FNIRS motion artifact correction for overground walking using entropy based unbalanced optode decision and wavelet regression neural network

Gihyoun Lee, Sang Hyeon Jin, Seung Hyun Lee, Berdakh Abibullaev, Jinung An

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

2 Citations (Scopus)

Abstract

Functional near-infrared spectroscopy (fNIRS) can be employed to investigate brain activation by measuring the absorption of near-infrared light through an intact skull. fNIRS can measure hemoglobin signals, which are similar to functional magnetic resonance imaging (fMRI) blood-oxygen-level-dependent (BOLD) signals. The general linear model (GLM), which is a standard method for fMRI imaging, has been applied for fNIRS imaging analysis. However, when the subject moves, the fNIRS signal can contain artifacts during the measurement. These artifacts are called motion artifacts. However, the GLM has a drawback of failure because of motion artifacts. Recently, wavelet and hemodynamic response function based algorithms are popular detrending methods of motion artifact correction for fNIRS signals. However, these methods cannot show impressive performance in harsh environments such as overground walking tasks. This paper suggests a new motion artifact correction method that uses an entropy based unbalanced optode decision rule and a wavelet regression based back propagation neural network. Through the experiments, the performance of the proposed method was proven using graphic results, a brain activation map, and an objective performance index when compared with conventional detrending algorithms.

Original languageEnglish
Title of host publicationMFI 2017 - 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages186-193
Number of pages8
Volume2017-November
ISBN (Electronic)9781509060641
DOIs
Publication statusPublished - Dec 7 2017
Event13th IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2017 - Daegu, Korea, Republic of
Duration: Nov 16 2017Nov 18 2017

Conference

Conference13th IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2017
CountryKorea, Republic of
CityDaegu
Period11/16/1711/18/17

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Keywords

  • Entropy
  • FNIRS
  • Mortion Artifact
  • Neural Network
  • Wavelet transform

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Science Applications

Cite this

Lee, G., Jin, S. H., Lee, S. H., Abibullaev, B., & An, J. (2017). FNIRS motion artifact correction for overground walking using entropy based unbalanced optode decision and wavelet regression neural network. In MFI 2017 - 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (Vol. 2017-November, pp. 186-193). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MFI.2017.8170427