Abstract
Recent studies for driving assistant system have been concerned with driver's convenience and safety. Especially, neurophysiological studies were employed to develop the novel driving assistant technologies for driver's safety. These studies verified that neurophysiological characteristics could be used for detection of emergency situations during simulated driving. However, it is impossible to control the vehicle spontaneously using previous approach. In this article, the method for decoding of driver's braking intention spontaneously is proposed to predict the amount of braking continuously based on analysis of neural correlates. The prediction results based on Kernel Ridge Regression (KRR), linear regression, and combined linear regression and classification approaches are compared and evaluated by the normalized root-mean square error (NRMSE) and one-way ANOVA for statistical test.
Original language | English |
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Title of host publication | 3rd International Winter Conference on Brain-Computer Interface, BCI 2015 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781479974948 |
DOIs | |
Publication status | Published - 2015 Mar 30 |
Event | 2015 3rd International Winter Conference on Brain-Computer Interface, BCI 2015 - Gangwon-Do, Korea, Republic of Duration: 2015 Jan 12 → 2015 Jan 14 |
Publication series
Name | 3rd International Winter Conference on Brain-Computer Interface, BCI 2015 |
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Other
Other | 2015 3rd International Winter Conference on Brain-Computer Interface, BCI 2015 |
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Country/Territory | Korea, Republic of |
City | Gangwon-Do |
Period | 15/1/12 → 15/1/14 |
Bibliographical note
Publisher Copyright:© 2015 IEEE.
Keywords
- Brain-computer interface (BCI)
- Classification
- Electroencephalography (EEG)
- Regression model
ASJC Scopus subject areas
- Human-Computer Interaction
- Cognitive Neuroscience
- Sensory Systems