https://scholar.dgist.ac.kr/handle/20.500.11750/465 2026-04-04T22:01:19Z https://scholar.dgist.ac.kr/handle/20.500.11750/9340 Title: Motion artifact correction of multi-measured functional near-infrared spectroscopy signals based on signal reconstruction using an artificial neural network Author(s): Lee, Gihyoun; Jin, Sang Hyeon; An, Jinung 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. 2018-08-31T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/8983 Title: Robust functional near infrared spectroscopy denoising using multiple wavelet shrinkage based on a hemodynamic response model Author(s): Lee, Gihyoun; Lee, Seung Hyun; Jin, Sang Hyeon; An, Jin Ung Abstract: Functional near infrared spectroscopy can measure hemodynamic signals, and the results are similar to functional magnetic resonance imaging of blood-oxygen-level-dependent signals. Thus, functional near infrared spectroscopy can be employed to investigate brain activity by measuring the absorption of near infrared light through an intact skull. Recently, a general linear model, which is a standard method for functional magnetic resonance imaging, was applied to functional near infrared spectroscopy imaging analysis. However, the general linear model fails when functional near infrared spectroscopy signals retain noise, such as that caused by the subject's movement during measurement. Although wavelet-based denoising and hemodynamic response function smoothing are popular denoising methods for functional near infrared spectroscopy signals, these methods do not exhibit impressive performances for very noisy environments and a specific class of noise. Thus, this paper proposes a new denoising algorithm that uses multiple wavelet shrinkage and a multiple threshold function based on a hemodynamic response model. Through the experiments, the performance of the proposed algorithm is verified using graphic results and objective indexes, and it is compared with existing denoising algorithms. © 2018, The Author(s) 2018. 2018-03-31T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/6554 Title: Selective Detrending using Baseline Drift Detection Index for Task-dependant fNIRS Signal Author(s): An, Jin Ung; Lee, Gihyoun; Lee, Seung Hyun; Jin, Sang Hyeon Abstract: A functional near-infrared spectroscopy (fNIRS) can be employed to investigate brain activation by measuring the absorption of near-infrared light through the intact skull. The general linear model (GLM) as a standard model for fMRI analysis has been applied to functional near-infrared spectroscopic (fNIRS) imaging analysis as well. The GLM has drawback of failure in fNIRS signals, when they have drift globally. Wavelet based detrending technique is very popular to correct the baseline drift (BD) in fNIRS. However, this method globally distorted the total multi-channel signals even if just one channel’s signal was locally drifted. This paper suggests the selective detrending method using BD detection index to indicate BD as an objective index. The experiments show the performance of the proposed method as graphic results and objective evaluation index with current detrending algorithms. © 2017 ASTES Publishers. All rights reserved. 2017-04-30T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/6485 Title: 최적 적분알고리즘의 특성에 관한 연구 Author(s): 손병락; 김은수; 최성희 Abstract: In the integration problem defined on a set of functions, we choose the Simpson's quadrature and study its probabilistic properties. In order to study the probabilistic properties, we assume that the function space is equipped with -fold Wiener measure. We also prove that the Simpson's quadrature is an optimal algorithm, when the regularity degree is less than 4. 2015-12-31T15:00:00Z