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

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, τ_n) 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(T^0.95)) and sub-linear memory scaling (approximately O(T^0.85)) 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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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-05-16
DOI
https://doi.org/10.5281/zenodo.20233975
Primary Topic
Functional Brain Connectivity Studies
Type
preprint
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preprint

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

Koji Okino
Zenodo (CERN European Organization for Nuclear Research)
Functional Brain Connectivity Studies
preprint

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

Koji Okino
preprint en

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, τ_n) 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(T^0.95)) and sub-linear memory scaling (approximately O(T^0.85)) 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.

Zenodo (CERN European Organization for Nuclear Research)
United States Department of Labor (US)
Functional Brain Connectivity Studies
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