• Journal of the HCI Society of Korea
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• v.9 no.1
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• pp.11-21
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• 2014

We present an experimental framework to analyze the physical reactions and causal factors of Visually Induced Motion Sickness (VIMS) using electroencephalography (EEG) signals and vital signs. We studied eleven subjects who are voluntarily participated in the experiments and conducted online and offline surveys. In order to simulate videos including global motions that could cause the motion sickness, we extracted global motions by optical flow estimation method from hand-held captured video recordings containing intense motions. Then, we applied the extracted global motions to our test videos with action movies and texts. Each genre of video includes three levels of different motions depending on its intensity. EEG signal and vital sign that were measured by a portable electrocorticography device and an electronic monometer in real time while the subjects watch the videos including ones with the extracted motions. We perform an analysis of the EEG signals using Distance Map(DM) calculated by correlation among each channel of brain signal. Analysis using the vital signs and the survey results is also performed to obtain relationship between the VIMS and causal factors. As a result, we clustered subjects into three groups based on the analysis of the physical reaction using the DM and the correlation between vital sign and survey results, which shows high relationships between the VIMS and the intensity of motions.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2000.11a
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• pp.69-72
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• 2000

색채 자극은 인간의 감성에 영향을 주며 또한 색채의 생리적인 작용을 응용하는 연구들이 보고되고 있다. 본 연구의 목적은 색채 자극이 정상인의 생리신호에 어떠한 영향을 미치는가를 정량적으로 측정해 보고자 하였다. 피검자는 정상인 성인 남,여 각 50명을 실시하였다. 색채 환경의 제시는 암실에서 백색광원에 채색 필터를 사용하여 제시하였다. EEG는 푸른색과 녹색에서 알파파가 상승하였으며, 베타파는 상대적으로 감소하였다. 심전도의 HRV분석에서는 남자는 녹색에서 부교감 신경계가 가장 활성화 되었으며, 여자는 파랑색에서 부교감신경계가 활성화 된 걸 볼 수 있었다. COP 측정에서는 녹색에서 누적거리가 가장 적은 것으로 나타났으나 통계적 유의성은 나타나지 않았다. 본 실험을 통해 색채 환경이 생리적 신호에 영향을 미치는 것을 알 수 있었으며, 임상적 응용을 위해서는 더 많은 피검자와 인지기능에 장애가 있는 환자를 대상으로 계속적인 연구가 필요한 것으로 사료된다.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1997.11a
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• pp.90-93
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• 1997

외부환경의 변화에 의한 감성의 상태를 특정 자극을 인식한 후 나타나는 생리신호의 정량적인 분석기법을 개발함으로써 감성과 생리신호간의 상관관계를 찾고자 하는 연구가 절실히 요구되고 있다. 본 연구에서의 자극은 모터를 이용하는 자극기를 제작하여 부드러운 천과 거친사포를 피검자의 왼손바닥에 제시하였다. 그리고, 피검자에게 피부자극을 제시할 때 피검자가 자극의 특성을 인식하는 과정에서 발생되는 뇌파를 검출, 분석하고자 하였다. 분석방법으로서는 nonstationary한 신호분석에 유용한 특성을 가지고 있는 wavelet변환을 이용한 시간-주파수 EEG 신호의 특정범위에서의 시간-주파수 에너지성분의 변화를 관찰하였다. 그 결과 무자극시의 뇌파와 자극시으 뇌파를 비교하였을 때 부드러운 자극을 제시한 경우에는 낮은 주파수 대역에서 에너지가 우세함을 보였으며, 거친 자극에 대해서는 20-30Hz대역에서의 우세한 에너지 분포가 관찰되었다.

• 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.

• Journal of KIISE
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• v.44 no.6
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• pp.587-594
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• 2017

Recently, motor imagery electroencephalogram(EEG) based Brain-Computer Interface(BCI) systems have received a significant amount of attention in various fields, including medicine and engineering. The Common Spatial Pattern(CSP) algorithm is the most commonly-used method to extract the features from motor imagery EEG. However, the CSP algorithm has limited applicability in Small-Sample Setting(SSS) situations because these situations rely on a covariance matrix. In addition, large differences in performance depend on the frequency bands that are being used. To address these problems, 4-40Hz band EEG signals are divided using nine filter-banks and Regularized CSP(R-CSP) is applied to individual frequency bands. Then, the Mutual Information-Based Individual Feature(MIBIF) algorithm is applied to the features of R-CSP for selecting discriminative features. Thereafter, selected features are used as inputs of the classifier Least Square Support Vector Machine(LS-SVM). The proposed method yielded a classification accuracy of 87.5%, 100%, 63.78%, 82.14%, and 86.11% in five subjects("aa", "al", "av", "aw", and "ay", respectively) for BCI competition III dataset IVa by using 18 channels in the vicinity of the motor area of the cerebral cortex. The proposed method improved the mean classification accuracy by 16.21%, 10.77% and 3.32% compared to the CSP, R-CSP and FBCSP, respectively The proposed method shows a particularly excellent performance in the SSS situation.

• Journal of Korea Multimedia Society
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• v.21 no.5
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• pp.609-616
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• 2018

In this paper, we designed a multimodal bio-signal measurement system to observe changes in the brain nervous system and vascular system during sleep. Changes in the nervous system and the cerebral blood flow system in the brain during sleep induce a unique correlation between the changes in the nervous system and the blood flow system. Therefore, it is necessary to simultaneously observe changes in the brain nervous system and changes in the blood flow system to observe the sleep state. To measure the change of the nervous system, EEG, EOG and EMG signal used for the sleep stage analysis were designed. We designed a system for measuring cerebral blood flow changes using functional near-infrared spectroscopy. Among the various imaging methods to measure blood flow and metabolism, it is easy to measure simultaneously with EEG signal and it can be easily designed for miniaturization of equipment. The sleep stage was analyzed by the measured data, and the change of the cerebral blood flow was confirmed by the change of the sleep stage.

• 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.

• Korean Journal of Biological Psychiatry
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• v.5 no.1
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• pp.95-101
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• 1998

The changes of electroencephalogram(EEG) in patients with dementia of Alzheimer's type are most commonly studied by analyzing power or magnitude in traditionally defined frequency bands. However because of the absence of an identified metric which quantifies the complex amount of information, there are many limitations in using such a linear method. According to the chaos theory, irregular signals of EEG can be also resulted from low dimensional deterministic chaos. Chaotic nonlinear dynamics in the EEG can be studied by calculating the largest Lyapunov exponent($L_1$). The authors have analyzed EEG epochs from three patients with dementia of Alzheimer's type and three matched control subjects. The largest $L_1$ is calculated from EEG epochs consisting of 16,384 data points per channel in 15 channels. The results showed that patients with dementia of Alzheimer's type had significantly lower $L_1$ than non-demented controls on 8 channels. Topographic analysis showed that the $L_1$ were significantly lower in patients with Alzheimer's disease on all the frontal, temporal, central, and occipital head regions. These results show that brains of patients with dementia of Alzheimer's type have a decreased chaotic quality of electrophysiological behavior. We conclude that the nonlinear analysis such as calculating the $L_1$ can be a promising tool for detecting relative changes in the complexity of brain dynamics.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.11
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• pp.206-216
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• 2013

The emotion plays a critical role in human's daily life including learning, action, decision and communication. In this paper, emotion discrimination classifier is designed to reduce system complexity through reduced selection of dominant features from biosignals. The photoplethysmography(PPG), skin temperature, skin conductance, fontal and parietal electroencephalography(EEG) signals were measured during 4 types of movie watching associated with the induction of neutral, sad, fear joy emotions. The genetic algorithm with support vector machine(SVM) based fitness function was designed to determine dominant features among 24 parameters extracted from measured biosignals. It shows maximum classification accuracy of 96.4%, which is 17% higher than that of SVM alone. The minimum error features selected are the mean and NN50 of heart rate variability from PPG signal, the mean of PPG induced pulse transit time, the mean of skin resistance, and ${\delta}$ and ${\beta}$ frequency band powers of parietal EEG. The combination of parietal EEG, PPG, and skin resistance is recommendable in high accuracy instrumentation, while the combinational use of PPG and skin conductance(79% accuracy) is affordable in simplified instrumentation.

• Journal of Biomedical Engineering Research
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• v.22 no.1
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• pp.81-90
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• 2001

본 연구에서는 디지털 영상 뇌파계(digital video electroencephalogram, Digital VEEG)에서 비디오 영상과 뇌전도 파형의 동기화된 편집 시스템을 구성한다. 이 시스템은 기존 아날로그 영상 뇌파계(analog video electroencephalogram)의 동기화 문제와 디지털 영상 시스템에서의 영상편집 문제를 해결하기 위하여 MPEG-I(이하 MPEG) 고압축 기술을 이용한 MPEG 인코딩 보드(encoding board)와 MPEG 편집 엔진(editing engine)을 각각 사용하였다. 시스템은 디지털 영상뇌파계모듈과 디지털 편집 모듈로 구성되며, 뇌전도모듈에서는 환자에게 연결된 전극을 통해 들어온 뇌파를 생체신호증폭기를 이용하여 증폭한 후 AD 보드(analog to digital board)를 이용 디지털화한다. 디지털 카메라로 촬영된 환자영상의 아날로그 영상신호(NTSC 신호)는 MPEG 인코딩 보드를 이용하여 고압축 디지털화한다. 이후 디지털화된 뇌전도신호와 MPEG 형식의 영상을 시간 동기화하여 두 개의 모니터에 각각보여준다. 편집 모듈에서는 영상신호와 뇌파신호를 어느 부분이든 간단한 조작으로 오려 붙이기(cut and paste) 기능을 이용할 수 있다. 본 시스템은 사용된 데이터 모두 디지털 기술을 이용하여 영상과 뇌파신호의 정확한 동기화 및 각각의 데이터의 오려 붙이기 기능을 가능케 하였으며, 이는 환자의 데이터를 관리 및 보관하는데 있어, 임상의에게 의미 있는 자료만을 모아서 효율적으로 관리할 수 있게 해준다. 이와 같은 장점을 갖는 디지 영상뇌파계 편집시스템을 구현하였다.