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

TL;DR

This paper introduces a fractional dynamical networks-based machine learning framework that models non-Markovian brain activity to detect cognitive fatigue in real-time, improving accuracy over existing methods.

Contribution

It develops a novel fractional-order differential equations approach to capture brain signal interdependencies and detect fatigue transitions with high accuracy.

Findings

Achieved 93.33% classification accuracy in fatigue detection.

Identified distinct multifractal signatures across fatigue levels.

Demonstrated early neural state transition detection to prevent performance decline.

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.