From signals to features
Before any score, the SDK computes features: well-defined, reproducible numbers.- EEG window features: band powers (absolute, relative, log), spectral entropy, dominant frequency, alpha peak, Hjorth parameters, and quality flags
- Pulse features: heart rate, RMSSD, SDNN, and signal quality
- Robust statistics: medians and median absolute deviations instead of means, so one bad sample doesn’t skew the result
- Personal baselines: a rolling 30-day median and range for each person, with outlier rejection
SDK headline scores
These are product interpretations, never measurements, and the SDK says
so. Two design rules keep them honest:
- Every contributor is visible. Each score exposes its inputs with their weights, standardized values, and quality.
- Scores withhold themselves. Instead of guessing, a score returns no value plus a reason when it can’t be computed responsibly:
A missing input never silently becomes a neutral middle value.
The biometric Score on Elata
The Score is a single number from 0 to 100 that reflects a user’s results across all the apps they use, only if they opt in. How it’s built:- Apps report derived session results, never raw signal. A report says, for example, that the user’s calm level went from 0.42 at baseline to 0.61 during a five-minute session, with its signal quality and confidence.
- Improvement relative to the user’s own baseline matters, not absolute values that differ between people.
- Low-quality sessions are dropped, and the Score shows as “calibrating” until there are enough good sessions to be stable.
- Consent is checked on the server for every report, so an app can’t contribute without it.
- Only approved apps can contribute. An app needs a permission granted by Elata before its reports count.
Sources
biosignal-analyticsREADME (public SDK repository)- Metrics and scores (builder docs)