• Journal of the Institute of Electronics Engineers of Korea CI
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• v.39 no.3
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• pp.32-43
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• 2002

In this paper, we propose a pattern classification system for digital signal which is based on neural networks. The proposed system consists of two models of neural network. The first part is a wavelet neural network whose role is a feature extraction. For this part, we compare existing models of wavelet networks and propose a new model for feature extraction. The other part is a wavelet network for pattern classification. We modify the structure of previous wavelet network for pattern classification and propose a learning method. The inputs of the pattern classification wavelet network is connection weights, dilation and translation parameters in hidden nodes of feature extraction network. And the output is a class of the signal which is input of feature extraction network. The proposed system is, applied to classification of EEG signal based on frequency.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1983-1984
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• 2008

뇌 활동의 전기적 신호인 뇌파(EEG)와 외부 자극에 대한 유발 전위(EP)를 측정하여 실시간으로 뇌지형도를 생성하는 real-time brain mapping system을 개발하였다. 측정 전극은 32채널을 사용하였고, EEG를 실시간 및 누적 주파수 분석을 통한 뇌파의 활성도 진단, EP를 측정하여 시각적/청각적 자극에 의한 유발 전위 분석을 할 수 있다. 본 시스템은 측정 대상군의 통계적 분석을 위한 Database를 구축하였고, 신뢰성 높은 뇌파 및 유발 전위 신호를 위하여 실시간 측정과정 및 측정 후 Data 검토과정에서 다양한 Artifact 제거 알고리즘이 도입되었다. 또한, 32 채널 Brain map을 구성하여 뇌파를 공간적으로 분석 가능하며, 시간 및 주파수의 증가에 따라 Brain map을 동영상화하여 시간적/주파수적 변화에 따른 분석이 가능하다.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.642-645
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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 Korea Game Society
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• v.9 no.2
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• pp.23-28
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• 2009

There are a lot of games which have good benefits in the game genre such as serious game. In this paper we implement an serious game for increasing intensive power by calculating the index of the intensive power based on EEG signal. First we explain the definition of the EEG and the classification of the brainwaves and we depict the method for increasing the intensive power. Then we apply the index of the intensive power to the game production to train the intensive power. At last we make an experiment on the effect of an game which increases the intensive power and the analysis shows the increase of the intensive power.

• Journal of Biomedical Engineering Research
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• v.20 no.2
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• pp.205-212
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• 1999

When we want to observe and record a patient's EEG in an operating room, the operation of electrosurgical unit(ESU) causes undesirable artifacts with high frequency and high voltage. These artifacts make the amplifiers of the conventional EEG system saturated and prevent the system from measuring the EEG signal. This paper describes a high-performance bipolar EEG amplifier for a CSA (compressed spectral array ) system with reduced ESU artifacts. The designed EEG amplifier uses a balanced filter to reduce the ESU artifacts, and isolates the power supply and the signal source of the preamplifier from the ground to cut off the current from the ESU to the amplifier ground. To cancel the common mode noise in high frequency, a high CMRR(common mode rejection ratio) diffferential amplifier is used. Since the developed bipolar EEG amplifier shows high gain, low noise, high CMRR, high input impedance, and low thermal drift, it is possible to observe and record more clean EEG signals in spite of ESU operation. Therefore the amplifier may be applicable to a high-fidelity CSA system.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.3
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• pp.89-95
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• 2015

Compresses sensing (CS) technique is beneficial for reducing power consumption of biopotential acquisition circuits in wireless healthcare system. This paper investigates the maximum possible compress ratio for various biopotential signal when the CS technique is applied. By using the CS technique, we perform the compression and reconstruction of typical electrocardiogram(ECG), electromyogram(EMG), electroencephalogram(EEG) signals. By comparing the original signal and reconstructed signal, we determines the validity of the CS-based signal compression. Raw-biopotential signal is compressed by using a psuedo-random matrix, and the compressed signal is reconstructed by using the Block Sparse Bayesian Learning(BSBL) algorithm. EMG signal, which is the most sparse biopotential signal, the maximum compress ratio is found to be 10, and the ECG'sl maximum compress ratio is found to be 5. EEG signal, which is the least sparse bioptential signal, the maximum compress ratio is found to be 4. The results of this work is useful and instrumental for the design of wireless biopotential signal monitoring circuits.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.3
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• pp.136-144
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• 2015

The EEG system can be classified as a wired or wireless device. Each device used for the medical or entertainment purposes. The collected EEG signals from sensor are analyzed using feature extractions. A wireless EEG system provides good portability and convenience, however, it requires a mobile system that has heavy computing power. In this paper a single board system is proposed to handle EEG signal processing for BCI applications. Unfortunately, the computing power of a single board system is limited unlike general desktop systems. Thus, parallel approach using multiple single board systems is investigated. The parallel EEG signal processing system that we built demonstrates superlinear speedup for an EEG signal processing algorithm.

• Korean Journal of Acupuncture
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• v.21 no.2
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• pp.1-28
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• 2004

Objectives : Human physiological changes in the state of qigong has been measured using EEG(Electroencephalography), functional MRI(functional Magnetic Resonance Image), EAV(Electro-Acupuncture according to Voll) and SQUID(Superconducting Quantum Interference Device) measurements. Methods & Results : EEGs were measured to study the differences between Qigong masters and Qi receiver on the changes of EEG. During Qigong, an alpha waves were increased. The power spectra indicate that the peak frequency of alpha waves increased during Qigong. Qi receiver's EEG signals seemed to affected by the state of himself. Brain activation did not observed when qigong master concentrates the Qi at Laogong(P8). But a localization of fMRI signal in the sensory cortex was observed by electric acupuncture stimulation at Laogong(P8). Five phase deviation of EAV were clearly changed in the both cases of Qigong master and Qi receiver. When a Qigong master concentrates the Qi at Yintang, Laogong(P8), Qihai(CV6) meridian points during Qigong state, the change of magnetic field around acupoints Yintang, Laogong points has been measured using 40-Channel DROS-SQUID apparatus. After smoothing process of the continuously measured magnetic signal around acupoints for a few minutes, we could observe that a series of peaks, magnitude of -1.0~2.5pT appeared. But there was no significant difference in changes of magnetic signal around acupoints. Physical signals of magnetocardiogram has been measured by using 2-Channel DROS SQUID(Magnetocardiogram). Physical signals of magnetocardiogram were clealy changed at the ST segments after S-wave when qigong master concentrates the Qi.

• Journal of National Security and Military Science
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• s.1
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• pp.249-267
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• 2003

EEG is an electrical signal, which occurs during information processing in the brain. These EEG signals have been used clinically, but nowadays we are mainly studying Brain-Computer Interface (BCI) such as interfacing with a computer through the EEG, controlling the machine through the EEG. The ultimate purpose of BCI study is specifying the EEG at various mental states so as to control the computer and machine. This research makes the controlling system of directions with the artifact that are generated from the subject's will, for the purpose of controlling the machine correctly and reliably. We made the system like this. First, we select the particular artifact among the EEG mixed with artifact, then, recognize and classify the signals' pattern, then, change the signals to general signals that can be used by the controlling system of directions.

• Journal of Information Technology Applications and Management
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• v.10 no.4
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• pp.159-168
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• 2003

This paper is concerned with the compression of EEG signals using wavelet-packet based techniques. EEG data compression is desirable for a number of reasons. Primarily it decreases for transmission time, archival storage space, and in portable systems, it decreases memory requirements or increases channels and bandwidth. Upon wavelet decomposition, inherent redundancies in the signal can be removed through thresholding to achieve data compression. We proposed the energy cumulative function for deciding of the threshold value and it works very innovative of EEG data.