• Proceedings of the Korea Contents Association Conference
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• 2018.05a
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• pp.167-168
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• 2018

The importance of business problem-solving creativity (BPSC) becomes crucial much more as competitive situations go on in the market. However, how to assess the BPSC remains an unsolved research issue yet in the literature. In this sense, this study proposes an exploratory analysis of the BPSC from the view of neuro-science experiments called fNIRS. The fNIRS represents a functional near-infrared spectroscopy, a new type of neuro-science research paradigm. This study proposes an exploratory level of how to conduct the fNIRS-based experiments to analyze the BPSC.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.305-307
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• 2021

Alzheimer's Disease (AD) is a cognitive disorder characterized by memory impairment that can be assessed at early stages based on administering clinical tests. However, the AD pathophysiological mechanism is still poorly understood due to the difficulty of distinguishing different levels of AD severity, even using a variety of brain modalities. Therefore, in this study, we present a hybrid EEG-fNIRS modalities to compensate for each other's weaknesses with the help of Machine Learning (ML) techniques for classifying four subject groups, including healthy controls (HC) and three distinguishable groups of AD levels. A concurrent EEF-fNIRS setup was used to record the data from 41 subjects during Oddball and 1-back tasks. We employed both a traditional neural network (NN) and a CNN-LSTM hybrid model for fNIRS and EEG, respectively. The final prediction was then obtained by using majority voting of those models. Classification results indicated that the hybrid EEG-fNIRS feature set achieved a higher accuracy (71.4%) by combining their complementary properties, compared to using EEG (67.9%) or fNIRS alone (68.9%). These findings demonstrate the potential of an EEG-fNIRS hybridization technique coupled with ML-based approaches for further AD studies.

• Journal of Biomedical Engineering Research
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• v.42 no.6
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• pp.268-276
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• 2021

Functional near-infrared spectroscopy-based brain-computer interface (fNIRS-based BCI) has been receiving much attention. However, we are practically constrained to obtain a lot of fNIRS data by inherent hemodynamic delay. For this reason, when employing machine learning techniques, a problem due to the high-dimensional feature vector may be encountered, such as deteriorated classification accuracy. In this study, we employ an elastic net-based feature selection which is one of the embedded methods and demonstrate the utility of which by analyzing the results. Using the fNIRS dataset obtained from 18 participants for classifying brain activation induced by mental arithmetic and idle state, we calculated classification accuracies after performing feature selection while changing the parameter α (weight of lasso vs. ridge regularization). Grand averages of classification accuracy are 80.0 ± 9.4%, 79.3 ± 9.6%, 79.0 ± 9.2%, 79.7 ± 10.1%, 77.6 ± 10.3%, 79.2 ± 8.9%, and 80.0 ± 7.8% for the various values of α = 0.001, 0.005, 0.01, 0.05, 0.1, 0.2, and 0.5, respectively, and are not statistically different from the grand average of classification accuracy estimated with all features (80.1 ± 9.5%). As a result, no difference in classification accuracy is revealed for all considered parameter α values. Especially for α = 0.5, we are able to achieve the statistically same level of classification accuracy with even 16.4% features of the total features. Since elastic net-based feature selection can be easily applied to other cases without complicated initialization and parameter fine-tuning, we can be looking forward to seeing that the elastic-based feature selection can be actively applied to fNIRS data.

• Journal of the Korea Convergence Society
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• v.12 no.12
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• pp.323-330
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• 2021

In this study, we intended to examine the difference in brain activation due to dominant and non-dominant hands using functional near-infrared spectroscopy(fNIRS) in 10 healthy adults. Box & Block Test(BBT) was conducted under two conditions: dominant hand and non-dominant hand. During the experiment, brain activity was measured using fNIRS and signals were analyzed using nirsLAB v2019.04 software after the experiment was completed. As a result, 6 out of 10 people showed activation of the cerebral hemisphere related to the dominant hand, and only 3 out of 10 people showed activation of the cerebral hemisphere related to the non-dominant hand. In other words, both dominant and non-dominant hand cconfirmed that the cerebral hemispheres related to dominant hands were more active. Therefore, it is believed that fNIRS can be used as a fundamental data applicable to children with sensory processing disorders that are difficult to identify dominant hand.

• Molecules and Cells
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• v.40 no.8
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• pp.523-532
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• 2017

Functional near-infrared spectroscopy (fNIRS) is a noninvasive optical imaging technique that indirectly assesses neuronal activity by measuring changes in oxygenated and deoxygenated hemoglobin in tissues using near-infrared light. fNIRS has been used not only to investigate cortical activity in healthy human subjects and animals but also to reveal abnormalities in brain function in patients suffering from neurological and psychiatric disorders and in animals that exhibit disease conditions. Because of its safety, quietness, resistance to motion artifacts, and portability, fNIRS has become a tool to complement conventional imaging techniques in measuring hemodynamic responses while a subject performs diverse cognitive and behavioral tasks in test settings that are more ecologically relevant and involve social interaction. In this review, we introduce the basic principles of fNIRS and discuss the application of this technique in human and animal studies.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.1-7
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• 2022

With the development of artificial intelligence, efforts to incorporate neuroscience mining with AI have increased. Neuroscience mining, also known as NSM, expands on this concept by combining computational neuroscience and business analytics. Using fNIRS (functional near-infrared spectroscopy)-based experiment dataset, we have investigated the potential of NSM in the context of the BPSC (business problem-solving creativity) prediction. Although BPSC is regarded as an essential business differentiator and a difficult cognitive resource to imitate, measuring it is a challenging task. In the context of NSM, appropriate methods for assessing and predicting BPSC are still in their infancy. In this sense, we propose a novel NSM method that systematically combines CNN, BiLSTM, and attention network for the sake of enhancing the BPSC prediction performance significantly. We utilized a dataset containing over 150 thousand fNIRS-measured data points to evaluate the validity of our proposed NSM method. Empirical evidence demonstrates that the proposed NSM method reveals the most robust performance when compared to benchmarking methods.

• Journal of Biomedical Engineering Research
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• v.36 no.5
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• pp.177-182
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• 2015

The activation of prefrontal cortex of brain during some mental tasks like mental arithmetic induce has been studied using hemodynamic imaging modalities. In this study, we focused on the differentiation of activated area in local prefrontal brain caused by the different mental activities as well as evaluating the classification accuracy of in-house fNIRS system. The study preliminarily validated the device including the signal quality and tightness of contact between detectors and prefrontal area. Experimental results of mental tasks on 5 subjects showed the subject dependent tendencies in correlated prefrontal activation and the area of highest accuracy.

• Physical Therapy Rehabilitation Science
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• v.12 no.3
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• pp.334-339
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• 2023

Objective: The present study is designed to delve deeper into the realm of fibromyalgia (FM) symptom management by investigating the effects of diaphragmatic breathing on the prefrontal cortex (PFC) in women diagnosed with FM. Using functional near-infrared spectroscopy (fNIRS), the study aims to capture real-time PFC activation patterns during the practice of diaphragmatic breathing. The overarching objective is to identify and understand the underlying neural mechanisms that may contribute to the observed clinical benefits of this relaxation technique. Design: A case report Methods: To achieve this, a twofold approach was adopted: First, the patient's breathing patterns were meticulously examined to detect any aberrations. Following this, fNIRS was employed, focusing on the activation dynamics within the PFC. Results: Our examination unveiled a notable breathing pattern disorder inherent to the FM patient. More intriguingly, the fNIRS analysis offered compelling insights: the ventrolateral prefrontal cortex (VLPFC) displayed increased activation. In stark contrast, regions of the anterior prefrontal cortex (aPFC) and orbitofrontal cortex (OFC) manifested decreased activity, especially when benchmarked against typical activations seen in healthy adults. Conclusions: These findings, derived from a nuanced examination of FM, underscore the condition's multifaceted nature. They highlight the imperative to look beyond conventional symptomatology and appreciate the profound neurological and physiological intricacies that define FM.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.407-408
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• 2009

fNIRS(functional Near Infrared Spectroscopy)는 비침습형 뇌기능 분석 시스템으로 뇌활성화 시 옥시 헤모글로빈(oxy-hemoglobin)과 디옥시헤모글로빈(deoxy-hemoglobin) 변화량을 측정할 수 있는 장치이다. 본 논문에서는 뇌기능 분석 장치인 fNIRS를 이용하여 피험자가 컴퓨터 게임 중에 어떤 뇌활성화 패턴을 보이는지를 실험하였다. 컴퓨터 게임 주의 및 집중 시 뇌의 전두엽(Frontal Lobe) 영역이 활성화 및 변화되는 것을 실험결과로 확인하였다. 그리고 게임 중 다른 사람이 피험자에게 개입을 하였을 때 전두엽의 활성화 영역이 다른 패턴을 보이는 것을 실험결과로 확인하였다.

• Journal of Biomedical Engineering Research
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• v.44 no.6
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• pp.392-403
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• 2023

This study presents a new hybrid fNIRS-EEG system to meet the demand for a lightweight and low-cost sleep pattern monitoring device. For multiple-channel configuration, a six-channel electroencephalogram (EEG) and a functional near-infrared spectroscopy (fNIRS) system with eight photodiodes (PD) and four dual-wavelength LEDs are designed. To enhance the convenience of signal measurement, the device is miniaturized into a patch-like form, enabling simultaneous measurement on the forehead. Due to its fully integrated functionality, the developed system is advantageous for performing sleep stage classification with high-temporal and spatial resolution data. This can be realized by utilizing a two-dimensional (2D) brain activation map based on the concentration changes in oxyhemoglobin and deoxyhemoglobin during sleep stage transitions. For the system verification, the phantom model with known optical properties was tested at first, and then the sleep experiment for a human subject was conducted. The experimental results show that the developed system qualifies as a portable hybrid fNIRS-EEG sleep pattern monitoring device.