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

Sunghee Dong; Jichai Jeong

doi:10.1109/IWW-BCI.2018.8311501

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

Sunghee Dong, Jichai Jeong

Department of Brain and Cognitive Engineering

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

4 Citations (Scopus)

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 language	English
Title of host publication	2018 6th International Conference on Brain-Computer Interface, BCI 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-3
Number of pages	3
Volume	2018-January
ISBN (Electronic)	9781538625743
DOIs	https://doi.org/10.1109/IWW-BCI.2018.8311501
Publication status	Published - 2018 Mar 9
Event	6th International Conference on Brain-Computer Interface, BCI 2018 - GangWon, Korea, Republic of Duration: 2018 Jan 15 → 2018 Jan 17

Other

Other	6th International Conference on Brain-Computer Interface, BCI 2018
Country/Territory	Korea, Republic of
City	GangWon
Period	18/1/15 → 18/1/17

Keywords

adaptive filtering
extended Kalman filter
fNIRS
noise reduction

ASJC Scopus subject areas

Artificial Intelligence
Human-Computer Interaction
Behavioral Neuroscience

Access to Document

10.1109/IWW-BCI.2018.8311501

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Noise reduction in fNIRS data using extended Kalman filter combined with short separation measurement. / Dong, Sunghee; Jeong, Jichai.
2018 6th International Conference on Brain-Computer Interface, BCI 2018. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-3.

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

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, Institute of Electrical and Electronics Engineers Inc., pp. 1-3, 6th International Conference on Brain-Computer Interface, BCI 2018, GangWon, Korea, Republic of, 18/1/15. https://doi.org/10.1109/IWW-BCI.2018.8311501

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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.",

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AB - 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.

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

Abstract

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Keywords

ASJC Scopus subject areas

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