What question did this study set out to answer?

To develop a unified framework linking public resting-state EEG data to digital twin inference for exploratory analysis.

May 18, 2026Open Access

TLMM v4.8: Unified Public EEG Contact Framework for Exploratory Digital Twin Inference

Key Points

To develop a unified framework linking public resting-state EEG data to digital twin inference for exploratory analysis.
Utilized public EEG envelope extraction and fluctuation analysis.
Employed various divergence metrics for statistical evaluations.
Conducted computational analyses to assess runtime and memory scaling properties.
Demonstrated near-linear runtime scaling (O(T^0.95)) and sub-linear memory scaling (O(T^0.85)) across long recordings.
Introduced a proof-of-contact framework using metrics like KL divergence and Spearman correlation.
Provided subject-specific digital twin examples and a roadmap for exploratory inference landscapes.

Abstract

This repository presents TLMM v4. 8, a unified exploratory framework connecting public resting-state EEG data to adaptive digital twin inference through envelope-based fluctuation analysis, feasibility-oriented sensitivity mapping, uncertainty-aware inference, and streaming adaptive updates. The framework integrates: 1. Public EEG envelope extraction2. Fokker–Planck landscape estimation3. Resilience-oriented inference4. Sensitivity landscape mapping5. Subject-specific digital twin construction6. Streaming adaptive update dynamics7. Limitation and constraint characterization8. Roadmap and progress tracking9. Computational feasibility analysis A central contribution of this work is the introduction of a quantitative “proof-of-contact” framework evaluating statistical comparability between simulation-generated envelope dynamics and public EEG-derived envelopes using: - KL divergence- Jensen–Shannon divergence- Earth Mover’s Distance- Spearman correlation- Dynamic Time Warping (DTW) The framework further introduces: - exploratory feasible inference landscapes in (α, τₙ) parameter space, - adaptive forgetting dynamics, - subject-specific digital twin examples, - systematic limitation mapping, - and scalability characterization under streaming adaptive inference. Computational analyses show near-linear runtime scaling (approximately O (T⁰. 95) ) and sub-linear memory scaling (approximately O (T⁰. 85) ) across long recording durations using a streaming implementation. Importantly, this work does NOT claim: - clinical validity, - physiological equivalence, - diagnosis, - prognosis, - or therapeutic utility. All analyses, figures, and metrics are exploratory and intended solely for methodological feasibility investigation and transparent uncertainty-aware computational research. Repository contents include: - Full PDF manuscript- Figures (Fig. 1–Fig. 9) - README- Exploratory computational demo script Data sources: - OpenNEURO- PhysioNet License: Research and exploratory use only.

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