Non-Markovian Dynamical Systems Modeling of Electroencephalogram-based Brain Activity for Anticipating the Cognitive Fatigue Level

2026-05-01

AI Summary

Existing methods for assessing cognitive fatigue struggle to capture the non-Markovian nature and time-varying dependencies of electroencephalographic (EEG) signals, limiting real-time detection of neural state transitions. This work proposes a novel dynamical network framework that integrates fractional-order differential equations, multifractal analysis, and Wasserstein distance, uniquely combining fractional-order dynamical systems with multifractal theory to accurately model the non-Markovian dynamics of EEG and uncover distinctive evolutionary patterns of generalized fractal dimensions during fatigue progression. The approach achieves 93.33% accuracy and 95% AUROC in fatigue state classification and effectively discriminates between distinct neural states using Wasserstein distances ranging from 0.08 to 0.13, enabling high-precision, real-time identification of dynamic transitions in cognitive fatigue.

Abstract

Cognitive fatigue, which 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 real-time 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 in real-time. 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 the prevention of performance degradation through early detection of neural state transitions.

Problem

• cognitive fatigue
• non-Markovian
• electroencephalogram
• phase transition
• time-varying interdependencies

Innovation

• fractional dynamical networks
• non-Markovian dynamics
• cognitive fatigue detection
• multifractal analysis
• EEG-based brain modeling