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

    3 Citations (Scopus)

    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 (Electronic)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

    Publication series

    Name3rd International Winter Conference on Brain-Computer Interface, BCI 2015

    Other

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

    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

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