A Framework for Detecting High-Order Statistical Associations in Discretized Biosignals

This work presents a formalized procedure for analyzing a specific class of persistent correlations in the meta-statistical properties of discretized biological signals. We define a binary encoding process, termed a Ze-stream, which transforms a continuous biosignal into a sequence of threshold-crossing events. The framework's core operational postulate, the Ze-ceiling, conjectures a limit on a system's capacity for perfect self-modeling of its own encoding process. The primary contribution is a concrete statistical formulation for detecting correlations between Ze-streams of two systems that exceed a well-defined chance level, based on a testable null hypothesis derived from the Poisson clumping heuristic. We demonstrate that the specific observables are non-signaling in a practical sense because their estimation necessitates ensemble analysis over extended time windows, precluding real-time single-shot communication. This operational constraint shares conceptual similarity with — but is not isomorphic to — the no-signaling condition in communication theory. We present explicit experimental protocols using synchronized biosignal measurements (e.g., heart rate variability) with detailed parameter estimation and falsification criteria.