• Journal of Biomedical Engineering Research
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• v.29 no.4
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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.

• Journal of the Ergonomics Society of Korea
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• v.22 no.2
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• pp.15-28
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• 2003

The one of the most important factors is the platoon design on developing AH3(Advanced Highway System), as it is related to traffic efficiency and drivers' safety. This study was evaluated that how much speed is comfortable for drivers and how long distance is appropriate for vehicular gap of platoon by measuring drivers' physiology signal and sensibility. A fixed-based AHS simulator was developed by using a real vehicle cockpit and the restructured part of Korean highway for human factors evaluation. The EEG(electroencephalogram), ECG (electrocardiogram) and GSR(Galvanic Skin Response) were measured for obtaining drivers' physiology signal according to the change of speed and gap. The brain wave(${\alpha},\;{\beta},\;{\delta},\;{\theta}$) by EEG, the response of the autonomic nervous system. the sympathetic and parasympathetic nervous system, by ECG, and relax-arousal situation by GSR were analyzed. The SD(Semantic Differential) method was also applied to evaluate drivers' sensibility by 5-grade evaluation scale with 96 adjectives. SSQ(Simulator Sickness Questionnaire) was used to measure the simulator sickness of pre and post driving, two times. As the results, drivers were comfortable with 120km/h speed of platoon and lam to 15m vehicular distance. The results of this study may differ from the adaption of the reality because of many parameters. However, the purpose of this study is show to significant results of the drivers' safety and the acceptability of human factors evaluation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.284-285
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• 2016

In this paper, reflecting the index of concentration and real-time EEG measurement, implementation of the game-type content that can be centralized power of training. Implemented content, for more effective training, based on the brain wave difference in each user, by reflecting an indication of concentration, it is possible by level training. In order to evaluate the usefulness of the implemented content, to target the five subjects, is underway to improve training of concentration, through a comparative analysis of the changes in the index of ability to concentrate, to confirm the improvement of the concentration of the user it could be.

• Journal of Biomedical Engineering Research
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• v.19 no.1
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• pp.81-90
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• 1998

Electromagnetic waves may induce various effects on nervous tissues either by thermal or non-thermal mechanisms. This paper intoduces a method to evalute the non-thermal effect to central nervous system by measuring the EEGs of the rabbits treated by nimodipine before exposed to weak microwave field. 20 rabbits were divided into 2 groups and their EEGs were measured after their head section were exposed to 2,450 MHz microwave with the power density of 10 dBm and 20 dBm respectively for 10 minutes and compared with those of the 3rd group of 10 rabbits which were not exposed. The 4th group of 10 rabbits were intravenously given with nimodipine before exposed to 20 dBm field to determine whether this drug would reverse the EEGs changes induced by weak microwave irradiation. As field poser exceeded 20 dBm although no significant physiological changes were observed, total induced EEGs power was remarkably decreased suggesting the presence of CNS activation. Using Fourier analysis on the EEGs signal it was found that remarkable decrease in delta band and increase in the alpha and beta bands in a significant manner(P<0.05) compared to control group. The changes were, however, not reversed by nimodipine-treatment. The effects may be pure thermal in nature because no significant change has been observed in nimodipine treated rabbits.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2011.11a
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• pp.9-10
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• 2011

• Science of Emotion and Sensibility
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• v.4 no.1
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• pp.61-67
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• 2001

This paper described the effect of subjective approach in case of the human sensibility experiments. The procedure is proceeded subjectively. Human faces are selected as the image presentation media. Pleasant and unpleasant images are selected directly by examiner, And also the image presentation system, which is executed with a computer and has the square-type black box monitor equipment, is manufactured. Images are presented with the step-variation time interval technique. questionnaire test and EEG signal detection data are analyzed. The analysis parameters are a “frequency band integral value” and a “band differential variation ratio”. he results show the high sensibility and fast response. The fact that image presenting repetition alleviates is verified.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.853-855
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• 2018

현재 맥파(ECG; Electrocardiogram) 및 뇌파(EEG: electroencephalography)의 파형 분석기술은 다양하게 적용되고 있으니 이들을 종합적으로 활용한 개인용 건강서비스 개발은 아직 미비 한 상태이다. 본 논문에서는 측정대상자의 정신적 혹은 육체적 피로도를 나타내는 8가지의 지표로서 집중도, 전두엽 비대칭 정도, 좌우뇌 활성도 대칭 값, 알파파 및 베타파 훈련도 (이상은 뇌파 분석 결과), 스트레스 레벨, 심박 수, 자율신경균형도 (이상은 맥파 분석 결과)]를 개인에게 알려주는 생체정보기반 개인건강 관리 소프트웨어 시스템의 설계 및 개발 결과에 대하여 서술한다.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.3
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• pp.671-677
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• 2012

A black-box program was implemented in order to monitor abnormal symptoms of human body irregularly occurring during sleep. The system consists of sensor probing body signals, auxiliary devices such as the alarm, lamp, network camera, and signal monitoring computer. Various types of sensors, PPG, ECG, EEG, temperature, respiration sensor, G-sensor, and microphone were used to more exactly identify the causes of abnormal symptoms. If a symptom occurs, the system records the patient's condition to provide information being utilized in the treatment. The sensors are attached on some locations of body being proper to check a specific type of abnormal reaction. Based on the normal range and type of measurement data, criteria of signal levels were set to distinguish abnormal reaction. An abnormal signal being probed, the program starts to operate the lamp, alarm, and network camera at the same time and stores the signal and video data.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.3
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• pp.527-532
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• 2020

In this paper, we presented the results of analysis through EEG measurement for the purpose of checking the frequency band of EEG signals that can be used for personal authentication. The measurement status was divided into the open-eye state and the closed-eye state depending on the presence or absence of an optical task. The data measured in the EEG experiments was divided into seven frequency bands : delta waves, theta waves, alpha waves, SMR waves, mid-beta waves, beta waves and gamma waves to identify the frequency band with the smallest power fluctuation over time. In our results, there was no significant difference between the open-eye state and the closed-eye state, and the SMR waves and mid-beta waves related to human concentration had the smallest fluctuation in power over time, and were a highly reproducible frequency band.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.768-773
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• 2012

One of the main reasons for serious road accidents is driving while drowsy. For this reason, drowsiness detection and warning system for drivers has recently become a very important issue. Monitoring physiological signals provides the possibility of detecting features of drowsiness and fatigue of drivers. One of the effective signals is to measure electroencephalogram (EEG) signals and electrooculogram (EOG) signals. The aim of this study is to extract drowsiness-related features from a set of EEG signals and to classify the features into three states: alertness, drowsiness, sleepiness. This paper proposes a drowsiness detection system using Linear Predictive Coding (LPC) coefficients and Support Vector Machine (SVM). Samples of EEG data from each predefined state were used to train the SVM program by using the proposed feature extraction algorithms. The trained SVM program was tested on unclassified EEG data and subsequently reviewed according to manual classification. The classification rate of the proposed system is over 96.5% for only very small number of samples (250ms, 64 samples). Therefore, it can be applied to real driving incident situation that can occur for a split second.