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Record W2103560945 · doi:10.1109/ccece.2003.1226019

Design and fabrication of a microelectrode array dedicated for cortical electrical stimulation

2004· article· en· W2103560945 on OpenAlexafffund
S. Pigeon, Michel Meunier, Mohamad Sawan, Sylvain Martel

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neural Engineering
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicroelectrodeMultielectrode arrayPhospheneElectrodeMaterials scienceVisual prosthesisFabricationBiomedical engineeringStimulationBiocompatibilityElectrode arrayVisual cortexOptoelectronicsElectrical impedancePlatinumNanotechnologyElectrical engineeringChemistryEngineeringMetallurgyNeuroscience

Abstract

fetched live from OpenAlex

A manufacturing process has been developed for a microelectrode array that can be used for stimulation of the visual cortex to provide an effective sense of sight to the blind. The first prototype merges sixteen 316L stainless steel electrodes on a 4/spl times/4 array. Several arrays can then be assembled together in a mosaic configuration to cover a large portion of the visual area. To reduce brain injuries while maximizing the quality of the phosphenes, the electrodes must be sharp, thin (50 /spl mu/m-diameter), separated 400 /spl mu/m apart and have stimulation sites located at 1.5 mm under the surface of the cortex. Using electrical discharge machining followed by an electrochemical surface treatment, it has been shown that it was possible to reach such geometrical ratios at this scale. This technique can be combined with electrodeposition of porous platinum to create low impedance stimulation sites. Encapsulation and biocompatibility of the array are also discussed.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

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 communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.039
GPT teacher head0.278
Teacher spread0.239 · 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

Citations13
Published2004
Admission routes2
Has abstractyes

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