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Detection of Driver Braking Intention Using EEG Signals During Simulated Driving
- Source :
- Sensors (Basel, Switzerland), Sensors, Vol 19, Iss 13, p 2863 (2019), Sensors, Volume 19, Issue 13
- Publication Year :
- 2019
-
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.
- Subjects :
- Automobile Driving
Computer science
0206 medical engineering
02 engineering and technology
Intention
Electroencephalography
lcsh:Chemical technology
Biochemistry
Article
Analytical Chemistry
03 medical and health sciences
User-Computer Interface
0302 clinical medicine
Cognition
medicine
Humans
brain-controlled vehicle
lcsh:TP1-1185
Electrical and Electronic Engineering
Instrumentation
medicine.diagnostic_test
business.industry
electroencephalogram (EEG)
Accidents, Traffic
Brain
Pattern recognition
020601 biomedical engineering
Atomic and Molecular Physics, and Optics
brain–computer interface (BCI)
ComputingMethodologies_PATTERNRECOGNITION
Autoregressive model
emergency braking intention
Artificial intelligence
Neural Networks, Computer
business
030217 neurology & neurosurgery
Algorithms
Subjects
Details
- ISSN :
- 14248220
- Volume :
- 19
- Issue :
- 13
- Database :
- OpenAIRE
- Journal :
- Sensors (Basel, Switzerland)
- Accession number :
- edsair.doi.dedup.....e6cb7d302f49406e81de1c47260ba157