ArticleFrontiers in neuroinformatics2018
EEG-Based Detection of Braking Intention Under Different Car Driving Conditions.
Article in Frontiers in neuroinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 75 citations in OpenAlex.
- Human-Centric Spatial Cognition Detecting System Based on Drivers' Electroencephalogram Signals for Autonomous Driving.Sensors (Basel, Switzerland) · 2025Article
- Time-variant Granger causality analysis for intuitive perception collision risk in driving scenario: an EEG study.Frontiers in neuroscience · 2025Article
- Convolutional spiking neural networks for intent detection based on anticipatory brain potentials using electroencephalogram.Scientific reports · 2024Article
- Electric Bus Pedal Misapplication Detection Based on Phase Space Reconstruction Method.Sensors (Basel, Switzerland) · 2023Article
- EEG-based emergency braking intention detection during simulated driving.Biomedical engineering online · 2023Article
- Emergency Braking Evoked Brain Activities during Distracted Driving.Sensors (Basel, Switzerland) · 2022Article
- EEG-EMG coupling as a hybrid method for steering detection in car driving settings.Cognitive neurodynamics · 2022Article
- Dynamic causal modeling of evoked responses during emergency braking: an ERP study.Cognitive neurodynamics · 2022Article
- Evaluation of a New Lightweight EEG Technology for Translational Applications of Passive Brain-Computer Interfaces.Frontiers in human neuroscience · 2022Article
- Reduction of Artifacts in Capacitive Electrocardiogram Signals of Driving Subjects.Entropy (Basel, Switzerland) · 2021Article
- Driver Fatigue Detection Systems Using Multi-Sensors, Smartphone, and Cloud-Based Computing Platforms: A Comparative Analysis.Sensors (Basel, Switzerland) · 2020Review
- Detection of Driver Braking Intention Using EEG Signals During Simulated Driving.Sensors (Basel, Switzerland) · 2019Article
- Electron microscopy demonstrating noise exposure alters synaptic vesicle size in the inferior colliculus of cat.Noise & healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The anticipatory recognition of braking is essential to prevent traffic accidents. For instance, driving assistance systems can be useful to properly respond to emergency braking situations. Moreover, the response time to emergency braking situations can be affected and even increased by different driver's cognitive states caused by stress, fatigue, and extra workload. This work investigates the detection of emergency braking from driver's electroencephalographic (EEG) signals that precede the brake pedal actuation. Bioelectrical signals were recorded while participants were driving in a car simulator while avoiding potential collisions by performing emergency braking. In addition, participants were subjected to stress, workload, and fatigue. EEG signals were classified using support vector machines (SVM) and convolutional neural networks (CNN) in order to discriminate between braking intention and normal driving. Results showed significant recognition of emergency braking intention which was on average 71.1% for SVM and 71.8% CNN. In addition, the classification accuracy for the best participant was 80.1 and 88.1% for SVM and CNN, respectively. These results show the feasibility of incorporating recognizable driver's bioelectrical responses into advanced driver-assistance systems to carry out early detection of emergency braking situations which could be useful to reduce car accidents.
Indexed as
Identifiers
What OpenQuestion holds
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.