ArticleSensors (Basel, Switzerland)2019
Detection of Driver Braking Intention Using EEG Signals During Simulated Driving.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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Who cites it
11 citing papers in PubMed.
- 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
- 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
- Reduction of Artifacts in Capacitive Electrocardiogram Signals of Driving Subjects.Entropy (Basel, Switzerland) · 2021Article
- Robust anticipation of continuous steering actions from electroencephalographic data during simulated driving.Scientific reports · 2021Article
- Emergency Braking Intention Detect System Based on K-Order Propagation Number Algorithm: A Network Perspective.Brain sciences · 2021Article
- The Effects of Driving Experience on the P300 Event-Related Potential during the Perception of Traffic Scenes.International journal of environmental research and public health · 2021Article
- Wireless Sensors System for Stress Detection by Means of ECG and EDA Acquisition.Sensors (Basel, Switzerland) · 2020Article
- Detection of Participation and Training Task Difficulty Applied to the Multi-Sensor Systems of Rehabilitation Robots.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
In this work, we developed a novel system to detect the braking intention of drivers in emergency situations using electroencephalogram (EEG) signals. The system acquired eight-channel EEG and motion-sensing data from a custom-designed EEG headset during simulated driving. A novel method for accurately labeling the training data during an extremely short period after the onset of an emergency stimulus was introduced. Two types of features, including EEG band power-based and autoregressive (AR)-based, were investigated. It turned out that the AR-based feature in combination with artificial neural network classifier provided better detection accuracy of the system. Experimental results for ten subjects indicated that the proposed system could detect the emergency braking intention approximately 600 ms before the onset of the executed braking event, with high accuracy of 91%. Thus, the proposed system demonstrated the feasibility of developing a brain-controlled vehicle for real-world applications.
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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.