What question did this study set out to answer?

The study aims to enhance the Time-Lagged Mutual Modeling framework to explore EEG data for reproducibility and uncertainty assessment.

May 19, 2026Open Access

TLMM v4.9: Exploratory Multi-Channel EEG Twin Framework with Test–Retest, Multi-Dataset, and Uncertainty-Aware Inference

Puntos clave

The study aims to enhance the Time-Lagged Mutual Modeling framework to explore EEG data for reproducibility and uncertainty assessment.
Developed multi-channel EEG envelope extraction and cross-channel coupling analysis.
Conducted test-retest reproducibility assessments and multi-dataset consistency analysis.
Implemented uncertainty propagation and counterfactual scenario simulations.
Inferred potential landscapes and resilience trajectories were reproducibly estimated from public EEG datasets.
Demonstrated the capability to track uncertainty and threshold dependence effectively.
Showed that the new framework can adaptivity simulate twin scenarios for exploratory analysis.

Resumen

This Zenodo record contains TLMM v4.9, an exploratory extension of the Time-Lagged Mutual Modeling (TLMM) framework toward multi-channel, multi-dataset, test–retest, and uncertainty-aware EEG twin inference. The framework extends the previous public EEG contact stage (v4.8) by incorporating: • Multi-channel EEG envelope extraction• Cross-channel coupling analysis• Representative null-model comparison• Threshold-dependence and robustness analysis• Test–retest reproducibility assessment• Subject-specific Edge Perturbation Index (EPI)• Multi-dataset consistency analysis• Streaming adaptive twin simulation• Counterfactual scenario simulation• Full-pipeline uncertainty propagation The manuscript evaluates whether inferred potential landscapes, resilience trajectories, and digital twin states can be reproducibly estimated from public EEG data while explicitly tracking uncertainty, threshold dependence, and model assumptions. Public EEG datasets referenced include OpenNEURO, CHB-MIT, and TUAB-style datasets used as exploratory methodological resources. This release includes:• PDF manuscript• Figure set (Fig.1–Fig.10)• Lightweight demonstration code (Python)• README documentation Important scope statement:All analyses are exploratory and hypothesis-generating.No diagnostic, prognostic, therapeutic, medical-device, or clinical utility claims are made.The framework is intended for methodological and computational neuroscience research only. Author:Koji OkinoIndependent Researcher / SD Lab2026

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