TLMM v5.1: Longitudinal Adaptive Resilience Twins for EEG Dynamics

TLMM v5.1 extends adaptive resilience twin frameworks for longitudinal EEG dynamics by introducing subject-level generalization, predictability horizon quantification, longitudinal drift-aware adaptation, semi-empirical resilience landscape reconstruction, topology-inspired precursor propagation, predictive uncertainty decomposition, counterfactual resilience exploration, and predictability–instability phase-plane analysis. The framework integrates probabilistic forecasting, adaptive forgetting, graph-based precursor dynamics, and resilience landscape reconstruction into a unified exploratory architecture validated across three public EEG datasets (OpenNEURO, CHB-MIT, TUAB; N = 43 subjects). Main features introduced in v5.1 include: • Subject-level out-of-sample validation• Predictability horizon estimation• Longitudinal twin drift tracking• Adaptive forgetting under nonstationarity• Semi-empirical resilience landscape reconstruction• Spatial precursor propagation using graph Laplacian dynamics• Predictive uncertainty decomposition• Counterfactual resilience exploration• Predictability–instability phase-plane interpretation• Conceptual roadmap toward closed-loop adaptive resilience systems Observed subject-level predictive consistency reached approximately:ρ ≈ 0.50–0.58 across datasets. Median personalized predictability horizons ranged from approximately:20–34 minutes depending on dataset characteristics. All results are exploratory and illustrative.No clinical deployment, therapeutic recommendation, or validated intervention strategy is claimed or implied. Files included:• Main PDF manuscript• Full figure set (Fig.1–11)• LaTeX source• Python demonstration script• README documentation This work is intended as a methodological and conceptual contribution toward adaptive resilience forecasting and longitudinal twin modeling for nonstationary neural dynamics.