ArticleComputational intelligence and neuroscience2019
Driving Fatigue Detection from EEG Using a Modified PCANet Method.
Article in Computational intelligence and neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Types, Risk Factors, Consequences, and Detection Methods of Train Driver Fatigue and Distraction.Computational intelligence and neuroscience · 2022Pooled it
- A Comprehensive Review of Unobtrusive Biosensing in Intelligent Vehicles: Sensors, Algorithms, and Integration Challenges.Bioengineering (Basel, Switzerland) · 2025Review
- Smart IoT-driven biosensors for EEG-based driving fatigue detection: A CNN-XGBoost model enhancing healthcare quality.BioImpacts : BI · 2025Article
- Reduction of cognitive fatigue and improved performance at a VR-based driving simulator using tRNS.iScience · 2024Article
- A Hybrid EEG-Based Stress State Classification Model Using Multi-Domain Transfer Entropy and PCANet.Brain sciences · 2024Article
- Driving fatigue detection based on brain source activity and ARMA model.Medical & biological engineering & computing · 2024Article
- EEG-based driving intuition and collision anticipation using joint temporal-frequency multi-layer dynamic brain network.Frontiers in neuroscience · 2024Article
- Study on the Effect of Judgment Excitation Mode to Relieve Driving Fatigue Based on MF-DFA.Brain sciences · 2022Article
- Concurrent fNIRS and EEG for Brain Function Investigation: A Systematic, Methodology-Focused Review.Sensors (Basel, Switzerland) · 2022Review
- A Novel Prototype for Safe Driving Using Embedded Smart Box System.Sensors (Basel, Switzerland) · 2022Article
- A real-time driver fatigue identification method based on GA-GRNN.Frontiers in public health · 2022Article
- Assessment of Combination of Automated Pupillometry and Heart Rate Variability to Detect Driving Fatigue.Frontiers in public health · 2022Article
- Feasibility study of personalized speed adaptation method based on mental state for teleoperated robots.Frontiers in neuroscience · 2022Article
- Advanced e-Call Support Based on Non-Intrusive Driver Condition Monitoring for Connected and Autonomous Vehicles.Sensors (Basel, Switzerland) · 2021Article
- A dynamic center and multi threshold point based stable feature extraction network for driver fatigue detection utilizing EEG signals.Cognitive neurodynamics · 2021Article
- Convolutional Neural Network for Drowsiness Detection Using EEG Signals.Sensors (Basel, Switzerland) · 2021Article
- Multi-Parameter Physiological State Monitoring in Target Detection Under Real-World Settings.Frontiers in human neuroscience · 2021Article
- EEG-Based Driving Fatigue Detection Using a Two-Level Learning Hierarchy Radial Basis Function.Frontiers in neurorobotics · 2021Article
- Deep Learning in Physiological Signal Data: A Survey.Sensors (Basel, Switzerland) · 2020Review
- Classification of Drowsiness Levels Based on a Deep Spatio-Temporal Convolutional Bidirectional LSTM Network Using Electroencephalography Signals.Brain sciences · 2019Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rapid development of the automotive industry has brought great convenience to our life, which also leads to a dramatic increase in the amount of traffic accidents. A large proportion of traffic accidents were caused by driving fatigue. EEG is considered as a direct, effective, and promising modality to detect driving fatigue. In this study, we presented a novel feature extraction strategy based on a deep learning model to achieve high classification accuracy and efficiency in using EEG for driving fatigue detection. EEG signals were recorded from six healthy volunteers in a simulated driving experiment. The feature extraction strategy was developed by integrating the principal component analysis (PCA) and a deep learning model called PCA network (PCANet). In particular, the principal component analysis (PCA) was used to preprocess EEG data to reduce its dimension in order to overcome the limitation of dimension explosion caused by PCANet, making this approach feasible for EEG-based driving fatigue detection. Results demonstrated high and robust performance of the proposed modified PCANet method with classification accuracy up to 95%, which outperformed the conventional feature extraction strategies in the field. We also identified that the parietal and occipital lobes of the brain were strongly associated with driving fatigue. This is the first study, to the best of our knowledge, to practically apply the modified PCANet technique for EEG-based driving fatigue detection.
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