Closed-Loop Quality Control

Closed-Loop Quality Control is the feedback mechanism at the core of Informed Acquisition. The acquisition system computes signal quality metrics in real time, compares them against expected benchmarks, and alerts the researcher when deviations are detected — enabling intervention during the session, before the data is lost.

How it works: Allego continuously computes per-channel SNR, impedance, unit yield estimates, and other metrics. When a metric deviates from the expected signal model (e.g., impedance drift on specific channels, SNR drop below threshold, sudden channel dropout), the system flags the deviation. The researcher can then intervene: re-ground, reposition the probe, check connectors, or adjust parameters.

Distinction from closed-loop stimulation: This is not closed-loop neural stimulation. It is closed-loop quality control — the same engineering principle (detect → evaluate → act) applied to data quality rather than neural activity.

Impact: The conventional workflow discovers problems during offline analysis, hours or days after the session. Closed-loop quality control discovers problems during the session, when they can still be fixed. This is the fundamental capability that separates Informed Acquisition from standard acquisition.