ArticleNeural computing & applications2021
Vehicle driver drowsiness detection method using wearable EEG based on convolution neural network.
Article in Neural computing & applications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 80 citations in OpenAlex.
- Design of an Adaptive Model for Intelligent Car Seats Based on Human Pose Recognition.Annals of the New York Academy of Sciences · 2026Article
- Sensing Technologies and Physiological Parameters for Real-Time Driver Drowsiness Detection: A Comprehensive Review.Sensors (Basel, Switzerland) · 2026Review
- Lightweight and Real-Time Driver Fatigue Detection Based on MG-YOLOv8 with Facial Multi-Feature Fusion.Journal of imaging · 2025Article
- Recent Advances in Portable Dry Electrode EEG: Architecture and Applications in Brain-Computer Interfaces.Sensors (Basel, Switzerland) · 2025Review
- A Novel Neural Network Model Based on Real Mountain Road Data for Driver Fatigue Detection.Biomimetics (Basel, Switzerland) · 2025Article
- Open-Source Algorithm for Automated Vigilance State Classification Using Single-Channel Electroencephalogram in Rodents.Sensors (Basel, Switzerland) · 2025Article
- [An emerging discipline: brain-computer interfaces medicine].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Review
- Wireless ear EEG to monitor drowsiness.Nature communications · 2024Article
- In-Car Environment Control Using an SSVEP-Based Brain-Computer Interface with Visual Stimuli Presented on Head-Up Display: Performance Comparison with a Button-Press Interface.Sensors (Basel, Switzerland) · 2024Article
- Remote Photoplethysmography and Motion Tracking Convolutional Neural Network with Bidirectional Long Short-Term Memory: Non-Invasive Fatigue Detection Method Based on Multi-Modal Fusion.Sensors (Basel, Switzerland) · 2024Article
- Biomedical Big Data Technologies, Applications, and Challenges for Precision Medicine: A Review.Global challenges (Hoboken, NJ) · 2024Review
- A systematic review of physiological signals based driver drowsiness detection systems.Cognitive neurodynamics · 2023Review
- EEG-based analysis for pilots' at-risk cognitive competency identification using RF-CNN algorithm.Frontiers in neuroscience · 2023Article
- Characterization of Retronasal Airflow Patterns during Intraoral Fluid Discrimination Using a Low-Cost, Open-Source Biosensing Platform.Sensors (Basel, Switzerland) · 2022Article
- Driver drowsiness estimation using EEG signals with a dynamical encoder-decoder modeling framework.Scientific reports · 2022Article
- Classifying Driving Fatigue by Using EEG Signals.Computational intelligence and neuroscience · 2022Article
- Advanced e-Call Support Based on Non-Intrusive Driver Condition Monitoring for Connected and Autonomous Vehicles.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Vehicle drivers driving cars under the situation of drowsiness can cause serious traffic accidents. In this paper, a vehicle driver drowsiness detection method using wearable electroencephalographic (EEG) based on convolution neural network (CNN) is proposed. The presented method consists of three parts: data collection using wearable EEG, vehicle driver drowsiness detection and the early warning strategy. Firstly, a wearable brain computer interface (BCI) is used to monitor and collect the EEG signals in the simulation environment of drowsy driving and awake driving. Secondly, the neural networks with Inception module and modified AlexNet module are trained to classify the EEG signals. Finally, the early warning strategy module will function and it will sound an alarm if the vehicle driver is judged as drowsy. The method was tested on driving EEG data from simulated drowsy driving. The results show that using neural network with Inception module reached 95.59% classification accuracy based on one second time window samples and using modified AlexNet module reached 94.68%. The simulation and test results demonstrate the feasibility of the proposed drowsiness detection method for vehicle driving safety.
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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.