgically more attractive. Nerve cuffs may also more effectively stimulate intrinsic hand muscles, which are difficult to implant and stimulate without spillover to adjacent muscles. APPROACH: To evaluate its ability to selectively activate muscles, we implanted and tested the flat interface nerve electrode (FINE), which is designed to selectively stimulate peripheral nerves that innervate multiple muscles (Tyler and Durand 2002 IEEE Trans. Neural Syst. Rehabil. Eng. 10 294-303). We implanted FINEs on the nerves and bipolar intramuscular wires for recording compound muscle action potentials (CMAPs) from up to 20 muscles in each arm of six monkeys. We then collected recruitment curves while the animals were anesthetized. MAIN RESULT: A single FINE implanted on an upper extremity nerve in the monkey can selectively activate muscles or small groups of muscles to produce multiple, independent hand functions. SIGNIFICANCE: FINE cuffs can serve as a viable supplement to intramuscular electrodes in FES systems, where they can better activate intrinsic and extrinsic muscles with lower currents and less extensive surgery.
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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 | 0.001 |
| 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 | 0.001 | 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".