• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.189-192
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• 2001

뇌 기능의 연구수단으로써 널리 사용되고 있는 뇌파(Electroencephalogram)는 측정시에 노이즈(noise)나 잡파(Artifacts)가 섞여서 측정되기 쉽다. 이러한 노이즈나 잡파들을 제거하기 위하여 미지의 혼합된 신호들을 분리하는데 적용되고 있는 통계적인 처리 방식인 독립성분분석(ICA) 알고리즘을 뇌파에 적용하여 그 결과를 알아보았다. 본 연구에서는 정상인의 안구운동(Eye Movement)상태의 뇌파 신호에 대해서 독립성분분석을 적용하여 안구운동과 관련된 잡파가 포함된 원래의 뇌파신호(Original EEG Signal)와 제거한 다음의 뇌파신호(Corrected EEG Signal)에 대하여 비선형 분석법을 사용하여 두 신호의 유의한 차이점을 밝히고, 분리된 독립 신호들의 해부학적 발생위치 및 분포를 추정하였다.

• Sleep Medicine and Psychophysiology
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• v.16 no.1
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• pp.16-21
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• 2009

In accordance with the development of EEG and polysomnography in the field of sleep research, the sleep onset period (SOP) between wakefulness and sleep has been considered an important part for understanding the physiology of sleep. SOP in the transition from wakefulness to sleep is a gradual process integrating various viewpoints such as behavior, EEG, physiology and subjective report. Particularly, based on understanding of EEG changes during sleep, SOP has been regarded as a pattern of topographical change in specific frequency and specific state in EEG. Studies on quantitative EEG (qEEG) and event-related potential (ERP) have suggested that SOP shows the changes of functional coordination at the specific cortical areas in qEEG and the changes of regular patterns in response to environmental stimulation in ERP. The development of sleep EEG and topographic mapping of EEG is expected to integrate various viewpoints of SOP and clarify the neurophysiologic mechanism of SOP further.

• Journal of Biomedical Engineering Research
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• v.24 no.6 s.81
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• pp.487-494
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• 2003

The sleep stages provides the most basic evidence for diagnosing a variety of sleep diseases. for staging sleep by analysis of EEG(electroencephalogram), it is especially important to detect the characteristic waveforms from EEG. In this paper, sleep EEG signals were analyzed using Fourier transform and continuous wavelet transform as well as discrete wavelet transform. Proposeed system methods. Fourier and wavelet for detecting of important characteristic waves(hump, sleep spindles. K-complex, hill wave, ripple wave) in sleep EEG. Sleep EEG data were analysed using Daubechies wavelet transform method and FFT method. As a result of simulation, we suggest that our neural network system attain high performance in classification of characteristic waves.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.6
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• pp.832-837
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• 2007

Many researchers are studying brain activity to using functional Magnetic Resonance Imaging (fMRI), Time Resolved Spectroscopy(TRS), Electroencephalography(EEG), and etc. They are used detection of seizures or epilepsy and deception detection in the main. In this paper, we focus on emotion recognition by recording brain waves. We specially use fMRI, TRS, and EEG for measuring brain activity Researchers are experimenting brain waves to get only a measuring apparatus or to use both fMRI and EEG. This paper is measured that we take images of fMRI and TRS about brain activity as human emotions and then we take data of EEG signals. Especially, we focus on EEG signals analysis. We analyze not only original features in brain waves but also transferred features to classify into five sections as frequency. And we eliminate low frequency from 0.2 to 4Hz for EEG artifacts elimination.

• Journal of Internet of Things and Convergence
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• v.9 no.1
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• pp.25-31
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• 2023

The brain-machine interface(BMI) is a next-generation interface that controls the device by decoding brain waves(also called Electroencephalogram, EEG), EEG is a electrical signal of nerve cell generated when the BMI user thinks of a command. The brain-machine interface can be applied to various smart devices, but complex computational process is required to decode the brain wave signal. Therefore, it is difficult to implement a brain-machine interface in an embedded system implemented in the form of an edge device. In this study, we proposed a new type of brain-machine interface system using IoT technology that only measures EEG at the edge device and stores and analyzes EEG data in the cloud computing. This system successfully performed quantitative EEG analysis for the brain-machine interface, and the whole data transmission time also showed a capable level of real-time processing.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.4
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• pp.427-432
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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 ate 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 Korean Ophthalmic Optics Society
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• v.5 no.2
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• pp.5-9
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• 2000

The visual evoked potential was electrophysiological method for the identify of the EEG response on visual cortex. This test was objective test method on the eye function. This study was used the visual evoked potential for the objective color test. The subjects was a normal color function in Korean adults. The test condition was performed on the differens distance and illumination. According to convergence condition of color vision target. On the appearance of EEG wave of visual stimulation on visual cortex. The most EEG wave style was delta wave, and the next amount wave form was beta wave and theta wave, and the least EEG wave form was alpha wave. The histogram of amplitude of EEG wave form was almost non-Gaussian shape, and the phase diagram of amplitude was almost all linear shape. On the kinds of color vision target, the frequency of EEG wave style appeared a similar results.

• Sleep Medicine and Psychophysiology
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• v.14 no.1
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• pp.42-48
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• 2007

Introduction: Detrended fluctuation analysis (DFA) is used as a way of studying nonlinearity of EEG. In this study, DFA is applied on sleep EEG of normal subjects to look into its nonlinearity in terms of EEG channels and sleep stages. Method: Twelve healthy young subjects (age：$23.8{\pm}2.5$ years old, male：female=7：5) have undergone nocturnal polysomnography (nPSG). EEG from nPSG was classified in terms of its channels and sleep stages and was analyzed by DFA. Scaling exponents (SEs) yielded by DFA were compared using linear mixed model analysis. Results: Scaling exponents (SEs) of sleep EEG were distributed around 1 showing long term temporal correlation and self-similarity. SE of C3 channel was bigger than that of O1 channel. As sleep stage progressed from stage 1 to slow wave sleep, SE increased accordingly. SE of stage REM sleep did not show significant difference when compared with that of stage 1 sleep. Conclusion: SEs of Normal sleep EEG showed nonlinear characteristic with scale-free fluctuation, long-range temporal correlation, self-similarity and self-organized criticality. SE from DFA differentiated sleep stages and EEG channels. It can be a useful tool in the research with sleep EEG.

• 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.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.5
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• pp.589-594
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• 2014

It has been known that the particular brain waves are induced when a human concentrates. In our study, we aimed to analysis the brain waves related to human concentration using visual stimulus to induce the concentration. The visual stimulus tasks were presented to subjects for concentration. We measured EEG signals with several channels and analyzed the signals into several frequency bands. In the measured EEG signals, we analyzed to focus on theta waves, SMR waves and mid-beta waves. Therefore we presented the results to investigate characteristics of the EEG signals related to the human concentration.