• Proceedings of the IEEK Conference
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• 2000.06e
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• pp.127-130
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• 2000

Classification of Electroencephalogram(EEG) makes one of key roles in the field of clinical diagnosis, such as detection for epilepsy. Spectrum analysis using the fourier transform(FT) uses the same window to signals, so classification rate decreases for nonstationary signals such as EEG's. In this paper, wavelet power spectrum method using wavelet transform which is excellent in detection of transient components of time-varying signals is applied to the classification of three types of Event Related Potential(EP) and compared with the result by fourier transform. In the experiments, two types of photic stimulation, which are caused by eye opening/closing and artificial light, are used to collect the data to be classified. After choosing a specific range of scales, scale-averaged wavelet spectrums extracted from the wavelet power spectrum is used to find features by Back-Propagation(13P) algorithm. As a result, wavelet analysis shows superiority to fourier transform for nonstationary EEG signal classification.

• Journal of Korea Multimedia Society
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• v.21 no.1
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• pp.1-9
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• 2018

Much research has been conducted to judge aesthetic value for a single type of stimuli, but research to determine aesthetic value when two kinds of stimuli are presented at the same time is not explored in depth. In this paper, we measure the difference between the presentation of visual stimuli like general image and the presentation of signboard image including text stimuli using EEG. In the experiment, two oddball tasks were performed for general images and signboard images, and EEG changes according to the aesthetic value of the images were measured. As a result, the change of ERP in signboard image was larger than that of general image. We confirmed that more visual information was received and processed when two stimuli were presented at the same time.

• Science of Emotion and Sensibility
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• v.2 no.1
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• pp.53-60
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• 1999

EEG 생리신호의 분석은 국내에서도 최근에 활발하게 연구가 진행되고 있으나, 시계열을 이용한 분석법은 통계학의 전문적인 지식을 요구하고 있기 때문에 연구에 많은 어려움이 있다. 그러므로 감성과학 연구자들이 보다 쉽게 이해하고 분석할 수 있는 Tool의 개발이 절실히 요구되고 있다. 본 논문에서는 EEG 생리신호 분석을 위한 모형분석 시스템과 생리신호 분류를 위한 판별분류 시스템을 구축하였다. 이 시스템에서는 신호분석을 위한 그래프 작성, 자극 신호에 대한 모형식별 방법의 제시, 모형에 대한 추정 및 진단 기준에 따른 최적의 모형선정 방법 등을 지원한다. 또한 선정된 모형에 이해 모수를 추정하고 이를 이용하여 통계에 대한 지식이 없이도 쉽게 각 뇌파 신호들을 판별 분류할 수 있다.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.12
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• pp.702-707
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• 2003

This study describes a new technique for human sensibility evaluation using electroencephalogram(EEG). Production of EEG is assumed to be linear. The linear predictor coefficients and the linear cepstral coefficients of EEG are used as the feature parameters of sensibility and pattern classification performances of them are compared. Using the better parameter, a human sensibility evaluation algorithm is designed. The obtained results are as follows. The linear predictor coefficients showed the better performance in pattern classification than the linear cepstral coefficients. Then, using the linear predictor coefficients as the feature parameter, a human sensibility evaluation algorithm is developed at the base of a multi-layer neural network. This algorithm showed 90% of accuracy in comfortableness evaluation in spite of fluctuations in statistics of EEG signal.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1997.11a
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• pp.179-182
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• 1997

In this study, the pre-amplifiers were manufac-tured, which correspinds with the properties of signal source, For measuring the EMG, EEG, ECG and EOG's signal, which are generated at human body, the pre-amplifiers were manufactured in this studywhich was corresponding with the propertiies of dach signal source. So as to do, the bandwidth of filters and the amplitude of amplifiers were adaptively adjusted, according to signal source. Then, the usefulness was represented by showing the measured examples.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.947-950
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• 2015

In the cause of car accidents in Korea, drowsy driving has shown that it is larger fctors than drunk driving. Therefore, in order to prevent drowsy driving accidents, drowsiness detection and warning system for drivers has recently become a very important issue. Furthermore, Many researches have been published that measuring alpha wave of EEG signals is the effective way in order to be aware of drowsiness of drivers. In this study, we have developed EEG measuring device that applies a signal processing algorithm using the LabView program for detecting drowsiness. According to results of drowsiness inducement experiments for small test subjects, it was able to detect the pattern of EEG, which means drowsy state based on the changing of power spectrum, counterpart of alpha wave. After all, Comparing to the results of drowsiness pattern between commercial equipments and developed device, we could confirm acquiring similar pattern to drowsiness pattern. With this results, the driver's drowsiness prevention system expect that it will be able to contribute to lowering the death rate caused by drowsy driving accidents.

• Anxiety and mood
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• v.20 no.1
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• pp.8-16
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• 2024

Objective : The purpose of this study is to compare the signal obtained from the frontal 2-electrodes EEG with that obtained from the temporal, central, and parietal 2 electrodes. Methods : EEGs were recorded in a total of 67 patients with major depressive disorder (MDD), 104 patients with schizophrenia (SCZ), and 29 patients with Alzheimer's disease (AD). For each disease group, there were healthy controls (HC) that were paired accordingly (HC1=69, HC2=104, HC3=27). The following measurements were compared across electrodes: band power, alpha peak frequency (APF), APF power, alpha asymmetry (AA), and Kolmogorov complexity (KC). Results : Statistically significant differences were found in band power measured from frontal electrodes compared to electrodes placed in other locations. Specifically, the power of theta waves was measured higher in the temporal electorodes, alpha 1 and alpha 2 waves in the parietal, beta 1 and beta 2 in the central, and gamma waves in the temporal electrodes. Both SCZ and AD patients showed increased theta power in all electrodes. In SCZ patients, APF decreased in the central and temporal electrodes, but the APF power analysis showed no difference between the patients and controls. Additionally, AD patients exhibited increased AA in the central EEG, while SCZ patients showed decreased KC in the parietal and temporal electrodes. Conclusion : Depending on the electrode location, sensitive EEG frequencies differed. Compared with signals from other electrodes, frontal EEG in MDD patients revealed generally constant signal values, though the temporo-parieto-central electrodes appeared to be more reliable in SCZ and AD patients.

• Progress in Medical Physics
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• v.13 no.3
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• pp.156-162
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• 2002

In this study, we attempted in preparing high precision EEG measuring equipment. To measure EEG in high efficiency, pre-amplifier should get high performance common mode rejection ratio. Also, separation amplifier is essential to eliminate common line noise. So, our study were pointed at elevating the efficiency of eliminating noise, user safety and low noise characteristics. Prepared high precision pre-amplifier for EEG was A/D converted to automatically classify $\alpha$ wave, $\beta$ wave and $\theta$ wave. And converted data were Fast Fourier Transformed with real time DSP (Digital Signal Processing). Clinical demonstrations were carried out with healthy students, aged between 20 to 26 who has no histories of illness. To recognize the efficiency of the EEG, prepared EEG were used with MS equipment in low stimulated state and high stimulated state. Then, we studied at the effect of sensitivity on brain wave. From this study, it is known that our EEG equipment is efficient in sensitivity evaluation and suitable stimulations for each psychological state are required.

• Journal of KIISE:Software and Applications
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• v.27 no.1
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• pp.24-32
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• 2000

BCI(Brain-Computer Interface) is studied to control the machines with brain. In this study, an EEG(Electroencephalography) signal classification model is proposed. The model gets EEG pattern from each subject's brain and extracts characteristic features. The model discriminates the EEG patterns by using those extracted characteristic features of each subject. The proposed method classifies each pair of the given tasks and combines the results to give the final result. Four tasks such as rest, movement, mental-arithmetic calculation and point-fixing were used in the experiment. Over 90% of the trials, the model yielded successful results. The model exploits characteristic features of the subjects and the weight table that was produced after training. The analysis results of the model such as its high success rates and short processing time show that it can be used in a real-time brain-computer interface system.

• Journal of IKEEE
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• v.14 no.2
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• pp.16-23
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• 2010

Since the interest of a brain science research is increased recently, various devices using brain waves have been developed in the field of brain training game, education application and brain computer interface. In this paper, we have developed a portable EEG measurement and a bluetooth based wireless transmission device measuring brain waves from the frontal lob simply and conveniently. The low brain signals about 10~100${\mu}V$ was amplified into several volts and low pass, high pass and notch filter were designed for eliminating unwanted noise and 60Hz power noise. Also, PIC24F192 microcontroller has been used to convert analog brain signal into digital signal and transmit the signal into personal computer wirelessly. The sampling rate of 1KHz and bluetooth based wireless transmission with 38,400bps were used. The LabVIEW programing was used to receive and monitor the brain signals. The power spectrum of commercial biopac MP100 and that of a developed EEG system was compared for performance verification after the simulation signals of sine waves of $1{\mu}V$, 0~200Hz was inputed and processed by FFT transformation. As a result of comparison, the developed system showed good performance because frequency response of a developed system was similar to that of a commercial biopac MP100 inside the range of 30Hz specially.