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Record W2791323418

A Low-Power Front-end Module Dedicated to Wireless Cortical Data Recording

2005· article· en· W2791323418 on OpenAlexaff
Benoit Gosselin, V. Simard, Juan Francisco Roy Delgado, Mohamad Sawan

Bibliographic record

VenuePolyPublie (École Polytechnique de Montréal) · 2005
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neural Engineering
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsFront and back endsAnalog front-endCMOSComputer scienceComputer hardwareChopperNoise (video)Electronic engineeringEmbedded systemElectrical engineeringEngineeringVoltageArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

This paper reports on the design and the implementation of a fully implantable cortical signals acquisition system. The pre-processing stage, one of the main modules, has been designed in CMOS 0.18um process and sent for fabrication. It includes a low-noise multichannel front-end and a new prototype analog wavelet processor intended for on-line neural signal detection. Ultra low power consumption is achieved for both modules with usage of CMOS weakly inverted transistors. Special attention has been paid on recording quality in the front-end design by using Chopper modulation technique. The proposed front-end achieves an input referred noise of less than 30nV/√Hz and its power consumption is below 20µW per channel. Custom object oriented software has also been implemented for neural data visualisation, storage and analysis, and for system configuration.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.359
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.001
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 head0.026
GPT teacher head0.261
Teacher spread0.235 · 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.

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
Published2005
Admission routes1
Has abstractyes

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