Fascicle-Selective Ultrasound-Powered Bidirectional Wireless Peripheral Nerve Interface IC
Bibliographic record
Abstract
This paper presents an innovative, minimally invasive, battery-free, wireless, peripheral nervous system (PNS) neural interface, which seamlessly integrates a millimeter-scale, fascicle-selective integrated circuit (IC) with extraneural recording and stimulating channels. The system also incorporates a wearable interrogator equipped with integrated machine-learning capabilities. This PNS interface is specifically tailored for adaptive neuromodulation therapy, targeting individuals with paralysis, amputation, or chronic medical conditions. By employing a neural pathway classifier and temporal interference stimulation, the proposed interface achieves precise deep fascicle selectivity for recording and stimulation without the need for nerve penetration or compression. Ultrasonic energy harvesters facilitate wireless power harvesting and data reception, enhancing the usability of the system. Key circuit performance metrics encompass a 2.2 μVrms input-referred noise, 14-bit ENOB, and a 173 dB Schreier figure of merit (FOM) for the neural analog-to-digital converter (ADC). Additionally, the ultra-low-power radio-frequency (RF) transmitter boasts a remarkable 1.38 pJ/bit energy efficiency. In vivo experiments conducted on rat sciatic nerves provide compelling evidence of the interface's ability to selectively stimulate and record neural fascicles. The proposed PNS neural interface offers alternative treatment options for diagnosing and treating neurological disorders, as well as restoring or repairing neural functions, improving the quality of life for patients with neurological and sensory deficits.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".