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Record W4416840818 · doi:10.1002/app.70099

Hybrid Flexible Dry Electrodes for Electroencephalography: Electrical and Thermo‐Mechanical Properties

2025· article· en· W4416840818 on OpenAlexafffund
George Gnonhoue, Éric David, Jérémie Voix, Ilyass Tabiai

Bibliographic record

VenueJournal of Applied Polymer Science · 2025
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neural Engineering
Canadian institutionsÉcole de Technologie SupérieureUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsCarbon blackElectrodeElastomerThermoplastic elastomerElectrical conductorCarbon nanotubeModulusPolymer

Abstract

fetched live from OpenAlex

ABSTRACT This study evaluates styrene–ethylene–butylene–styrene (SEBS) composites modified with carbon nanotubes (CNTs) and carbon black (CB) for flexible electroencephalography (EEG) electrodes. Maleic anhydride‐grafted SEBS (SEBS‐MA) with 8 wt% CNTs and 2 wt% CB provides an optimal balance of conductivity and flexibility, a storage modulus comparable to SEBS, and yields a highly flexible conductive material. SEBS and SEBS‐MA composites with 8 wt% CNT/2 wt% CB produced stable, low‐noise signals, suggesting responsiveness to brain activity. The contact impedance of the elastomeric thermoplastic polymer (SEBS)/8 wt% CNT/2 wt% CB electrode is 4.25 ± 0.5 kΩ, and 4.5 ± 0.6 kΩ for SEBS‐MA/8 wt% CNT/2 wt% CB, comparable to a commercial electrode (4.75 ± 1.5 kΩ). SEBS/8 wt% CNT/2 wt% CB and SEBS‐MA/8 wt% CNT/2 wt% CB produced stable, low‐noise EEG signals. In vivo EEG recordings demonstrated that SEBS‐MA with 8 wt% CNT/2 wt% CB effectively captured transitions between the eyes‐open and eyes‐closed states, yielding clear and stable signals. These findings suggest that SEBS‐MA/8 wt% CNT/2 wt% CB is a promising material for flexible, high‐performance EEG electrodes due to its balance of electrical conductivity, mechanical stability, and signal clarity.

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)0.000
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 score0.046
Threshold uncertainty score0.549

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.017
GPT teacher head0.254
Teacher spread0.237 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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