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Sensors 2018, 18(9), 2957; https://doi.org/10.3390/s18092957

Motion Artifact Correction of Multi-Measured Functional Near-Infrared Spectroscopy Signals Based on Signal Reconstruction Using an Artificial Neural Network

Convergence Research Center for Wellness, DGIST, Daegu 42988, Korea
This paper is an extension of Lee, G.; Jin, S. H.; Lee, S. H.; Abibullaev, B.; An, J. fNIRS motion artifact correction for overground walking using entropy based unbalanced optode decision and wavelet regression neural network. In Proceedings of the 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), Daegu, Korea, 16–18 November 2017.
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Received: 26 June 2018 / Revised: 7 August 2018 / Accepted: 31 August 2018 / Published: 5 September 2018
(This article belongs to the Section Sensor Networks)
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Abstract

In this paper, a new motion artifact correction method is proposed based on multi-channel functional near-infrared spectroscopy (fNIRS) signals. Recently, wavelet transform and hemodynamic response function-based algorithms were proposed as methods of denoising and detrending fNIRS signals. However, these techniques cannot achieve impressive performance in the experimental environment with lots of movement such as gait and rehabilitation tasks because hemodynamic responses have features similar to those of motion artifacts. Moreover, it is difficult to correct motion artifacts in multi-measured fNIRS systems, which have multiple channels and different noise features in each channel. Thus, a new motion artifact correction method for multi-measured fNIRS is proposed in this study, which includes a decision algorithm to determine the most contaminated fNIRS channel based on entropy and a reconstruction algorithm to correct motion artifacts by using a wavelet-decomposed back-propagation neural network. The experimental data was achieved from six subjects and the results were analyzed in comparing conventional algorithms such as HRF smoothing, wavelet denoising, and wavelet MDL. The performance of the proposed method was proven experimentally using the graphical results of the corrected fNIRS signal, CNR that is a performance evaluation index, and the brain activation map. View Full-Text
Keywords: functional near-infrared spectroscopy; motion artifact; artificial neural network; signal entropy; wavelet transform functional near-infrared spectroscopy; motion artifact; artificial neural network; signal entropy; wavelet transform
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Lee, G.; Jin, S.H.; An, J. Motion Artifact Correction of Multi-Measured Functional Near-Infrared Spectroscopy Signals Based on Signal Reconstruction Using an Artificial Neural Network. Sensors 2018, 18, 2957.

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