• The Transactions of the Korea Information Processing Society
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• v.6 no.5
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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.

• Science of Emotion and Sensibility
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• v.14 no.2
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• pp.311-320
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• 2011

To quantitatively analyze the effects of color stimulation which is one of the major affecting factors on human emotion, we studied the relationship between color preference and the Electroencephalography (EEG) to 3 color stimuli; bright yellow red (BYR), deep green yellow (DGY), and vivid blue (VB). Physiological signal measured by EEG on the color stimulation was closely related with their well-known colorful images. The brain become more activated with decreasing the color temperature (BYR${\geq}$DGY>VB), and the right brain is more sensitive than the left. On the whole, the EEG values of the frequency bands are in order to beta ${\geq}$ theta and alpha > gamma. As decreasing the color temperature, beta wave increased (BYR${\geq}$DGY>VB), and alpha, beta and gamma waves increased with increasing the color temperature (BYR${\geq}$DGY>VB). The relationship between the color preference and EEG values showed EEG gets more activated at some frequency bands when the color preference becomes higher. In conclusion, the specific frequency band could be activating by a color stimuli which had showed higher the preference. It means that these color stimuli can apply for various industries such as beauty industry, interior design, fashion design, color therapy, and etc.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.1088-1090
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• 2017

최근 국내에서는 상용화된 뇌파기반 인터페이스(BCI) 구현을 위한 연구가 활발히 진행되고 있다. 이에 본 논문에서는 안전한 비침습형 뇌파 측정 방법을 사용하여 뇌전도(EEG)를 측정하고 증폭된 데이터를 사용하여 RC카의 4방향 제어가 가능한 알고리즘을 제안한다. 뇌파측정기로 수집된 데이터 셋은 고속 푸리에 변환을 거쳐 사전 정의된 7가지 뇌파의 필터를 통해 집중도와 이완도를 검출하게 된다. 검출된 데이터는 아두이노 우노에 연결된 원격컨트롤러를 통하여 RC카의 전진 및 후진 제어를 담당한다. 또한 추가로 설치된 자이로센서를 통해 입력된 전자신호는 칼만 필터를 이용하여 좌회전 및 우회전 제어를 담당한다. 훈련된 실험자에 의해 실내 외에서 검출된 뇌파가 각기 다른 특성과 머리 회전만으로 상황을 구분하여 RC카 제어를 할 수 있음을 확인하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06c
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• pp.331-334
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• 2011

입력 데이터의 공통적인 특징을 찾아내는 방법은 기계 학습 분야의 중요한 분야이다. 일반적으로 입력 데이터의 형태적 패턴을 찾아내는 알고리즘들이 많이 연구되었는데, 최근에는 데이터의 입력 순서 또는 데이터 사이의 시간적 인과 관계와 같이 시간에 연관된 패턴을 찾는 방법이 주목을 받고 있다. 우리는 형태적 혹은 공간적 패턴 탐색에 뛰어난 성능을 보이는 하이퍼네트워크 모델을 확장하여 입력 데이터의 시공간적 패턴을 찾는 방법을 제시한다. 하이퍼네트워크는 두 개 이상의 변수를 하나의 엣지로 연결하여 문제공간을 탐색하는 모델로, 시간과 공간의 변수를 동시에 고려하여 데이터의 특성을 찾아내는 데에 적합하다. 이를 확인하기 위하여 사람의 EEG 신호를 분석하였는데, 시각적인 정보를 처리할 때와 언어적 정보를 처리할 때의 특징적인 패턴들을 찾았다.

• Science of Emotion and Sensibility
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• v.2 no.1
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• pp.147-155
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• 1999

온열쾌적감에 영향을 주는 중요한 요인들로는 온도, 습도, 기류 등의 물리적 요인과 성별이나 체질 등 뿐만 아니라 온열환경에서 느끼는 인간의 감성적인 측면도 요인으로 작용한다. 본 연구에서는 여러 가지 온열 환경 중에서 실내의 상하온도차와 기류방식의 제어에 따른 생체반응의 변화, 및 감성의 변화를 관찰하여 온열환경에 따른 인간의 온열쾌적감을 평가하기 위해 생리신호를 측정, 분석하였다. 인간에게 가장 쾌적함을 주는 최적의 실내 상하온도차와 기류제어방식을 구현하기 위한 평가방법으로 MST(mean skin temperature)분석 및 HRV(heart rate variability) 분석과 EEG 주파수 스펙트럼 분석을 시행하였다. 그 결과 실내의 상하온도차는 23$^{\circ}C$의 머리부위 온도에서 발 부위와의 온도차가 -3$^{\circ}C$일 때 가장 쾌적한 조건으로 나타났고, 기류제어방식은 감성기류조건에서 가장 쾌적함을 보였다. 본 연구를 통해 실내의 상하온도차와 기류방식에 대한 온열환경의 쾌적조건을 설정하였고, HRV 분석과 EEG의 주파수 분석이 주판신소설문평가와 유의한 결과를 나타내어 이러한 생리신호의 분석이 인간의 감성적 측면을 고려한 온열쾌적성을 펑가하는데 보다 객관적이고 신뢰성 있는 평가지표로 이용될 수 있음을 제시하였다.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.29 no.3
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• pp.134-141
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• 2017

The purpose of this study was to analyze physiological changes via a cold air reaction experiment to generate basic data that are useful for the development of an automobile active air conditioning system to prevent drowsiness. The $CO_2$ concentration causing drowsiness in vehicle operation was kept below a certain level. Air was blown to the driver's face by using an indoor air cooling apparatus. Sleepiness and the arousal state of the driver in cold wind were measured by physiological signals. It was evident in the EEG that alpha waves decreased and beta waves increased, caused by cold air stimulation. The ${\alpha}/{\beta}$ ratio was reduced by about 52.9% and an alert state confirmed. In the electrocardiogram analysis, the efficiency of cold air stimulation was confirmed by the mean heart rate interval change. The R-R interval had a delay time of about one minute compared to the EEG response. The findings confirmed an arousal effect from sleepiness due to cold air stimulation.

• Proceedings of the KOSOMBE Conference
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• v.1994 no.05
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• pp.49-52
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• 1994

We have developed a biological signal measurement, archiving, and communication system (SiMACS). The front end of the system is the intelligent data processing unit (IDPU) which includes ECG, EEG, EMG, blood pressure, respiration, temperature measurement modules, module control and data acquisition unit, real-time display and signal processing unit. IDPUS are connected to central data base unit through LAN(Ethernet). Workstations which receive signals from central DB and provide various signal analysis tools are also connected to the network. The developed PC-based SiMACS is described.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.10
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• pp.207-216
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• 2013

The aim of this study is to design a classifier for sleep stage analysis and select important feature set which shows sleep stage well based on physiological signals during sleep. Sleep has a significant effect on the quality of human life. When people undergo lack of sleep or sleep-related disease, they are likely to reduced concentration and cognitive impairment affects, etc. Therefore, there are a lot of research to analyze sleep stage. In this study, after acquisition physiological signals during sleep, we do pre-processing such as filtering for extracting features. The features are used input for the new combination algorithm using genetic algorithm(GA) and neural networks(NN). The algorithm selects features which have high weights to classify sleep stage. As the result of this study, accuracy of the algorithm is up to 90.26% with electroencephalography(EEG) signal and electrocardiography(ECG) signal, and selecting features are alpha and delta frequency band power of EEG signal and standard deviation of all normal RR intervals(SDNN) of ECG signal. We checked the selected features are well shown that they have important information to classify sleep stage as doing repeating the algorithm. This research could use for not only diagnose disease related to sleep but also make a guideline of sleep stage analysis.

• Journal of IKEEE
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• v.25 no.4
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• pp.679-688
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• 2021

Gait is an evaluation index used in various clinical area including brain nervous system diseases. Signal source localizing and time-frequency analysis are mainly used after extracting independent components for Electroencephalogram data as a method of measuring and analyzing brain activation related to gait. Existing treadmill-based walking EEG analysis performs signal preprocessing, independent component analysis(ICA), and source localizing by merging data after the multiple EEG measurements, and extracts representative component clusters through inter-subject clustering. In this study we propose an analysis method, without merging to single dataset, that performs signal preprocessing, ICA, and source localization on each measurements, and inter-subject clustering is conducted for ICs extracted from all subjects. The effect of data merging on the IC clustering and time-frequency analysis was investigated for the proposed method and two conventional methods. As a result, it was confirmed that a more subdivided gait-related brain signal component was derived from the proposed "non-merging" method (4 clusters) despite the small number of subjects, than conventional method (2 clusters).

• Journal of Biomedical Engineering Research
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• v.28 no.2
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• pp.287-293
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• 2007

The researchers have studied for a long time about the depth of anesthesia but they don't make criteria for the depth of anesthesia. Anesthetists can't make a prediction about patient's reaction. Therefore, patients have potential risk such as poisonous side effect, late-awake, early-awake and strain reaction. In this study, the distributed characteristics on the bispectrum and bicoherence, the type of nonlinear signal processing, as a result of the coupling of EEG were presented according to depth of anesthesia. These results were consistent with a trend of delta ratio that the index of evaluation for the depth of anesthesia. The higher-order spectrum (HOS), the bispectrum and bicoherence, gives the useful information about depth of anaesthesia than other indexes.