Abstract
Recently neurophysiological studies have been concerned with using brain signals for driving assistance technologies. These studies verified that neurophysiological characteristics could be used for detection of emergency situations during simulated driving. However, it is hard to develop the braking assistant system which could control the vehicle continuously using this approach. In this article, the method for decoding of driver's braking intention based on analysis of neural correlates is proposed to control the braking of vehicle continuously. The participants' braking intention is decoded by kernel ridge regression (KRR) model to overcome the limitation of classification approach. In addition, the combination of three different features is employed to enhance the decoding performance. The decoding performances are evaluated by the correlation coefficient (r-value) and the normalized root-mean square error (NRMSE).
| Original language | English |
|---|---|
| Article number | 6974583 |
| Pages (from-to) | 4129-4132 |
| Number of pages | 4 |
| Journal | Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics |
| Volume | 2014-January |
| Issue number | January |
| DOIs | |
| Publication status | Published - 2014 |
| Event | 2014 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2014 - San Diego, United States Duration: 2014 Oct 5 → 2014 Oct 8 |
Bibliographical note
Publisher Copyright:© 2014 IEEE.
Keywords
- Brain-computer interface (BCI)
- Electroencephalography (EEG)
- Kernel ridge regression model (KRR)
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Control and Systems Engineering
- Human-Computer Interaction