ArticleSensors (Basel, Switzerland)2024
A Novel Approach for Automatic Detection of Driver Fatigue Using EEG Signals Based on Graph Convolutional Networks.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 39 citations in OpenAlex.
- SE-Driven Dynamic Convolution for Adaptive EEG-Based Driver Fatigue Detection Across Spectral, Spatial, and Temporal Domains.Sensors (Basel, Switzerland) · 2026Article
- A fatigue driving detection method based on driver posture and facial state analysis.Scientific reports · 2026Article
- Generative Adversarial Networks for Modeling Bio-Electric Fields in Medicine: A Review of EEG, ECG, EMG, and EOG Applications.Bioengineering (Basel, Switzerland) · 2026Review
- Driver Fatigue Detection using EEG Microstate Features and Support Vector Machines.Journal of biomedical physics & engineering · 2025Article
- ASFT-Transformer: A Fast and Accurate Framework for EEG-Based Pilot Fatigue Recognition.Sensors (Basel, Switzerland) · 2025Article
- Multi-body sensor based drowsiness detection using convolutional programmed transfer VGG-16 neural network with automatic driving mode conversion.Scientific reports · 2025Article
- A Novel Neural Network Model Based on Real Mountain Road Data for Driver Fatigue Detection.Biomimetics (Basel, Switzerland) · 2025Article
- Biomedical Signal Processing and Health Monitoring Based on Sensors.Sensors (Basel, Switzerland) · 2025Article
- Automatic Detection of Acute Leukemia (ALL and AML) Utilizing Customized Deep Graph Convolutional Neural Networks.Bioengineering (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
6 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nowadays, the automatic detection of driver fatigue has become one of the important measures to prevent traffic accidents. For this purpose, a lot of research has been conducted in this field in recent years. However, the diagnosis of fatigue in recent research is binary and has no operational capability. This research presents a multi-class driver fatigue detection system based on electroencephalography (EEG) signals using deep learning networks. In the proposed system, a standard driving simulator has been designed, and a database has been collected based on the recording of EEG signals from 20 participants in five different classes of fatigue. In addition to self-report questionnaires, changes in physiological patterns are used to confirm the various stages of weariness in the suggested model. To pre-process and process the signal, a combination of generative adversarial networks (GAN) and graph convolutional networks (GCN) has been used. The proposed deep model includes five convolutional graph layers, one dense layer, and one fully connected layer. The accuracy obtained for the proposed model is 99%, 97%, 96%, and 91%, respectively, for the four different considered practical cases. The proposed model is compared to one developed through recent methods and research and has a promising performance.
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Registered trials
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.