Detection of Driver Braking Intention Using EEG Signals During Simulated Driving
In this work, the authors 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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- Record URL:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/14248220
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Supplemental Notes:
- © 2019 Trung-Hau Nguyen and Wan-Young Chung.
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Authors:
- Nguyen, Trung-Hau
- Chung, Wan-Young
- Publication Date: 2019-7
Language
- English
Media Info
- Media Type: Web
- Features: Figures; References; Tables;
- Pagination: 2863
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Serial:
- Sensors
- Volume: 19
- Issue Number: 13
- Publisher: MDPI AG
- ISSN: 1424-8220
- Serial URL: http://www.mdpi.com/journal/sensors
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Publication flags:
Open Access (libre)
Subject/Index Terms
- TRT Terms: Behavior; Braking; Detection and identification technologies; Drivers; Driving simulators; Electroencephalography
- Subject Areas: Highways; Safety and Human Factors; Vehicles and Equipment;
Filing Info
- Accession Number: 01716570
- Record Type: Publication
- Files: TRIS
- Created Date: Sep 16 2019 5:19PM