ArticleFrontiers in neurorobotics2021
EEG-Based Driving Fatigue Detection Using a Two-Level Learning Hierarchy Radial Basis Function.
Article in Frontiers in neurorobotics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 63 citations in OpenAlex.
- Machine Learning-Based Fatigue Detection from EEG Signals: A Methodological Pilot Framework with Potential for Nursing Applications.Journal of clinical practice and research · 2026Article
- Simultaneous detection of physical and mental fatigue using limited-channel EEG for practical workplace monitoring.Medical & biological engineering & computing · 2026Article
- Driver Fatigue Detection using EEG Microstate Features and Support Vector Machines.Journal of biomedical physics & engineering · 2025Article
- Smart IoT-driven biosensors for EEG-based driving fatigue detection: A CNN-XGBoost model enhancing healthcare quality.BioImpacts : BI · 2025Article
- Ultra-Wide Band Radar Empowered Driver Drowsiness Detection with Convolutional Spatial Feature Engineering and Artificial Intelligence.Sensors (Basel, Switzerland) · 2024Article
- Driving fatigue detection based on brain source activity and ARMA model.Medical & biological engineering & computing · 2024Article
- Relationships between subjective experience, electroencephalogram, and heart rate variability during a series of cosmetic behavior.Frontiers in psychology · 2024Article
- EEG and ECG-Based Multi-Sensor Fusion Computing for Real-Time Fatigue Driving Recognition Based on Feedback Mechanism.Sensors (Basel, Switzerland) · 2023Article
- Machine learning polysomnographically-derived electroencephalography biomarkers predictive of epworth sleepiness scale.Scientific reports · 2023Article
- Computer vision-based approach to detect fatigue driving and face mask for edge computing device.Heliyon · 2022Article
- Concurrent fNIRS and EEG for Brain Function Investigation: A Systematic, Methodology-Focused Review.Sensors (Basel, Switzerland) · 2022Review
- Diabetic Retinopathy Detection from Fundus Images of the Eye Using Hybrid Deep Learning Features.Diagnostics (Basel, Switzerland) · 2022Article
- Dissecting the fatigue experience: A scoping review of fatigue definitions, dimensions, and measures in non-oncologic medical conditions.Brain, behavior, & immunity - health · 2021Article
- Study on the Effect of Driving Time on Fatigue of Grassland Road Based on EEG.Journal of healthcare engineering · 2021Article
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Authors and funding
8 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Electroencephalography (EEG)-based driving fatigue detection has gained increasing attention recently due to the non-invasive, low-cost, and potable nature of the EEG technology, but it is still challenging to extract informative features from noisy EEG signals for driving fatigue detection. Radial basis function (RBF) neural network has drawn lots of attention as a promising classifier due to its linear-in-the-parameters network structure, strong non-linear approximation ability, and desired generalization property. The RBF network performance heavily relies on network parameters such as the number of the hidden nodes, number of the center vectors, width, and output weights. However, global optimization methods that directly optimize all the network parameters often result in high evaluation cost and slow convergence. To enhance the accuracy and efficiency of EEG-based driving fatigue detection model, this study aims to develop a two-level learning hierarchy RBF network (RBF-TLLH) which allows for global optimization of the key network parameters. Experimental EEG data were collected, at both fatigue and alert states, from six healthy participants in a simulated driving environment. Principal component analysis was first utilized to extract features from EEG signals, and the proposed RBF-TLLH was then employed for driving status (fatigue
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.