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Halowerk Two-Channel Global Fourth-Cumulant ICA Projection
x402
Halowerk Two-Channel Global Fourth-Cumulant ICA Projection is a paid API for AI agents from detect.halowerk.com, paid per call via x402, $0.003/call, status unknown (last checked 2026-11-04).
Performs independent component analysis on two-channel signal data using fourth-cumulant (kurtosis-based) methods to recover non-Gaussian source projections and stationary-contrast artifacts.
Price / call$0.003
Success—
StatusUnknown
How this service works
Recover a complete two-channel nonGaussian component projection and stationary-contrast artifact.
Output
Returns the recovered two-channel non-Gaussian independent component projection along with the stationary-contrast artifact, computed via global fourth-cumulant (kurtosis-based) ICA methods applied to the supplied observation pairs.
Example use cases
EEG two-channel artifact removal
“I have 50 paired two-channel EEG readings (values scaled between -10 and 10) and I need to separate the neural signal from the artifact component using fourth-cumulant ICA — can you run the non-Gaussian projection and give me the stationary-contrast artifact?”
Audio stereo source separation
“I've got 40 stereo audio sample pairs from a mixed recording, each channel value normalized between -10 and 10 — please decompose them using two-channel global fourth-cumulant ICA to recover the independent source projections.”
Sensor data blind source separation
“I have 20 paired observations from two mixed sensor streams, with values ranging between -5 and 5, and I want to recover the original non-Gaussian independent components using fourth-cumulant ICA analysis.”
Technical details
Endpoint
POSThttps://detect.halowerk.com/v1/F-TD2000-GEO-INDUSTRY-B013/two-channel-global-fourth-cumulant-independent-component-projection?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need fast, single-call blind source separation on exactly two-channel mixed signal data using a kurtosis/fourth-cumulant statistical approach rather than second-order PCA methods. Best suited for non-Gaussian source signals with up to 64 observation samples per call.
Side effects
- Fewer than 8 observation pairs provided (minItems violation)
- More than 64 observation pairs provided (maxItems violation)
- Observation values outside the [-10, 10] range
- Malformed ICA object missing required 'data' or 'claim' fields
- Numerically degenerate input causing ICA to fail (e.g., constant or perfectly correlated channels)
- Missing 'observations' array inside 'data' object
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| artifact | object |
Tags
x402
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