Pilot Evaluation of Sevo Systems for Epilepsy: Equitable EEG for Coarse, Dense, and Curly Hair
Bibliographic record
Abstract
Collecting electroencephalography (EEG) data from individuals with coarse, curly, or afro-textured hair can be challenging, leading to noisier data, negative clinical outcomes (e.g., risk of misdiagnosis, discomfort, longer setup times, unsavory treatment, hair shaving, and microaggressions), and unreliable basic science conclusions due to the disproportionate exclusion of people with this physical feature worldwide. Our prior work demonstrated that strategically braiding hair to expose the scalp at standard locations improves impedance and that novel electrode adapters that we developed called "Sevo" can further lower impedance by more than 15x. In this article, we further quantify the signal quality of Sevo EEG compared to traditional EEG on a cohort of pediatric epileptic patients and health adult volunteers. We assessed the influence of braiding and the use of Sevo adapters on signal quality and found that Sevo reduces noise in two key metrics of the power spectra, especially for the pediatric epilepsy group. We conclude by outlining best practices for preparing coarse, curly hair for use by EEG technicians, researchers, and patients.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".