SCFL Reference Implementation v1.0 — Standard Coherence Fidelity Layer: Python Measurement Framework for Consciousness Research

This repository contains the reference Python implementation of the Standard Coherence Fidelity Layer (SCFL), a substrate-independent, theory-agnostic measurement framework for quantifying structural organization in systems that support conscious processes.

The module implements four formally defined metrics:

∙ CF — Coherence Fidelity (Procrustes distance vs. baseline)

∙ DR — Drift Rate (KL-divergence slope over sliding windows)

∙ RI — Rupture Index (change-point detection at CF < θ_r = 0.3)

∙ RT — Recoverability Time (latency from rupture onset to CF + δ_r recovery)

Biological embedding pipelines are provided for EEG/MEG, ECoG/LFP, fMRI, multi-unit recordings, and TMS-EEG perturbation studies. The implementation is compatible with the SciPy/scikit-learn stack and includes a self-test suite.

Companion paper: Toward a Coherence-Based Measurement Framework for Consciousness Research — Standard Coherence Fidelity Layer (SCFL) | Ronald Brogdon | Upstream Coherence Measurement Stratum (UCMS)