TLMM v5. 2 (Topological Landscape Manifold Model) is an exploratory framework for adaptive resilience modeling in EEG dynamics integrating: - site-level external validation, - dynamic predictability horizon estimation, - Personalized Risk Index (PRI), - time-varying coupling dynamics, - uncertainty decomposition, - baseline forecasting comparison, - and exploratory real-time feasibility assessment. The framework combines adaptive resilience twins, online predictability estimation, uncertainty-aware monitoring, and conceptual closed-loop adaptation strategies. External validation on an independent holdout site (TUH EEG Corpus) demonstrates improved robustness, calibration, and predictability-horizon estimation relative to standard forecasting baselines including AR (p), LSTM, linear state-space, and moving-average models. The repository includes: - full manuscript PDF, - figure set, - and a lightweight synthetic demonstration script (tlmmᵥ52demo. py). All analyses are exploratory and hypothesis-generating only. No diagnostic, therapeutic, or clinical decision-making claims are made. Keywords: adaptive resilience, EEG dynamics, predictability horizon, uncertainty quantification, adaptive twins, resilience modeling, external validation, early warning systems, real-time feasibility, seizure dynamics
Koji Okino (Wed,) studied this question.