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Implantable Electronics for the Recovery of Neuromuscular Functions

2008· article· en· W1968765794 on OpenAlexafffund
Mohamad Sawan, Benoit Gosselin, Jonathan Coulombe, Amer Elias Ayoub, Ananish Chaudhuri, Frank Louis Lepore

Bibliographic record

VenueAdvances in science and technology · 2008
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neural Engineering
Canadian institutionsUniversité de MontréalMcGill UniversityPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCMC Microsystems
KeywordsMicrosystemMicrostimulationReliability (semiconductor)NeuroprostheticsElectronicsMaterials scienceWirelessComputer scienceElectronic engineeringElectrical engineeringPower (physics)NanotechnologyEngineeringStimulationNeuroscience

Abstract

fetched live from OpenAlex

This paper covers circuits and systems techniques for the construction of high reliability biosensing and stimulation medical devices. Such microsystems are dedicated for interconnections through either the central or the peripheral nervous systems. Low-power high-reliability wireless links are used to power up the implanted devices while data are exchanged bidirectionaly between these implants and external controllers. A global view of main devices is given, case studies related to applications such as bladder control, intracortical monitoring and microstimulation are discussed, altogether with modeling, characterization, as well as microsystems assembly and packaging. Also, dedicated electrode arrays and their interfaces to tissues interfaces are summarized.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.003

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.

Opus teacher head0.016
GPT teacher head0.263
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2008
Admission routes2
Has abstractyes

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