Non-Markovian Dynamical Systems Modeling of Electroencephalogram-based Brain Activity for Anticipating the Cognitive Fatigue Level
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arXiv
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| Format: | Preprint |
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2026
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| author | Saghi, Zeinabsadat Riabukhina, Daria Akinbami, Olubukola Bogdan, Paul Chattopadhyay, Souti |
| author_facet | Saghi, Zeinabsadat Riabukhina, Daria Akinbami, Olubukola Bogdan, Paul Chattopadhyay, Souti |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_01043 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Non-Markovian Dynamical Systems Modeling of Electroencephalogram-based Brain Activity for Anticipating the Cognitive Fatigue Level Saghi, Zeinabsadat Riabukhina, Daria Akinbami, Olubukola Bogdan, Paul Chattopadhyay, Souti Human-Computer Interaction 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. |
| title | Non-Markovian Dynamical Systems Modeling of Electroencephalogram-based Brain Activity for Anticipating the Cognitive Fatigue Level |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2605.01043 |