• Journal of Biomedical Engineering Research
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• v.10 no.3
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• pp.303-308
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• 1989

A new estimationn mothod of single-EP(evoked potential) using adaptive algorithm and paralnetrlc model is proposed. Since the EEG(eletroencephalogram) signal is stationary in short time interval the AR(autoregressive) parameters of the EEG are estimated by the Burg algorithm using the EEG of prestimulus interval. After stimulus, the single-EP is estimated by adaptive algorithm. The validity of this method is verified by the simulation for generated auditory single-EP based on parametric model.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.2
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• pp.217-222
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• 2004

A person does communication between each other using language. But, In the case of disabled person, cannot communicate own idea to use writing and gesture. We embodied communication system using the EEG so that disabled person can do communication. After feature extraction of the EEG included facial muscle signals, it is converted the facial muscle into control signal, and then did so that can select character and communicate idea.

• Proceedings of the KAIS Fall Conference
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• 2009.05a
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• pp.162-165
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• 2009

본 논문에서는 뇌-컴퓨터 인터페이스(Brain-Computer Interface) 기술을 중 움직임과 관련된 EEG(Electroencephalograph)신호를 이용하여 한국어를 생성하기 위한 시스템 설계 방법을 제안한다. 뇌-컴퓨터 인터페이스의 정보변환율(Information Transfer Rate)향상을 위하여 바이오피드백 방법과 기계학습 방법을 동시에 적용시킬 수 있는 방법과 움직임 관련 SMR(Sensorimotor Rhythm)과 한국어 음절, 어절 예측을 기술을 사용하여 ALS환자 혹은 운동능력이 없는 사람들을 위한 한국어 생성을 위한 설계 방법에 대해서 연구하였다.

• Journal of Biomedical Engineering Research
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• v.31 no.6
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• pp.456-463
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• 2010

In the present study, we proposed a new subspace scanning algorithm to enhance the spatial resolution of electroencephalography (EEG) and magnetoencephalography(MEG) source localization. Subspace scanning algorithms, represented by the multiple signal classification (MUSIC) algorithm and the first principal vector (FINE) algorithm, have been widely used to localize asynchronous multiple dipolar sources in human cerebral cortex. The conventional MUSIC algorithm used principal component analysis (PCA) to extract the noise vector subspace, thereby having difficulty in discriminating two or more closely-spaced cortical sources. The FINE algorithm addressed the problem by using only a part of the noise vector subspace, but there was no golden rule to determine the number of noise vectors. In the present work, we estimated a non-orthogonal signal vector set using independent component analysis (ICA) instead of using PCA and performed the source scanning process in the signal vector subspace, not in the noise vector subspace. Realistic 2D and 3D computer simulations, which compared the spatial resolutions of various algorithms under different noise levels, showed that the proposed ICA-MUSIC algorithm has the highest spatial resolution, suggesting that it can be a useful tool for practical EEG/MEG source localization.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.06a
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• pp.993-994
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• 2009

• Journal of Biomedical Engineering Research
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• v.17 no.2
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• pp.209-214
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• 1996

The display of biological signals in raster scan monitors often involves a multirate sampling operation which consists of decimation .and interpolation. All electroencephalouaphic (EEG) samples of 10 to 30 seconds (2, 500 to 7, 500 samples at 250[Hz] sampling frequency) must be displayed in the computer screen to keep the aspect ratio of the paper polygraph output. Since the current afrorclable display technology Plots at most 2, 000 Pixels Per row, sDme signal samples need to be discarde4 This Paper studies methods to perform this operation characterizing them from the signal processing viewpoint and compares the display quality among several decimation techniques. Experimental results show that a nonlinear operation such as the peak detection method could be preferable to the canonical linear filtering to reduce aliasing.

• Progress in Medical Physics
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• v.5 no.1
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• pp.41-53
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• 1994

The accurate localization of electrical sources in the brain is one of the most important questions in EEG, especially in the analysis of evoked responses and of epileptiform spike activity. A detailed simulation study of single dipole source estimation based on EEG is given in this paper. The effects of dipole model parameters on single dipole source tracing in EEG are examined in some detail using the Monte Carlo simulation. The error of source localization is found to be greatly influenced by how the electrodes are distributed over the head and the number of them.

• Annals of Clinical Neurophysiology
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• v.2 no.1
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• pp.8-12
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• 2000

Purpose : The rapid development and wide popularity of Digital EEG(DEEG) is due to its convenience, accuracy and applicability for quantitative analysis. These advantages of DEEG make one hesitate to use analog EEG(AEEG). To assess the advantage of DEEG system utilizing AEEG(DAEEG) over conventional AEEG and the clinical applicability, a DAEEG system was developed and applied to animal model Methods : Sprague-Dawley rat as status epilepticus model were used for collecting the EEG data. After four epidural electrodes were inserted and connected to 8-channel analog EEG(Nihon-Kohden, Japan), continous. EEG monitoring via computer screen was done from two rats simultaneously. EEG signals through analog amplifier and filters were digitized at digital signal processor and stored in Windows-based pentium personal computer. Digital data were sampled at a rate of 200 Hz and 12 bit of resolution. Acquisition software was able to carry out 'real-time view, sensitivity control and event marking' during continuous EEG monitoring. Digital data were stored on hard disk and hacked-up on CD-ROM for off-line review. Review system consisted of off-line review, saving and printing out interesting segment and annotation function. Results: This DAEEG system could utilize most major functions of DEEG sufficiently while making a use of an AEEG. It was easy to monitor continuously compared to Conventional AEEG and to control sensitivity during ictal period. Marking the event such as a clinical seizure or drug injection was less favorable than AEEG due to slowed processing speed of digital processor and central processing unit. Reviewing EEG data was convenient, but paging speed was slow. Storage and management of data was handy and economical. Conclusion : Relatively simple digital EEG system utilizing AEEG can be set-up at n laboratory level. It may be possible to make an application for clinical purposes.

• The Journal of the Society of Korean Medicine Diagnostics
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• v.11 no.2
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• pp.96-115
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• 2007

Background and Purpose: Electroencephalogram(EEG) is a multi-scaled signal consisting of several components of time series with different origins. Recently, because of the absence of an identified metric which quantifies the complex amount of information, there are many limitations in using such a linear method. According to chaos theory, irregular signals of EEG can also result from low dimensional deterministic chaos. Chaotic nonlinear dynamics in the EEG can be studied by calculating the correlation dimension. The aim of this study is to analyze correlation between the correlation dimension of EEG and psychological Test (TCI). Methods: Before and after moxibustion treatment, EEG raw data were measured by moving windows during 15 minutes. The correlation dimension(D2) was calculated from stabilized 40 seconds in 15 minutes data. 8 channels EEG study on the Fp, F, T, P was carried out in 30 subjects. Results: Correlation analysis of TCI test is calculated with deterministic non-linear data and stochastic non-linear data. 1. Novelty seeking in temperament is positive correlated with D2 of EEG on Fp. 2. reward dependence in temperament is positive correlated with D2 of EEG on T3,T4 and negative correlated with D2 of EEG on P3,P4. 3. self directedness in character is positive correlated with D2 of EEG on F4, P3. 4. Harm avoidance is negative correlated with D2 of EEG on Fp2, T3, P3. Conclusion: These results suggest that nonlinear analysis of EEG can quantify dynamic state of brain abolut psychological Test (TCI).

• Journal of KIISE
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• v.45 no.1
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• pp.45-52
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• 2018

With the advent of BCI technology in recent years, various BCI products have been released. BCI technology enables brain information to be transmitted directly to a computer, and it will bring a lot of convenience to life. However, there is a problem with information protection. In particular, EEG data can raise issues about personal privacy. Collecting and analyzing big data on EEG reports raises serious concerns about personal information exposure. In this paper, we propose a secure privacy-preserving BCI model in a big data environment. The proposed model could prevent personal identification and protect EEG data in the cloud environment.