This repository accompanies the exploratory preprint: “TLMM v5. 0: Exploratory Predictive and Adaptive Twin Framework for EEG Resilience Dynamics” TLMM v5. 0 extends prior landscape-based EEG resilience analysis toward a predictive and adaptive digital twin framework under uncertainty. The framework integrates: - multi-step resilience forecasting- uncertainty propagation via Monte Carlo ensembles- counterfactual twin scenario playback- adaptive thresholding- spatial early-warning propagation (SPI) - uncertainty-aware urgency estimation- streaming adaptive twin update- roadmap integration toward future adaptive twin systems Exploratory analyses were conducted using public EEG datasets including: - OpenNEURO- CHB-MIT Scalp EEG- TUAB / TUH EEG Abnormal Corpus Included materials: - full PDF manuscript- figure set (Fig. 1–12) - README- lightweight synthetic demonstration script (tlmmᵥ50demo. py) All analyses are exploratory, conceptual, and simulation-informed. Real EEG data are used only to parameterize models and estimate coupling/envelope dynamics. No clinical, diagnostic, prognostic, or therapeutic claims are made or implied. Mathematical forms are illustrative and intended for conceptual organization only. This work is intended for methodological investigation and hypothesis generation toward future prospective validation.
Koji Okino (Mon,) studied this question.