• International Journal of Contents
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• v.9 no.4
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• pp.1-10
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• 2013

The electroencephalogram (EEG) time series is a measure of electrical activity received from multiple electrodes placed on the scalp of a human brain. It provides a direct measurement for characterizing the dynamic aspects of brain activities. These EEG signals are formed from a series of spatial and temporal data with multiple dimensions. Missing data could occur due to fault electrodes. These missing data can cause distortion, repudiation, and further, reduce the effectiveness of analyzing algorithms. Current methodologies for EEG analysis require a complete set of EEG data matrix as input. Therefore, an accurate and reliable imputation approach for missing values is necessary to avoid incomplete data sets for analyses and further improve the usage of performance techniques. This research proposes a new method to automatically recover random consecutive missing data from real world EEG data based on Linear Dynamical System. The proposed method aims to capture the optimal patterns based on two main characteristics in the coevolving EEG time series: namely, (i) dynamics via discovering temporal evolving behaviors, and (ii) correlations by identifying the relationships between multiple brain signals. From these exploits, the proposed method successfully identifies a few hidden variables and discovers their dynamics to impute missing values. The proposed method offers a robust and scalable approach with linear computation time over the size of sequences. A comparative study has been performed to assess the effectiveness of the proposed method against interpolation and missing values via Singular Value Decomposition (MSVD). The experimental simulations demonstrate that the proposed method provides better reconstruction performance up to 49% and 67% improvements over MSVD and interpolation approaches, respectively.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.3
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• pp.388-393
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• 2010

Cultural Content Technology(CT, Culture Technology) for the development of cultural industry and the commercialization of technology, cultural contents, media, mount, pass the value chain process and increase the added value of cultural products that are good for all forms of intangible technology. In the field of Culture Technology, Music by analyzing the characteristics of the development of a variety of applications has been studied. Associated with EEG measures and the results of their research in response to musical stimuli are used to detect and study is getting attention. In this paper, the musical stimuli in EEG signals by amplifying the corresponding reaction to the averaging method, ERP (Event-Related Potentials) experiments based on the process of extracting sound methods for removing noise from the ICA algorithm to extract the tone and noise removal according to the results are applied to analyze the characteristics of EEG.

• The Journal of the Korea Contents Association
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• v.7 no.8
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• pp.41-49
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• 2007

BCI system integrates control or telecommunication system with generating electric signals in scalp itself after signal acquisition. This system detect a movement of EEG at real time, can control an electron equipment using a generated signal through EEG movement or software-based processor. In this paper, we deal with removing and separating artifacts induceced from measurement when brain-computer interface system that analyzes recognizes EEG signals occurred from various mental states. In this paper, we proposed a method of EEG classification and an artifact interval detection using bisection mathematical modeling in the EEG classification process for BCI system implementation.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.2
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• pp.63-69
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• 2019

In this study, bispectrum analysis method introduced to reduce variability of SEF(spectral edge frequency) and MF(median frequency), which are the anesthetic depth indexes extracted by EEG spectral analysis. Bispectrum analysis is an analytical method that can confirm the nonlinearity of EEG. Signal measurement and analysis in the surgical environment should take into consideration various external artifact factors. Bispectrum analysis can confirm the presence of externally introduced artifacts, thereby effectively eliminating artifacts that affect the EEG signal. By applying bispectrum parameters, real-time variability of the anesthetic depth parameters SEF, MF could be reduced. Elimination of variability makes it possible to use SEF, MF as a real-time index during surgery.

• Journal of the Korea Society of Computer and Information
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• v.23 no.11
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• pp.129-135
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• 2018

In this study, we designed a model that can measure the level of user's concentration by measuring and analyzing EEG data of the subjects who are performing Continuous Performance Test based on visual stimulus. This study focused on alpha and beta waves, which are closely related to concentration in various brain waves. There are a lot of research and services to enhance not only concentration but also brain activity. However, there are formidable barriers to ordinary people for using routinely because of high cost and complex procedures. Therefore, this study designed the model using the portable EEG measurement device with reasonable cost and Visual Continuous Performance Test which we developed as a simplified version of the existing CPT. This study aims to measure the concentration level of the subject objectively through simple and affordable way, EEG analysis. Concentration is also closely related to various brain diseases such as dementia, depression, and ADHD. Therefore, we believe that our proposed model can be useful not only for improving concentration but also brain disease prediction and monitoring research. In addition, the combination of this model and the Brain Computer Interface technology can create greater synergy in various fields.

• Journal of Korea Multimedia Society
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• v.21 no.5
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• pp.609-616
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• 2018

In this paper, we designed a multimodal bio-signal measurement system to observe changes in the brain nervous system and vascular system during sleep. Changes in the nervous system and the cerebral blood flow system in the brain during sleep induce a unique correlation between the changes in the nervous system and the blood flow system. Therefore, it is necessary to simultaneously observe changes in the brain nervous system and changes in the blood flow system to observe the sleep state. To measure the change of the nervous system, EEG, EOG and EMG signal used for the sleep stage analysis were designed. We designed a system for measuring cerebral blood flow changes using functional near-infrared spectroscopy. Among the various imaging methods to measure blood flow and metabolism, it is easy to measure simultaneously with EEG signal and it can be easily designed for miniaturization of equipment. The sleep stage was analyzed by the measured data, and the change of the cerebral blood flow was confirmed by the change of the sleep stage.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2001.05a
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• pp.271-275
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• 2001

두뇌-컴퓨터 인터페이스로 활용하기 위한 시도로서, 인간의 가장 간단한 의사 표시인 긍/부정 의사와 관련한 뇌파를 측정하고 시간-주파수 분석을 수행하였다. 선행 연구 결과와 뇌파 측정 실험 조건에 대해 살펴 보고, 시간-주파수 분석 결과로부터 긍/부정 반응 동작에 따른 뇌파 변화에 대해 토론하였다.

• Proceedings of the ESK Conference
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• 1996.04a
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• pp.259-263
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• 1996

Psychological action is physiological response of outernal stimulus. Physiological response is accompanied b physiological signals which are EEG, EMG, GSR, ECG, BP, and tec. Physiological signals are recently studied for determination of human phychological state. Psychological activity causes electric potential of brain. Physiological signal is considered as measurement of human psychological state. Aditory sensibility which is one of the sense of human may determine differences between positive and negative feeling. EEG and GSR variation with auditory quality of stimulus can be define human negative and positive mental state. This study is to characterize parameters which can determine negative and positive psycholigical state of human.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.1
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• pp.1-8
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• 2019

Motion sickness is dizziness symptom that occurs when movement detected in the vestibular organ and movement detected visually are collide with each other. When dizziness occurs, user complains of symptoms such as nausea and vomiting, sense of direction abnormality, and fatigue. These causes of dizziness are various and difficult to differentiate and treat the symptoms. Especially, among the types of dizziness VIMS(Visually Induced Motion Sickness) is a problem to solve in developing VR industry. These VIMS analysis can be done through user's vital signs measurement and feature analysis, and EEG characteristics analysis. Therefore, this paper is discuss the minimization of motion sickness caused by visual information based on EEG signal and present research trends related to it.

• Journal of IKEEE
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• v.23 no.2
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• pp.727-734
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• 2019

This paper confirms whether the movement or specific operation of the muscles in the process of transferring a person from the brain can find a signal showing an essential feature of a certain part of the brain. As a rule, the occurrence of EEG(Electroencephalogram) changes when a signal is received from a specific action or from an induced action. These signals are very vague and difficult to distinguish from the naked eye. Therefore, it is necessary to define a signal for analysis before classification. The EEG form can be divided into the alpha, beta, delta, theta and gamma regions in the frequency ranges. The specific size of these signals does not reflect the exact behavior or intention, since the band or energy difference of the activated frequencies varies depending on the EEG measurement domain. However, if different actions are performed in a specific method, it is possible to classify the movement based on EEG activity and to determine the EEG tendency affecting the movement. Therefore, in this article, we first study the EEG expression pattern based on the activation of the left and right biceps EMG, and then we determine whether there is a significant difference between the EEG due to the activation of the left and right muscles through EEG. If we can find the EEG classification criteria in accordance with the EMG activation, it can help to understand the form of the transmitted signal in the process of transmitting signals from the brain to each muscle. In addition, we can use a lot of unknown EEG information through more complex types of brain signal generation in the future.