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Characterizing the reduction of stimulation artifact noise in a tripolar nerve cuff electrode by application of a conductive shield layer

2016· article· en· W2560168635 on OpenAlexafffund
Parisa Sabetian, Bita Sadeghlo, Chengran Harvey Zhang, Paul B. Yoo

Bibliographic record

VenueMedical Engineering & Physics · 2016
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neural Engineering
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Toronto
KeywordsArtifact (error)CuffMaterials scienceNoise (video)Biomedical engineeringElectrodeSIGNAL (programming language)Electrical conductorShieldLayer (electronics)MedicineComputer scienceSurgeryComposite materialGeology

Abstract

fetched live from OpenAlex

Tripolar nerve cuff electrodes have been widely used for measuring peripheral nerve activity. However, despite the high signal-to-noise ratio levels that can be achieved with this recording configuration, the clinical use of cuff electrodes in closed-loop controlled neuroprostheses remains limited. This is largely attributed to artifact noise signals that contaminate the recorded neural activity. In this study, we investigated the use of a conductive shield layer (CSL) as a means of reducing the artifact noise recorded by nerve cuff electrodes. Using both computational simulations and in vivo experiments, we found that the CSL can result in up to an 85% decrease in the recorded artifact signal. Both the electrical conductivity and the surface area of the CSL were identified as important design criteria. Although this study shows that the CSL can significantly reduce artifact noise in tripolar nerve cuff electrodes, long-term implant studies are needed to validate our findings.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.319

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.253
Teacher spread0.234 · 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 teacher head, 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
Published2016
Admission routes2
Has abstractyes

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