Every session you run generates value. EE Token captures it.
A federated incentivization layer where wellness centers earn analytical currency by contributing high-quality biometric data—no speculation, no PII, just science.
Four steps from biometric session to analytical value. No wallets, no gas fees—just data quality translating directly into network utility.
A wellness center conducts a biometric session. Sensor data is captured, timestamped, and locally hashed before anything leaves the device.
The session is evaluated across four axes: signal-to-noise ratio, artifact rejection, duration completeness, and sensor calibration. A composite quality score is computed.
Higher-quality sessions earn proportionally more EE Tokens. The scoring is deterministic—same data always yields the same reward. No staking, no mining.
Tokens are redeemed for analytical outputs: population trajectories, intervention comparisons, phenotype distributions. Contributors consume the value they helped create.
Token yield is a direct function of measurable data integrity. Each dimension contributes to the composite score that determines your earning rate.
Clean biometric signal with minimal environmental interference. Measured as the ratio of physiological signal power to baseline noise floor across all sensor channels.
Percentage of data windows that pass artifact detection filters. Motion artifacts, electrode drift, and saturation events are flagged and quantified automatically.
Ratio of usable recording time to the protocol-defined session length. Early terminations, gaps, and dropout periods reduce this metric proportionally.
Whether sensors were calibrated within the manufacturer-specified window. Includes impedance checks, baseline drift verification, and gain validation prior to session start.
Contributors are also consumers. The same network that ingests your session data produces the analytical outputs you redeem tokens for.
Traditional data marketplaces create extractive one-way flows. EE Token closes the loop: every contributor enriches the models that produce the analytics they want access to.
No personally identifiable information ever leaves the contributing site. Provenance is mathematically verifiable, not trust-based.
Sessions are identified by randomly generated UUIDs with no reversible link to individuals. No names, no demographics, no device fingerprints. The network literally cannot re-identify participants.
Raw biometric data never leaves the originating site. Only model parameter updates—gradients and weights—traverse the network. The central aggregator never sees a single waveform.
Every session's quality score is chained to the previous entry via SHA-256 digest, then anchored to a public timestamp. Tampering with any record breaks the chain detectably.