Decision of braking intensity during simulated driving based on analysis of neural correlates

Jeong Woo Kim, Il Hwa Kim, Seong Whan Lee

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

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 languageEnglish
Title of host publicationConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4129-4132
Number of pages4
Volume2014-January
EditionJanuary
DOIs
Publication statusPublished - 2014
Event2014 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2014 - San Diego, United States
Duration: 2014 Oct 52014 Oct 8

Other

Other2014 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2014
CountryUnited States
CitySan Diego
Period14/10/514/10/8

Fingerprint

Braking
Decoding
Mean square error
Brain

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

Cite this

Kim, J. W., Kim, I. H., & Lee, S. W. (2014). Decision of braking intensity during simulated driving based on analysis of neural correlates. In Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics (January ed., Vol. 2014-January, pp. 4129-4132). [6974583] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SMC.2014.6974583

Decision of braking intensity during simulated driving based on analysis of neural correlates. / Kim, Jeong Woo; Kim, Il Hwa; Lee, Seong Whan.

Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics. Vol. 2014-January January. ed. Institute of Electrical and Electronics Engineers Inc., 2014. p. 4129-4132 6974583.

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

Kim, JW, Kim, IH & Lee, SW 2014, Decision of braking intensity during simulated driving based on analysis of neural correlates. in Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics. January edn, vol. 2014-January, 6974583, Institute of Electrical and Electronics Engineers Inc., pp. 4129-4132, 2014 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2014, San Diego, United States, 14/10/5. https://doi.org/10.1109/SMC.2014.6974583
Kim JW, Kim IH, Lee SW. Decision of braking intensity during simulated driving based on analysis of neural correlates. In Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics. January ed. Vol. 2014-January. Institute of Electrical and Electronics Engineers Inc. 2014. p. 4129-4132. 6974583 https://doi.org/10.1109/SMC.2014.6974583
Kim, Jeong Woo ; Kim, Il Hwa ; Lee, Seong Whan. / Decision of braking intensity during simulated driving based on analysis of neural correlates. Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics. Vol. 2014-January January. ed. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 4129-4132
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