Closed-loop Control of Peripheral Nerve Stimulation using Resource-efficient Neural Networks
Bibliographic record
Abstract
Closed-loop control of functional electrical stimulation (FES) can restore movement to paralyzed limbs by using recorded nerve signals to modulate stimulation in real-time. Previous work performed state-of-the-art classification of nerve signals using neural networks (NNs). This thesis explored reduced NNs for use in implanted systems and incorporated such NNs into closed-loop stimulation. NNs of varying complexities were evaluated for performance and resource usage when classifying individual naturally-evoked compound action potentials, previously collected from rat sciatic nerves using 7x8-channel cuff electrodes. Acute in vivo experiments used NNs to estimate nerve branch activity in real-time as feedback for neural stimulation, producing desired ankle movement trajectories. Reduced NNs achieved macro F1-scores of 0.70-0.71 while remaining within resource constraints defined by NN implementations in integrated circuit fabrication technologies. We demonstrated closed-loop stimulation in 6 rats. NNs that classify nerve signals can be reduced to fit implantable hardware platforms and used for closed-loop stimulation control.
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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) | 0.000 | 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 | 0.000 |
| 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".