• Journal of Biomedical Engineering Research
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• v.6 no.1
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• pp.1-4
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• 1985

Background EEG signals can be represented as the sum of a conventional AR process and an innovation process. It is know that conventional estimation techniques, such as least square estimates (LSE) or Gauasian maximum likelihood estimates (MLE-G) are optimal when the innovation process satisfies the Gaussian or presumed distribution. When the data are contaminated by outliers, however, these assumptions are not met and the power spectrum estimated by conventional estimation techniques may be fatally biased. EEG signal may be affected by artifacts, which are outliers in the statistical term. So the robust filtering estimation technique is used against those artifacts and it performs well for the contaminated EEG signal.

• Journal of IKEEE
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• v.24 no.1
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• pp.269-274
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• 2020

This paper is to study the exoskeleton robot for the walking of the elderly and the disabled. We developed and tested an Exoskeletal robot with two axes of freedom for joint motion. The EEG and EMG signals were used to move the joints of the Exoskeletal robot. By analyzing the EMG signal, the control signal was extracted and applied to the robot to facilitate the walking operation of the walking assistance robot. In addition, the brain-computer interface technology is applied to perform the operation of the robot using brain waves, spontaneous electrical activities recorded on the human scalp. These two signals were fused to study the walking recognition method of the supporting robot leg.

• 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.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.2083-2085
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• 2009

Brain Computer Interface (BCI) is a communication pathway between devices (computers) and human brain. It treats brain signals in real-time basis and discriminates some information of what human brain is doing. In this work, we develop a EEG BCI system using a feature extraction such as common spatial pattern (CSP) and a classifier using Fisher linear discriminant analysis (FLDA). Two-class EEG motor imagery movement datasets with both cued and uncued are tested to verify its feasibility.

• 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.

• 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.