Flexible Dry Electrodes Based on Styrene‐Ethylene‐Butylene‐Styrene/Carbon Black and Ethylene‐Vinyl Acetate/Carbon Black for Electroencephalography: Electrical, Thermal and Mechanical Properties
Bibliographic record
Abstract
ABSTRACT Electroencephalography (EEG) is an essential technique for monitoring brain electrical activity in clinical, sports, and wearable health settings. However, traditional wet electrodes face issues like gel drying and skin irritation, while coated dry electrodes tend to degrade over time, affecting long‐term signal stability. This study explores flexible dry electrodes made from conductive polymer composites—poly(styrene‐b‐ethylene‐ran‐butylene‐b‐styrene) filled with carbon black (SEBS/CB) and ethylene‐vinyl acetate filled with carbon black (EVA/CB)—as affordable and recyclable alternatives to standard materials such as PDMS and TPU. The electrodes were manufactured using solvent casting and compression molding, ensuring even filler distribution and consistent surface quality. Both composites reached an electrical conductivity of around 0.01 S/m with a percolation threshold close to 12 wt% CB. Contact impedance tests showed better performance for SEBS/CB electrodes (5.4 ± 0.9 kΩ) compared to EVA/CB (26.7 ± 4.4 kΩ), nearing the value of a commercial flexible electrode (4.2 ± 0.5 kΩ). Mechanical testing confirmed that SEBS/CB is softer and more elastic, facilitating stable, low‐noise EEG signal collection. Overall, SEBS/CB composites provide a good balance of electrical performance, flexibility, and scalability, highlighting their potential for next‐generation, long‐term EEG monitoring systems.
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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".