A fractional dynamical networks ML framework detects cognitive fatigue transitions from EEG with 93.33% accuracy and 95% AUROC by capturing non-Markovian interdependencies via multifractal signatures.
Decoding fatigue levels of pilots using eeg signals with hybrid deep neural networks
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Non-Markovian Dynamical Systems Modeling of Electroencephalogram-based Brain Activity for Anticipating the Cognitive Fatigue Level
A fractional dynamical networks ML framework detects cognitive fatigue transitions from EEG with 93.33% accuracy and 95% AUROC by capturing non-Markovian interdependencies via multifractal signatures.