• 수면정신생리
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• 제5권2호
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• pp.185-193
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• 1998

연구배경 : 양성 및 음성 정신분열병 환자간의 뇌파를 비선형적으로 분석하고 그 결과를 대조군과 비교하여 뇌파의 비선형 분석을 통한 정신분열병의 병태생리를 이해하기 위하여 양성 정신분열병 환자 8명, 음성 정신분열병 환자 9명과 정상 대조군 8명을 대상으로 하여 16전극에서 뇌파를 기록하여 비선형 분석을 시행하였다. 결과 : 좌측측두부 최대 양수 리아프노프 지수 값이 음성 정신분열병군에서 양성 정신분열병군과 정상 대조군에 비하여 유의하게 낮았으며, 일부 전극에서 양성증상은 좌측 두뇌의 카오스적 성상과 정상관이 있었으며 우측두뇌의 카오스적 성상과는 역상관이 있었다. 결론 : 본 연구결과를 통하여 정신분열병 환자의 두뇌 기능을 조사하는 데에 있어서 카오스적 역동을 응용한 뇌파분석이 임상적 유용성이 있음을 알 수 있었으며, 임상적 변인을 잘 통제한 연구가 필요하다는 것을 확인할 수 있었다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 추계학술대회 학술발표 논문집
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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.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.1844-1847
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• 1997

Assuming that EEG(electroencephalogram), which is generated by a nonlinear electrical of billions of neurons in the brain, has chaotic characteristics, it is confirmend by frequency spectrum analysis, log frequency spectrum analysis, correlation dimension analysis and Lyapunov exponents analysis. Some chaotic characteristics are related to the degree of brain activity. The slope of log frequency spectrum increases and the correlation dimension decreasess with respect to the activities, while the largest Lyapunov exponent has only a rough correlation.

• ETRI Journal
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• 제35권6호
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• pp.1105-1114
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• 2013

Owing to the large number of video programs available, a method for accessing preferred videos efficiently through personalized video summaries and clips is needed. The automatic recognition of user states when viewing a video is essential for extracting meaningful video segments. Although there have been many studies on emotion recognition using various user responses, electroencephalogram (EEG)-based research on preference recognition of videos is at its very early stages. This paper proposes classification models based on linear and nonlinear classifiers using EEG features of band power (BP) values and asymmetry scores for four preference classes. As a result, the quadratic-discriminant-analysis-based model using BP features achieves a classification accuracy of 97.39% (${\pm}0.73%$), and the models based on the other nonlinear classifiers using the BP features achieve an accuracy of over 96%, which is superior to that of previous work only for binary preference classification. The result proves that the proposed approach is sufficient for employment in personalized video segmentation with high accuracy and classification power.

• 감성과학
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• 제1권1호
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• pp.69-78
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• 1998

Sleep deprivation may affect the brain functions such as cognition and consequently, dynamics of the BEG. We examined the effects of sleep deprivation on chaoticity of the EEG. Five volunteers were sleep-deprived over a period of 24 hours They were checked by EEG during two days. thc first day of baseline period and the second day of total sleep deprivation period. EEGs were recorded from 16 channels for nonlinear analysis. We employed a method of minimum embedding dimension to calculate the first positive Lyapunov exponent. Fer limited noisy data, this algorithm was strikingly faster and more accurate than previous ones. Our results show that the sleep deprived volunteers had lower values of the first positive Lyapunov exponent at ten channels (Fp1, F4. F8. T4, T5. C3, C4. P3. P4. O1) compared with the values of baseline periods. These results suggested that sleep deprivation leads to decrease of chaotic activity in brain and impairment of the information processing in the brain. We suggested that nonlinear analysis of the EEG before and after sleep deprivation may offer fruitful perspectives for understanding the role if sleep and the effects of sleep deprivation on the brain function.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1994년도 춘계학술대회
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• pp.39-42
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• 1994

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.3238-3240
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• 2000

본 논문의 목적은 음주섭취로 인한 혈중 알코올 농도에 따른 뇌의 활동도변화를 측정, 분석하는데 있다. 1차원 시계열데이터인 EEG신호는 생체 비선형 동역학 시스템으로부터 발생하는 Deterministic Nonlinear Chaos신호로써 무작위적인 신호와는 구분되어질 수 있다. EEG시계열데이터를 위상공간에 적절한 어트랙터로 재구성하여 상관차원 최대발산지수 등의 카오스 지수들을 추출하여보면 EEG시계열데이터가 무작위적인 계에서 발생하는 랜덤한 신호가 아닌 카오스계에서 기인함을 알 수 있고, 인간의 정신상태에 따른 뇌의 활동도를 정성적, 정량적으로 판별해 볼 수 있다. 이러한 카오스 분석방법을 토대로 음주전의 뇌의 활동도와 음주후 혈중알코올 농도에 따른 뇌의 활동도변화를 EEG의 카오스 지수들의 변화를 통해 분석해 보았다.

• 대한의용생체공학회:의공학회지
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• 제29권4호
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• pp.329-336
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• 2008

Recently, causality analysis of source time series extracted from EEG or MEG signals is becoming of great importance in human brain mapping studies and noninvasive diagnosis of various brain diseases. Two approaches have been widely used for the analyses: one is independent component analysis (ICA), and the other is multiple signal classification (MUSIC). To the best of our knowledge, however, any comparison studies to reveal the difference of the two approaches have not been reported. In the present study, we compared the performance of the two different techniques, ICA and MUSIC, especially focusing on how accurately they can estimate and separate various brain electrical signals such as linear, nonlinear, and chaotic signals without a priori knowledge. Results of the realistic simulation studies, adopting directed transfer function (DTF) and Granger causality (GC) as measures of the accurate extraction of source time series, demonstrated that the MUSIC-based approach is more reliable than the ICA-based approach.

• 한국정보처리학회논문지
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• 제6권5호
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• pp.1312-1322
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• 1999

The world of linearity, which is regular, predictable and irrelevant to time sequence in most natural phenomenon, is a very small part. In fact, signals generated from natural phenomenon with which we're in contact are showed only slight linearity. Therefore it is very difficult to understand and analyze natural phenomenon with only predictable and regular linear systems. Due to these reasons researches concerning non-linear signals that of analysis were excluded being regarded as noise are being actively carried out. Countless signals generated from nonlinear system have the information about itself, and analyzing those signals and get information from it, that will be able to be used effectively in so may fields. Hence, in this paper we used a higher order spectrum, especially the bispectrum. After we prove the validity applying bispectrum to logistic map, which is typical chaotic signal. Subsequently by showing the result applying for actual signal analysis of EEG according to auditory stimuli, we show that higher order spectra is a very useful parameter in analysis of non-linear signals and the result of EEG analysis according to auditory stimuli.

• Annals of Clinical Neurophysiology
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• 제3권1호
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• pp.31-36
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• 2001

Background & Objectives : Fractal Dimension(FD) could be an index of correlation between variable parameters in non-linear chaotic signals. We tried to demonstrate that EEG wave is compatible with chaotic waves by measuring the Lyapunov exponent index and compared the difference of FD between variable age groups(teens, 30's, 50's) Methods : We estimated the Lyapunov exponent index and the FD from digital EEG data among five persons in each normal age groups by using the software which is programmed in our laboratory. Statistical analysis was done with SPSS win 8.0. The statistical differences of Lyapunov exponent index and FD between each electrodes and each age groups were done with ANOVA and paired sample t-test. Result : The Lyapunov exponent indexes were larger than 1 in each electrode and age group. There is no statistical difference in FD between each electrodes and each age groups. Except in 30th age group. In this group the FD of right hemisphere is larger than that of left hemisphere. Conclusion : The result of Lyapunov exponent index means EEG wave is a non-linear chaotic signal. And the results of FD suggest that chaotic parameters of right hemisphere is larger than those of left hemisphere at rest at least in younger people. We think that chaotic parameters can be a useful tool in investigating the variable diseases or brain states.