ArticleSensors (Basel, Switzerland)2017
Design of a Fatigue Detection System for High-Speed Trains Based on Driver Vigilance Using a Wireless Wearable EEG.
Article in Sensors (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 2 of them syntheses that pooled it.
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Who cites it
32 citing papers in PubMed, 2 syntheses or guidelines pooled it, 158 citations in OpenAlex.
- Fatigue Monitoring Through Wearables: A State-of-the-Art Review.Frontiers in physiology · 2021Pooled it
- A Systemic Review of Available Low-Cost EEG Headsets Used for Drowsiness Detection.Frontiers in neuroinformatics · 2020Pooled it
- System Integration and Participant-Level Validation of a Gate-Based Contactless Multimodal System for Railway Pre-Duty Screening.Sensors (Basel, Switzerland) · 2026Article
- Sensing Technologies and Physiological Parameters for Real-Time Driver Drowsiness Detection: A Comprehensive Review.Sensors (Basel, Switzerland) · 2026Review
- A Facial-Expression-Aware Edge AI System for Driver Safety Monitoring.Sensors (Basel, Switzerland) · 2025Article
- Impact of visual preferences for indoor green walls on emotion and brain activity of college students.Scientific reports · 2025Article
- The impact of virtual images of coastal landscape features on stress recovery based on EEG.Scientific reports · 2025Article
- Neuroergonomic Attention Assessment in Safety-Critical Tasks: EEG Indices and Subjective Metrics Validation in a Novel Task-Embedded Reaction Time Paradigm.Brain sciences · 2024Article
- Neuroergonomics on the Go: An Evaluation of the Potential of Mobile EEG for Workplace Assessment and Design.Human factors · 2023Article
- Eyes on the road: brain computer interfaces and cognitive distraction in traffic.Frontiers in neuroergonomics · 2023Article
- Reorganization of Brain Functional Network during Task Switching before and after Mental Fatigue.Sensors (Basel, Switzerland) · 2022Article
- Improved Cognitive Vigilance Assessment after Artifact Reduction with Wavelet Independent Component Analysis.Sensors (Basel, Switzerland) · 2022Article
- The Application of Electroencephalogram in Driving Safety: Current Status and Future Prospects.Frontiers in psychology · 2022Review
- Evaluation of a Fatigue Detector Using Eye Closure-Associated Indicators Acquired from Truck Drivers in a Simulator Study.Sensors (Basel, Switzerland) · 2021Article
- Brain-Computer Interface: Advancement and Challenges.Sensors (Basel, Switzerland) · 2021Review
- Evaluating deep learning EEG-based mental stress classification in adolescents with autism for breathing entrainment BCI.Brain informatics · 2021Article
- An Adaptive EEG Feature Extraction Method Based on Stacked Denoising Autoencoder for Mental Fatigue Connectivity.Neural plasticity · 2021Article
- Vehicle driver drowsiness detection method using wearable EEG based on convolution neural network.Neural computing & applications · 2021Article
- Toward EEG-Based BCI Applications for Industry 4.0: Challenges and Possible Applications.Frontiers in human neuroscience · 2021Review
- Real-Time System for Driver Fatigue Detection Based on a Recurrent Neuronal Network.Journal of imaging · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The vigilance of the driver is important for railway safety, despite not being included in the safety management system (SMS) for high-speed train safety. In this paper, a novel fatigue detection system for high-speed train safety based on monitoring train driver vigilance using a wireless wearable electroencephalograph (EEG) is presented. This system is designed to detect whether the driver is drowsiness. The proposed system consists of three main parts: (1) a wireless wearable EEG collection; (2) train driver vigilance detection; and (3) early warning device for train driver. In the first part, an 8-channel wireless wearable brain-computer interface (BCI) device acquires the locomotive driver's brain EEG signal comfortably under high-speed train-driving conditions. The recorded data are transmitted to a personal computer (PC) via Bluetooth. In the second step, a support vector machine (SVM) classification algorithm is implemented to determine the vigilance level using the Fast Fourier transform (FFT) to extract the EEG power spectrum density (PSD). In addition, an early warning device begins to work if fatigue is detected. The simulation and test results demonstrate the feasibility of the proposed fatigue detection system for high-speed train 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.