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A Complex Network-Based Broad Learning System for Detecting Driver Fatigue From EEG Signals
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What points at this work Incoming claims: where this work appears in other papers’ reference lists and arguments.
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inferred ← cites https://doi.org/10.1016/j.jtte.2023.07.004 openalex-snapshot Bibliometric study and critical individual literature review of driving behavior analysis methods based on brain imaging from 1993 to 2022
inferred ← cites https://doi.org/10.1063/5.0133092 openalex-snapshot A regression model combined convolutional neural network and recurrent neural network for electroencephalogram-based cross-subject fatigue detection
inferred ← cites https://doi.org/10.1109/tits.2026.3660698 openalex-snapshot Intelligent Driver Drowsiness Detection Using Brain Imaging: A Systematic Review for Enhanced Road Safety in Intelligent Transportation Systems
inferred ← cites https://doi.org/10.1109/tsmc.2021.3113823 openalex-snapshot EEG-Based Drowsiness Detection With Fuzzy Independent Phase-Locking Value Representations Using Lagrangian-Based Deep Neural Networks
inferred ← cites https://doi.org/10.1109/tsmc.2023.3282635 openalex-snapshot Autonomous System for EEG-Based Multiple Abnormal Mental States Classification Using Hybrid Deep Neural Networks Under Flight Environment
inferred ← cites https://doi.org/10.3390/bs13100788 openalex-snapshot Psychophysiology of Monotonous Driving, Fatigue and Sleepiness in Train and Non-Professional Drivers: Driver Safety Implications
inferred ← cites https://doi.org/10.3390/electronics13071374 openalex-snapshot Development of an Integrated Longitudinal Control Algorithm for Autonomous Mobility with EEG-Based Driver Status Classification and Safety Index
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