ReviewSensors (Basel, Switzerland)2022
Electroencephalogram-Based Approaches for Driver Drowsiness Detection and Management: A Review.
Review in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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Who cites it
14 citing papers in PubMed, 45 citations in OpenAlex.
- A Bio-Inspired Physiological-Behavioral Fusion Method for Human-Factor Risk Assessment of Imported Unmanned Intelligent System Operators in Port-Security Scenarios.Biomimetics (Basel, Switzerland) · 2026Article
- Sensing Technologies and Physiological Parameters for Real-Time Driver Drowsiness Detection: A Comprehensive Review.Sensors (Basel, Switzerland) · 2026Review
- Real-Time Driver Attention Detection in Complex Driving Environments via Binocular Depth Compensation and Multi-Source Temporal Bidirectional Long Short-Term Memory Network.Sensors (Basel, Switzerland) · 2025Article
- Capacitance-Based Untethered Fatigue Driving Recognition Under Various Light Conditions.Sensors (Basel, Switzerland) · 2024Article
- Efficient Generalized Electroencephalography-Based Drowsiness Detection Approach with Minimal Electrodes.Sensors (Basel, Switzerland) · 2024Article
- Contactless Heart and Respiration Rates Estimation and Classification of Driver Physiological States Using CW Radar and Temporal Neural Networks.Sensors (Basel, Switzerland) · 2023Article
- Optimizing Road Safety: Advancements in Lightweight YOLOv8 Models and GhostC2f Design for Real-Time Distracted Driving Detection.Sensors (Basel, Switzerland) · 2023Article
- EEG and ECG-Based Multi-Sensor Fusion Computing for Real-Time Fatigue Driving Recognition Based on Feedback Mechanism.Sensors (Basel, Switzerland) · 2023Article
- A Neuroergonomic Approach Fostered by Wearable EEG for the Multimodal Assessment of Drivers Trainees.Sensors (Basel, Switzerland) · 2023Article
- An event-based magnetoencephalography study of simulated driving: Establishing a novel paradigm to probe the dynamic interplay of executive and motor function.Human brain mapping · 2023Article
- Designing an Embedded Feature Selection Algorithm for a Drowsiness Detector Model Based on Electroencephalogram Data.Sensors (Basel, Switzerland) · 2023Article
- Driving drowsiness detection using spectral signatures of EEG-based neurophysiology.Frontiers in physiology · 2023Article
- Attention Detection by Heartbeat and Respiratory Features from Radio-Frequency Sensor.Sensors (Basel, Switzerland) · 2022Article
- Evaluation of a New Lightweight EEG Technology for Translational Applications of Passive Brain-Computer Interfaces.Frontiers in human neuroscience · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drowsiness is not only a core challenge to safe driving in traditional driving conditions but also a serious obstacle for the wide acceptance of added services of self-driving cars (because drowsiness is, in fact, one of the most representative early-stage symptoms of self-driving carsickness). In view of the importance of detecting drivers' drowsiness, this paper reviews the algorithms of electroencephalogram (EEG)-based drivers' drowsiness detection (DDD). To facilitate the review, the EEG-based DDD approaches are organized into a tree structure taxonomy, having two main categories, namely "detection only (open-loop)" and "management (closed-loop)", both aimed at designing better DDD systems that ensure early detection, reliability and practical utility. To achieve this goal, we addressed seven questions, the answers of which helped in developing an EEG-based DDD system that is superior to the existing ones. A basic assumption in this review article is that although driver drowsiness and carsickness-induced drowsiness are caused by different factors, the brain network that regulates drowsiness is the same.
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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.