Caractérisation d'un circuit neuro-enregistreur faisant partie d'une boucle de contre-réaction sensorimotrice pour la stimulation intramédullaire
Bibliographic record
Abstract
Traumatic spinal cord injuries often result in severe sequelae, including paraplegia, which affects approximately four out of ten patients. This condition, characterized by the loss of motor function in the lower limbs, represents a major public health issue in Canada. Although there is currently no cure, strategies such as electrical stimulation can mitigate the functional consequences. The latter has demonstrated its ability to restore certain motor functions in several clinical trials. The only spinal cord stimulation system currently in clinical trials for motor restoration incorporates a closed loop (neural recording to adapt epidural stimulation), capable of restoring lower limb movement in some patients. However, this implant has low accuracy due to the extensive stimulation of nerve areas, including sensory regions, which can cause involuntary motor reflexes. In addition, the recording implant used is limited by its autonomy, requires delicate surgical implantation, and involves certain clinical risks. With the aim of increasing the precision of stimulation and improving the resolution of neural recordings by targeting the activity of individual neurons rather than the summation of entire populations, while ensuring autonomous and reliable operation, the Neurorestorative Interfaces Group (GIN) is developing a solution based primarily on sensorimotor feedback and real-time signal processing. This solution uses neurostimulation and neuro-recording implants developed by the Sherbrooke Medical Device Research Group (GRAMS) and interfaced with a haptic glove. This approach aims to offer paraplegic patients a more natural gait by activating intramedullary stimulation according to the motor intentions detected by the gloves, while providing sensory feedback. The master’s work presented in this thesis focuses on the development of a test interface to characterise the neural recording implant in terms of energy consumption, the operation of its blocks (such as system clock recovery), recording accuracy, and the performance of its integrated compression algorithm. This interface also makes it possible to validate compliance with the initial specifications and identify any faults. This characterisation is essential for integrating the implant into the closed loop envisaged by the GIN and for improving the next generation of implants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".