ArticleCognitive neurodynamics2018
Automated detection of driver fatigue based on EEG signals using gradient boosting decision tree model.
Article in Cognitive neurodynamics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Human-inspired time-series health evaluation with an adaptive multimodal electronic skin.Science advances · 2026Article
- Application of Filter Bank to Improve Fatigue Monitoring in Wearable EEG-Based Brain-Computer Interface.NeuroSci · 2026Article
- Divergent eye-brain-heart fatigue signatures during sustained simulated agricultural operations.Frontiers in physiology · 2026Article
- Evaluation of entropy features and classifier performance in person authentication using resting-state EEG.Frontiers in neuroscience · 2025Article
- Driving fatigue detection based on brain source activity and ARMA model.Medical & biological engineering & computing · 2024Article
- Subject-Independent Emotion Recognition Based on EEG Frequency Band Features and Self-Adaptive Graph Construction.Brain sciences · 2024Article
- ID3RSNet: cross-subject driver drowsiness detection from raw single-channel EEG with an interpretable residual shrinkage network.Frontiers in neuroscience · 2024Article
- A Novel Epilepsy Detection Method Based on Feature Extraction by Deep Autoencoder on EEG Signal.International journal of environmental research and public health · 2022Article
- Automatic Recognition of High-Density Epileptic EEG Using Support Vector Machine and Gradient-Boosting Decision Tree.Brain sciences · 2022Article
- Multi-Level Classification of Driver Drowsiness by Simultaneous Analysis of ECG and Respiration Signals Using Deep Neural Networks.International journal of environmental research and public health · 2022Article
- Pilot Behavior Recognition Based on Multi-Modality Fusion Technology Using Physiological Characteristics.Biosensors · 2022Article
- Applications of Machine Learning in Alloy Catalysts: Rational Selection and Future Development of Descriptors.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Review
- A comprehensive review of approaches to detect fatigue using machine learning techniques.Chronic diseases and translational medicine · 2022Review
- Alzheimer-Compound Identification Based on Data Fusion and forgeNet_SVM.Frontiers in aging neuroscience · 2022Article
- A Hybrid Approach for MS Diagnosis Through Nonlinear EEG Descriptors and Metaheuristic Optimized Classification Learning.Computational intelligence and neuroscience · 2022Article
- An Evaluation of the EEG Alpha-to-Theta and Theta-to-Alpha Band Ratios as Indexes of Mental Workload.Frontiers in neuroinformatics · 2022Article
- Analysis of the Relationship Between Motor Imagery and Age-Related Fatigue for CNN Classification of the EEG Data.Frontiers in aging neuroscience · 2022Article
- EEG based cognitive task classification using multifractal detrended fluctuation analysis.Cognitive neurodynamics · 2021Article
- A Review of EEG Signal Features and their Application in Driver Drowsiness Detection Systems.Sensors (Basel, Switzerland) · 2021Review
- Research on Channel Selection and Multi-Feature Fusion of EEG Signals for Mental Fatigue Detection.Entropy (Basel, Switzerland) · 2021Article
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Authors and funding
2 authors.
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Abstract
Driver fatigue is increasingly a contributing factor for traffic accidents, so an effective method to automatically detect driver fatigue is urgently needed. In this study, in order to catch the main characteristics of the EEG signals, four types of entropies (based on the EEG signal of a single channel) were calculated as the feature sets, including sample entropy, fuzzy entropy, approximate entropy and spectral entropy. All feature sets were used as the input of a gradient boosting decision tree (GBDT), a fast and highly accurate boosting ensemble method. The output of GBDT determined whether a driver was in a fatigue state or not based on their EEG signals. Three state-of-the-art classifiers, k-nearest neighbor, support vector machine and neural network were also employed. To assess our method, several experiments including parameter setting and classification performance comparison were performed on 22 subjects. The results indicated that it is possible to use only one EEG channel to detect a driver fatigue state. The average highest recognition rate in this work was up to 94.0%, which could meet the needs of daily applications. Our GBDT-based method may assist in the detection of driver fatigue.
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