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Motion artifact correction of multi-measured functional near-infrared spectroscopy signals based on signal reconstruction using an artificial neural network

Title
Motion artifact correction of multi-measured functional near-infrared spectroscopy signals based on signal reconstruction using an artificial neural network
Authors
Lee, GihyounJin, Sang HyeonAn, Jinung
DGIST Authors
Lee, Gihyoun; Jin, Sang Hyeon; An, Jinung
Issue Date
2018-09
Citation
Sensors, 18(9), 1-16
Type
Article
Article Type
Article
Author Keywords
functional near-infrared spectroscopymotion artifactartificial neural networksignal entropywavelet transform
Keywords
WAVELET SHRINKAGECEREBRAL-BLOODNIRSFMRIGAITIMPROVEMENTACTIVATIONRESPONSESSTROKEMRI
ISSN
1424-8220
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. © 2018 by the authors. Licensee MDPI, Basel, Switzerland.
URI
http://hdl.handle.net/20.500.11750/9340
DOI
10.3390/s18092957
Publisher
Multidisciplinary Digital Publishing Institute (MDPI)
Related Researcher
  • Author An, Jinung Brain Robot Augmented InteractioN(BRAIN) Laboratory
  • Research Interests
Files:
There are no files associated with this item.
Collection:
Convergence Research Center for Wellness1. Journal Articles
ETC1. Journal Articles
Division of Intelligent RobotBrain Robot Augmented InteractioN(BRAIN) Laboratory1. Journal Articles


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