Zeinabsadat Saghi, Daria Riabukhina, Olubukola Akinbami, Paul Bogdan, Souti Chattopadhyay
Cognitive fatigue transitions from focused attention to inexact responses can cause catastrophic failures in high-stakes environments, yet current black-box assessment techniques ignore the brain's non-Markovian and time-varying interdependent properties, limiting realtime phase transition detection. We develop a fractional dynamical networks-based machine learning (FDNML) framework using coupled fractional-order differential equations to capture brain signal interdependencies and detect cognitive fatigue transitions. Multifractal properties of brain activity exhibit distinct generalized fractal dimension signatures across fatigue levels, with Wasserstein distances of 0.10, 0.13, and 0.08 between states 0-1, 1-2, and 0-2, respectively. The framework achieves 93.33% classification accuracy and 95% AUROC enabling prevention of performance degradation through early detection of neural state transitions.