@inproceedings{619f3d58337c430583e89494d123522e,
title = "An EEG-based multi-classification method of braking intentions for driver-vehicle interaction",
abstract = "This paper proposes an electroencephalography (EEG)-based classification method to distinguish emergency and soft braking intentions from normal driving intentions. Time-frequency analysis of EEG signals shows that there exist differences between emergency and soft braking intentions. Power spectral density (PSD) values are used as features. Three Support Vector Machine (SVM)-based binary classifiers are developed to recognize three kinds of driving intentions. Results show that the average recognition accuracy of three classes is over 74%, which shows the feasibility of the proposed method. This study has important values in the exploration of neural signatures of different driving intentions and developing assistant driving systems based on the proposed braking intention detection method.",
keywords = "Braking intention, EEG, Three-class classification",
author = "Huikang Wang and Luzheng Bi and Weijie Fei and Ling Wang",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2019 ; Conference date: 04-08-2019 Through 09-08-2019",
year = "2019",
month = aug,
doi = "10.1109/RCAR47638.2019.9044151",
language = "English",
series = "2019 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "438--441",
booktitle = "2019 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2019",
address = "United States",
}