Selective peripheral nerve recording using simulated human median nerve activity and convolutional neural networks
Bibliographic record
Abstract
BACKGROUND: It is difficult to create intuitive methods of controlling prosthetic limbs, often resulting in abandonment. Peripheral nerve interfaces can be used to convert motor intent into commands to a prosthesis. The Extraneural Spatiotemporal Compound Action Potentials Extraction Network (ESCAPE-NET) is a convolutional neural network (CNN) that has previously been demonstrated to be effective at discriminating neural sources in rat sciatic nerves. ESCAPE-NET was designed to operate using data from multi-channel nerve cuff arrays, and use the resulting spatiotemporal signatures to classify individual naturally evoked compound action potentials (nCAPs) based on differing source fascicles. The applicability of this approach to larger and more complex nerves is not well understood. To support future translation to humans, the objective of this study was to characterize the performance of this approach in a computational model of the human median nerve. METHODS: Using a cross-sectional immunohistochemistry image of a human median nerve, a finite-element model was generated and used to simulate extraneural recordings. ESCAPE-NET was used to classify nCAPs based on source location, for varying numbers of sources and noise levels. The performance of ESCAPE-NET was also compared to ResNet-50 and MobileNet-V2 in the context of classifying human nerve cuff data. RESULTS: Classification accuracy was found to be inversely related to the number of nCAP sources in ESCAPE-NET (3-class: 97.8% ± 0.1%; 10-class: 89.3% ± 5.4% in low-noise conditions, 3-class: 70.3% ± 0.1%; 10-class: 52.5% ± 0.3% in high-noise conditions). ESCAPE-NET overall outperformed both MobileNet-V2 (3-class: 96.5% ± 1.1%; 10-class: 84.9% ± 1.7% in low-noise conditions, 3-class: 86.0% ± 0.6%; 10-class: 41.4% ± 0.9% in high-noise conditions) and ResNet-50 (3-class: 71.2% ± 18.6%; 10-class: 40.1% ± 22.5% in low-noise conditions, 3-class: 81.3% ± 4.4%; 10-class: 31.9% ± 4.4% in high-noise conditions). CONCLUSION: All three networks were found to learn to differentiate nCAPs from different sources, as evidenced by performance levels well above chance in all cases. ESCAPE-NET was found to have the most robust performance, despite decreasing performance as the number of classes increased, and as noise was varied. These results provide valuable translational guidelines for designing neural interfaces for human use.
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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".