Noise reduction in fNIRS data using extended Kalman filter combined with short separation measurement

Sunghee Dong, Jichai Jeong

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

Abstract

It is challenging to remove the physiological noise that is not evoked by the brain activity in fNIRS signals. We propose a novel method to effectively remove the superficial noise in the hemodynamic signals by combining an extended Kalman filter (EKF) with a short separation measurement based on a nonlinear balloon model. To demonstrate the improved performances of the proposed method over the existing linear Kalman filter (LKF), we use a synthetic hemodynamic signal to compare. As a result, the proposed EKF recovers the modeled hemodynamic responses with lower errors and higher correlation than the LKF.

Original languageEnglish
Title of host publication2018 6th International Conference on Brain-Computer Interface, BCI 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-3
Number of pages3
Volume2018-January
ISBN (Electronic)9781538625743
DOIs
Publication statusPublished - 2018 Mar 9
Event6th International Conference on Brain-Computer Interface, BCI 2018 - GangWon, Korea, Republic of
Duration: 2018 Jan 152018 Jan 17

Other

Other6th International Conference on Brain-Computer Interface, BCI 2018
CountryKorea, Republic of
CityGangWon
Period18/1/1518/1/17

Keywords

  • adaptive filtering
  • extended Kalman filter
  • fNIRS
  • noise reduction

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Behavioral Neuroscience

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  • Cite this

    Dong, S., & Jeong, J. (2018). Noise reduction in fNIRS data using extended Kalman filter combined with short separation measurement. In 2018 6th International Conference on Brain-Computer Interface, BCI 2018 (Vol. 2018-January, pp. 1-3). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IWW-BCI.2018.8311501