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

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

doi:10.1109/IWW-BCI.2015.7073027

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 proceeding › Conference contribution

2 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 language	English
Title of host publication	3rd International Winter Conference on Brain-Computer Interface, BCI 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Print)	9781479974948
DOIs	https://doi.org/10.1109/IWW-BCI.2015.7073027
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

Other

Other	2015 3rd International Winter Conference on Brain-Computer Interface, BCI 2015
Country/Territory	Korea, Republic of
City	Gangwon-Do
Period	15/1/12 → 15/1/14

Keywords

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

ASJC Scopus subject areas

Human-Computer Interaction
Cognitive Neuroscience
Sensory Systems

Access to Document

10.1109/IWW-BCI.2015.7073027

Cite this

Combined regression and classification approach for prediction of driver's braking intention. / Kim, Jeong Woo; Suk, Heung-Il; Kim, Jong Pil et al.
3rd International Winter Conference on Brain-Computer Interface, BCI 2015. Institute of Electrical and Electronics Engineers Inc., 2015. 7073027.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kim, JW, Suk, H-I, Kim, JP & Lee, SW 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., 2015 3rd International Winter Conference on Brain-Computer Interface, BCI 2015, Gangwon-Do, Korea, Republic of, 15/1/12. https://doi.org/10.1109/IWW-BCI.2015.7073027

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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.",

keywords = "Brain-computer interface (BCI), Classification, Electroencephalography (EEG), Regression model",

author = "Kim, {Jeong Woo} and Heung-Il Suk and Kim, {Jong Pil} and Lee, {Seong Whan}",

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AU - Suk, Heung-Il

AU - Kim, Jong Pil

AU - Lee, Seong Whan

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Y1 - 2015/3/30

N2 - 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.

AB - 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.

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KW - Classification

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BT - 3rd International Winter Conference on Brain-Computer Interface, BCI 2015

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ER -

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

Abstract

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Keywords

ASJC Scopus subject areas

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