Combined regression and classification approach for prediction of driver's braking intention

Jeong Woo Kim, Heung-Il Suk, Jong Pil Kim, Seong Whan Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publication3rd International Winter Conference on Brain-Computer Interface, BCI 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781479974948
DOIs
Publication statusPublished - 2015 Mar 30
Event2015 3rd International Winter Conference on Brain-Computer Interface, BCI 2015 - Gangwon-Do, Korea, Republic of
Duration: 2015 Jan 122015 Jan 14

Other

Other2015 3rd International Winter Conference on Brain-Computer Interface, BCI 2015
CountryKorea, Republic of
CityGangwon-Do
Period15/1/1215/1/14

    Fingerprint

Keywords

  • Brain-computer interface (BCI)
  • Classification
  • Electroencephalography (EEG)
  • Regression model

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Cognitive Neuroscience
  • Sensory Systems

Cite this

Kim, J. W., Suk, H-I., Kim, J. P., & Lee, S. W. (2015). Combined regression and classification approach for prediction of driver's braking intention. In 3rd International Winter Conference on Brain-Computer Interface, BCI 2015 [7073027] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IWW-BCI.2015.7073027