• Korean Journal of Clinical Laboratory Science
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• v.51 no.4
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• pp.397-405
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• 2019

Quantitative electroencephalography (QEEG) has been widely used in research and clinical fields. QEEG has been widely used to objectively document cerebral changes for the purpose of identifying the electrophysiological biomarkers across various clinical symptoms and for the stimulation of specific cortical regions associated with cognitive function. In electroencephalography (EEG), the difference in quantitative and qualitative analyses is discriminated not by its measurement methods and relevant clinical or research environments, but by its analysis methods. When performing a qualitative analysis, it is possible for a medical technologist or experienced researchers to read the EEG waveforms to exclude artifacts. However, the quantitative analysis is still based on mathematical modeling, and all EEG data are included for the analysis, leading the results to be affected by unexpected artifacts. In the hospital setting, the case that the medical technologists in charge of the EEG test perform academic research has been little reported, compared to other clinical physiological measurement-based research. This is because there are few laboratories specialized in clinical physiological research. In this respect, this study is expected to be utilized as a basic reference material for medical technologists, students, and academic researchers, all of whom would like to conduct a quantitative analysis.

• Journal of the Korean Society for information Management
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• v.41 no.1
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• pp.261-282
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• 2024

This study proposed and evaluated electroencephalography (EEG)-based and eye-tracking-based methods to determine relevance by utilizing users' implicit relevance feedback while navigating content in a digital library. For this, EEG/eye-tracking experiments were conducted on 32 participants using video, image, and text data. To assess the usefulness of the proposed methods, deep learning-based artificial intelligence (AI) techniques were used as a competitive benchmark. The evaluation results showed that EEG component-based methods (av_P600 and f_P3b components) demonstrated high classification accuracy in selecting relevant videos and images (faces/emotions). In contrast, AI-based methods, specifically object recognition and natural language processing, showed high classification accuracy for selecting images (objects) and texts (newspaper articles). Finally, guidelines for implementing a digital library interface based on EEG, eye-tracking, and artificial intelligence technologies have been proposed. Specifically, a system model based on implicit relevance feedback has been presented. Moreover, to enhance classification accuracy, methods suitable for each media type have been suggested, including EEG-based, eye-tracking-based, and AI-based approaches.

• Journal of Digital Contents Society
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• v.19 no.9
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• pp.1819-1824
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• 2018

This paper proposes a control method of ASMR(autonomous sensory meridian response) contents to relieve user's stress and improve his attention. The proposed method measures EEG(electroencephalography), attention, meditation, and eyeblink data from an EEG device and sends them to an oneM2M-compliant IoT(internet of things) server platform through an Android IoT Application. Then a SVM(support vector machine) model is built to classify user's mental health status by using EEG, attention and meditation data collected in the server platform. The ASMR contents are controlled by the mental health status classified by a SVM model and the eyeblink data. When comparing the SVM models according to types of data used, the SVM model with attention and meditation data showed accuracy of 85.7%. It was verified that the proposed control algorithm of ASMR contents properly worked as the mental health status from the SVM model and the eyeblink data changed.

• Journal of Korean Elementary Science Education
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• v.34 no.1
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• pp.131-141
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• 2015

본 연구에서는 과학 영재와 일반아의 창의적 추리과정시 나타나는 두뇌사고 패턴을 sLORETA 분석 기법을 통해 분석하고, 신경생리적 특성을 파악하여 과학 영재아 판별의 기초와 활용 가능성을 알아보는 것이다. 본 연구를 위한 대상자는 과학영재아 6명과 동일 학군 및 학년에 속한 일반아 6명으로 총 12명의 오른손잡이로 하였다. 창의적 추리과정을 위해 사용된 과제는 레이븐 도형점진행렬검사를 사용하였고, 안정상태와 과제 수행간 뇌파를 측정하였다. 뇌파는 19개의 전극을 통해 수집된 16초간의 데이터를 통해 분석하였으며, sLORETA 분석 기법을 통해 8개의 주파수 대역(Delta, Theta, Alpha-1/2, Beta-1/2, Gamma, Omega)에 대한 평균 전류밀도값을 그룹별로 비교하였다. 그룹간 두뇌 활성 주파수 대역을 비교한 결과 눈감고 안정 상태에서 과학영재아가 일반아에 비해 알파-2 대역에서, 레이븐 과제 수행시 과학 영재아가 일반아에 비해 알파-1과 감마 대역에서 강한 활성이 관찰되었다. 연구 결과 나타난 알파 및 감마 대역 활성과 우반구로의 기능적 편측화(Lateralization)는 창의적 문제 해결시 영재아에게 나타나는 대표적 특성 중 하나이며, 배외측전전두피질(DLPFC)의 활성은 과학영재아의 높은 유동지능을 반영하는 결과라 볼 수 있다.

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.1
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• pp.7-12
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• 2020

Various parameters are used to measure anesthetic depth during surgery using brain waves, and in actual clinical use, the linear analysis SEF is widely used. However, with recent studies showing that biological signals including EEG, contain nonlinear properties interest in nonlinear analysis of brain signals is increasing and parameters based on these are being developed. In this study, we are going to develop a parameter that can measure EEG using the nonlinear analysis method and extract noise that can be mixed with external electronic equipment and EEG instrumentation by comparing it with the data from the bispectrum analysis of static waves.

• The Journal of the Korea Contents Association
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• v.13 no.1
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• pp.40-47
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• 2013

To verify the condition of patients moving in the medical center like hospital needs to be consider the various wireless communication network protocols and network components. Wireless communication protocols such as the 802.11a, 802.11g, and direct sequence has their specific characteristics, and the various components such as the number of mobile nodes or the distance of transmission range could affects the performance of the network. Especially, the network topologies are considered the characteristic of the brain wave(EEG) since the condition of patient is detected from it. Therefore, in this paper, various wireless communication networks are designed and simulated with Opnet simulator, then evaluated the performance to verify the wireless network that transmits the patient's EEG data efficiently. Overall, the 802.11g had the best performance for the wireless network environment that transmits the EEG data. However, there were minor difference on the performance result depends on the components of the topologies.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.12
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• pp.459-465
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• 2017

This research aims to assess whether neurofeedback training can reduce theta waves in adolescents. The experiment was conducted on 35 early youths living in Gyeonggi-do at youth counseling centers during April-October. According to circumstances and opinions of participants in the pre-brain analysis, they were classified into a non-training group (A), 12-week training group (B), and 24-week training group (C), containing 10, 15, and 10 members, respectively. EEG measurement and neurofeedback training was performed using the prefrontal 2-channel NeuroharmonyS and Brain Optimization program. EEG data was processed utilizing Brain Analysis ver1.3. Deducted data was converted to SPSS 21.0 to enable statistical processing. As a strategy to reduce theta through the Beta increase training, we applied the appropriate Alpha, SMR, Beta low reward training to the individual. Study results confirmed that theta waves of adolescents decreased through the prefrontal neurofeedback training. Groups (B) and (C) exhibited a greater decrease in theta waves compared with the control group.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.9 no.6
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• pp.729-738
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• 2019

Recently, we aim to improve the quality of virtual reality contents based on quantitative analysis results of emotions through combination of emotional recognition field and virtual reality field. Emotions are analyzed based on the participant's vital signs. Much research has been done in terms of signal analysis, but the methodology for quantifying emotions has not been fully discussed. In this paper, we propose a normalization function design and expression method to quantify the emotion between various bio - signals. Use the Brute force algorithm to find the optimal parameters of the normalization function and improve the confidence score of the parameters found using the true and false scores defined in this paper. As a result, it is possible to automate the parameter determination of the bio-signal normalization function depending on the experience, and the emotion can be analyzed quantitatively based on this.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.2
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• pp.122-129
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• 2014

In this study, emotion-classification gathered from users was performed, classification-experiments were then conducted using SVM(Support Vector Machine) and K-means algorithm. Total 15 numbers of channels; CP6, Cz, FC2, T7. PO4, AF3, CP1, CP2, C3, F3, FC6, C4, Oz, T8 and F8 among 32 members of the channels measured were adapted in Brain signals which indicated obvious the classification of emotions in previous researches. To extract emotion, watching DVD and IAPS(International Affective Picture System) which is a way to stimulate with photos were applied and SAM(Self-Assessment Manikin) was used in emotion-classification to users' emotional conditions. The collected users' Brain-wave signals gathered had been pre-processing using FIR filter and artifacts(eye-blink) were then deleted by ICA(independence component Analysis) using. The data pre-processing were conveyed into frequency analysis for feature extraction through FFT. At last, the experiment was conducted suing classification algorithm; Although, K-means extracted 70% of results, SVM showed better accuracy which extracted 71.85% of results. Then, the results of previous researches adapted SVM were comparatively analyzed.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.9
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• pp.361-366
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• 2018

Previous studies on fear of crime used survey and interview to measure fear of crime. However, though these methods can measure fear of crime in past events, they cannot measure real time fear of crime. In this paper, we use EEG to measure fear of crime in real time. We measure and analyze the EEG of subjects watching the video and confirm the difference between three groups classified according to the degree of fear of crime. As a result, about two times more beta waves are shown when a group of subjects with a high degree of fear of crime watches the images of fear of crime and 1.5 times more beta waves are shown among the other groups. Although watching videos related to the crime increased the beta waves, the police video showed little increase in beta waves because the subjects can sense safety in the video even if it is related to crime.