Hybrid Flexible Dry Electrodes for Electroencephalography: Electrical and Thermo‐Mechanical Properties
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".