• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1996년도 추계학술대회
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• pp.233-236
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• 1996

We are exposed to the various external stimuli input from the environment, which cause emotional changes based on the characteristics of the stimuli. Unfortunately, there are no quantitative results on relationship between human sensibility and the characteristics of physiological signals. The objective of this study was to quantify EEG signals evoked by auditory stimulation based on the assumption that the analysis of the variability on the characteristics of the EEG waveform may provide the significant information regarding changes in psychological states of the subject. The experiment was devised with seven experimental conditions, which are control and six different types of auditory stimulation. Twenty subjects were used to obtain EEGs while introducing auditory stimulation. Wavelet transformation was employed to analyze the EEG signals. The results showed that the reconstructed signals at the decomposition level revealed the different energy value on the EEG signals. Also, general patterns of EEG signals in rest state compare with negative and positive stimulus were found. This study could be extended to estabilish an algorithm which distinguishes psychophysiological states of the subjects exposed to the auditory stimulation.

• 한국전자통신학회논문지
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• 제9권11호
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• pp.1277-1282
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• 2014

본 논문에서는 청소년의 집중과 관련된 EEG신호를 분석하는 것을 목적으로 하여 글자 시각자극 과제로 제시했을 때 유발되는 EEG신호를 분석한 결과를 제시한다. 시각자극 과제는 글 속에서 틀린 조사들을 찾는 것이다. 본 실험에서는 선행연구결과에 따라 EEG신호 중에서도 특히 SMR파와 중간 베타파를 분석하는데 초점을 맞추었다. 실험결과로서 피험자의 집중력과 상관성이 높은 채널의 위치와 중간 베타파 대역을 제시하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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• pp.52.5-52
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• 2002

In this paper, we develop analysis models for classification of temporal data from human subjects. The study focuses on the analysis of electroencephalogram (EEG) signals obtained during various emotional states. We demonstrate a generally applicable method of removing EOG and EMG artifacts from EEGs based on independent component analysis (ICA). All EEG channel maps were interpolated from 10 EEG subbands. ICA methods are based on the assumptions that the signals recorded on the scalp are mixtures of signals from independent cerebral and artifactual sources, that potentials arising from different parts of the brain, scalp and body are summed linearly at the electrodes and that prop...

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제4권2호
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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.

• 대한한의진단학회지
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• 제11권2호
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• pp.84-95
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• 2007

Background and Purpose: We have studied the trends of EEG signals in the voluntary breathing condition by applying the fractal analysis. According to chaos theory, irregularity of EEG signals can result from low dimensional deterministic chaos. A principal parameter to quantify the degree of Chaotic nonlinear dynamics is correlation dimension. The aim of this study was to analyze correlation between the correlation dimension of EEG and HRV(heart rate variability). We have studied the trends of EEG signals in the voluntary breathing condition by applying the fractal analysis. Methods: EEG raw data were measured by moving windows during 15 minutes. Then, the correlation dimension(D2) was calculated by each 40-seconds-segment in 15 minutes data, totally 36 segments. 8 channels EEG study on the Fp, F, T, P was carried out in 30 subjects. Results and Conclusion: Correlation analysis of HRV was calculated with deterministic non-linear data and stochastic non-linear data. 1. Ch1(Fp1), Ch4(F3), Ch4(F4) is positive correlated with In LF. 2. Ch1(Fp1), Ch3(F3) is positive correlated with In TF.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.450-452
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• 2004

Every movement, perception and thought we perform is associated with distinct neural activation patterns. Neurons in the brain communicate with each other by sending electrical impulses that produce currents. These currents give rise to electrical fields that can be measured outside the head. It shows some variation on the electroencephalographic signals. In recent devices, the EEG signals measured from head surface are a sum of all the momentary brain activation. With these EEG signals, it is difficult to distinguish the patterns correlated with a certain event from the signals. However, the system must discriminate some patterns with some events especially for any kind of device as a brain control interface system. In this experiment, the sensory-motor cortex of humans has been extensively studied. Activation related to several movements on both sides of the sensory-motor cortices in imaginary. The activation patterns during imagination of several movements resemble the activation patterns during preparation of movements. The result represents the system based on the optimal filters discriminated at least 60% of mental imageries.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 하계종합학술대회 논문집(5)
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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.

• 한국정보기술응용학회:학술대회논문집
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• 한국정보기술응용학회 2002년도 추계공동학술대회 정보환경 변화에 따른 신정보기술 패러다임
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• pp.461-467
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• 2002

뇌에서 얻은 시계열 신호들은 대부분 불규칙하고 복잡한 파형을 가지고 있으며, 1980년대 중반까지만 해도 이러한 신호들은 확률론 과정(stochastic process)으로 발생된 '소음'(noise)으로 여겨졌다. 하지만 최근 들어 뇌에 관한 연구가 활발히 진행되었고 EEG를 이용한 생체신호의 비선형분석에 관한 연구가 진행되면서 뇌에서 발생되는 신호는 의미 있는 신호로 분석되어지고 있다. 이에 비선형 분석방법인 상관차원을 이용하여 라벤더 향기 전과 향기 후의 뇌파의 변화를 분석하고, 주파수 대역별로 delta파, theta파, alpha파, beta파로 나누어서 라벤더 향이 뇌에 미치는 영향을 분석한다. 즉, 뇌에서 발생되는 신호의 특징을 찾기 위해 다른 향보다 강하게 반응하는 라벤더 향을 후각자극으로 제시하여 EEG를 측정한 후, 16채널에 대한 상관차원을 구하고 라벤더 향이 뇌에 미치는 영향을 분석한다.

• 한국산업정보학회:학술대회논문집
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• 한국산업정보학회 2002년도 추계공동학술대회
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• pp.461-467
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• 2002

뇌에서 얻은 시계열 신호들은 대부분 불규칙하고 복잡한 파형을 가지고 있으며, 1980년대 중반까지만 해도 이러한 신호들은 확률론 과정(stochastic process)으로 발생된 '소음'(noise)으로 여겨졌다. 하지만 최근 들어 뇌에 관한 연구가 활발히 진행되었고 EEG를 이용한 생체신호의 비선형 분석에 관한 연구가 진행되면서 뇌에서 발생되는 신호는 의미 있는 신호로 분석되어지고 있다. 이에 비선형 분석방법인 상관차원을 이용하여 라벤더 향기 전과 향기 후의 뇌파의 변화를 분석하고, 주파수 대역별로 delta파, theta파, alpha파, beta파로 나누어서 라벤더 향이 뇌에 미치는 영향을 분석한다. 즉, 뇌에서 발생되는 신호의 특징을 찾기 위해 다른 향보다 강하게 반응하는 라벤더 향을 후각 자극으로 제시하여 EEG를 측정한 후, 16채널에 대한 상관차원을 구하고 라벤더 향이 뇌에 미치는 영향을 분석한다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 D
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• pp.2699-2701
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• 2004

This paper describes a new algorithm for human sensibility evaluation using two personality-group templates of electroencephalogram (EEG) signals. EEG signals of two groups arc collected in relaxed state, comfortable state and uncomfortable state. First of all, the characteristics of EEGs in relaxed state for two groups are compared. After verification of the results, an algorithm for sensibility evaluation is developed. In comparison of the characteristics for two personality-group EEG signals. there are distinct difference between the EEG patterns of the extrovert and the introvert. Upon these findings, the algorithm for human sensibility evaluation is designed. The results of the algorithm showed 90.0% of coincidence with given tasks. This seems to be compromising results for subject independent sensibility evaluation using EEG signal.