• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1998.11a
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• pp.94-98
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• 1998

최근 온열 환경에서 인간의 쾌적감에 대한 관심이 커지고 있다. 온열쾌적감에 영향을 주는 요인들로는 온도, 습도, 기류 둥의 물리적 요인과 성별이나 체질 둥의 개인적인 요인들 뿐만 아니라 온열환경에서 느끼는 인간의 감성적인 측면도 요인으로 작용하게 된다. 본 연구에서는 여러가지 온열 환경 중에서 기류환경에 따른 인간의 온열 쾌적감을 평가하기 위해 생체반응의 변화 및 감성의 변화에 따른 생리신호를 분석을 통해 살펴보았다. 기류환경은 기존에 사용되고 있는 풍향변화기류 및 풍량변화기류와 새롭게 개발되어진 감성기류의 3가지 기류 조건을 제시하였고, 이에 따른 인체의 자율신경계의 반응과 감성 상태를 관찰하기 위해 심전도(ECG)와 뇌파(EEC)를 측정하여 HRV(Heart Rate Variability) 분석과 EEG 주파수 스펙트럼 분석을 시행하였다. 생리신호 분석결과 심전도의 HRV 분석에서는 감성기류가 풍향변화 기류와 풍속변화기류에 비해 HF/LF 비가 높게 나타났고, 뇌파의 주파수 스펙트럼 분석에서도 $\beta$파에 대한 뇌파의 상대 전력비가 감성기류에서 높게 나타나 감성기류가 제시된 다른 기류인 풍향변화기류나 풍속변화기류에 비해 쾌적한 온열환경 제시를 위한 기류조건이라고 평가되었다. 결론적으로 심전도의 HRV분석과 뇌파의 주파수 분석이 .제시된 기류환경의 온열쾌적감 평가에서 서로 유의한 결과를 나타냄으로써, 이들 생리신호의 분석이 온열환경에 따른 인간의 감성 변화를 객관적으로 나타내고 온열 쾌적감을 평가하는데 있어 유용한 정보가 될 수 있음을 제시하였다.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.1
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• pp.24-30
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• 2019

Modern people argue that mental health care is necessary because of various factors such as unstable income and conflict with others. Recently, equipments capable of measuring electrocardiogram (ECG) in wearable equipment have been widely used. In the case of overseas, it can be seen as a medical assistant [14]. By using such functions, studies are being conducted to distinguish representative emotions (joy, sadness, anger, etc.) with objective values. However, most studies are increasing accuracy by collecting complex bio-signals in a limited environment. Therefore, we examine the factors that have the greatest influence on the change and discrimination of biometric information on each stimulus.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.199-207
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• 2016

In this paper, we propose efficient smart indoor emotional lighting control system based on android platform using the biological signal. The proposed smart indoor smart emotional lighting control system were configured as the biological signal measurement device and removable smart wall pad, lighting driver, luminaire. The control system was extracts the emotional language by measured the biological signal, and it was transmitted a control signal to each lighting driver using a bluetooth in the wall pad. The lighting driver were designed to control the lighting device through an expansion board by collected control signal and the illuminance information the surrounding. In this case, the wall pad can be selecting of manual control and the bio signal mode by that indoor emotional lighting control algorithms, and it was implemented the control program that possible to partial control by selecting the wanted light. Experiment results of the proposed smart indoor emotional lighting control system, it were possible to the optional control about the luminaire of required area, and the manual control by to adjustable of color temperature with that the efficiently adjustable of lighting by to biological signal and emotional language. Therefore, were possible to effective control for improvement of concentration and business capability of indoor space business conduct by controlling the color and brightness that is appropriate for your situation. And, was reduced power consumption and dimmer voltage, lighting-current than the existing-emotional lighting control system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.10
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• pp.887-895
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• 2014

Drowsy driving is a large proportion of the total car accidents. 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. Many researches have been published that to measure electroencephalogram(EEG) signals is the effective way in order to be aware of fatigue and drowsiness of drivers. 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, transition, and drowsiness. This paper proposes a drowsiness detection system using errors-in-variables(EIV) for extraction of feature vectors and multilayer perceptron (MLP) for classification. The proposed method evaluates robustness for noise and compares to the previous one using linear predictive coding (LPC) combined with MLP. From evaluation results, we conclude that the proposed scheme outperforms the previous one in the low signal-to-noise ratio regime.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.103-108
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• 2016

In daily life, humans get stress very often. Stress is one of the important factors of healthy life and closely related to the quality of life. Too much stress is known to cause hormone imbalance of our body, and it is observed by the brain and bio signals. Based on this, the relationship between brain signal and stress is explored, and brain signal based stress index is proposed in our work. In this study, an EEG measurement device with 32 channels is adopted. However, only two channels (FP1, FP2) are used to this study considering the applicability of the proposed method in real enveironment, and to compare it with the commercial 2 channel EEG device. Frequency domain features are power of each frequency bands, subtraction, addition, or division by each frequency bands. Features in time domain are hurst exponent, correlation dimension, lyapunov exponent, etc. Total 6 subjects are participated in this experiment with English sentence reading task given. Among several candidate features, ${\frac{{\theta}\;power}{mid\;{\beta}\;power}}$ shows the best test performance (70.8%). For future work, we will confirm the results is consistent in low price EEG device.

• 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 Biomedical Engineering Research
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• v.25 no.3
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• pp.217-225
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• 2004

In this paper, a new automatic classification method for the normal EEC and schizophrenia EEC using hidden Markov model(HMM) is proposed. We used the feature parameters which are the variance for statistical stationary interval of the EEC and power spectrum ratio of the alpha, beta, and theta wave. The results were shown that high classification accuracy of 90.9％ in the case of normal person, and 90.5％ in the case of schizophrenia patient. It seems that proposed classification system is more efficient than the system using complicate signal processing process. Hence, the proposed method can be used at analysis and classification for complicated biosignal such as EEC and is expected to give considerable assistance to clinical diagnosis.

• Journal of Biomedical Engineering Research
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• v.22 no.5
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• pp.425-430
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• 2001

The Purpose of this study was to find the way of discriminating EEG for some mental activity. which are not characterized within linear spectral analysis but with non-linear analysis . We lave investigated the way of characterizing EEG changes during emotional and cognitive states in healthy volunteered subjects who responded to three designed status. in which the subjects were relaxing with ease and eyes closed. listening to music and computing a simple subtraction with eyes closed. Especially, we estimated EEG dimensional complexity by Skinner s Point-wise correlation dimension(PD2) method for each mental states. As a result it has been found that the subjects, who responded that the\ulcorner had concentrated well during the arithmetic task. show higher PD2 in their non-linear EEG measures. in comparison with the subjects who responded that they had not concentrated during the task This highness of PD2 is also significant in statistical analysis. A subject who had the highest score in evaluating the intensity of induced emotion during emotional task shows significantly lower PD2 in statistical analysis than other subjects who had lower scores. Linear spectral analysis was also performed on these data. However, they did not show and significant difference. Only non-linear dynamical analysis shows the significant different result on these mental status.

• Journal of Biomedical Engineering Research
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• v.18 no.4
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• pp.389-396
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• 1997

Several types of electrical artifact seen on electroencephalogram( EEG) records are described. Those are the EOG and the PVC roller pump noise, and so on. An adaptive digital filtering of the electroencephalogram( EEG) is a successful way of suppressing mains interference, but it affects some of the frequency components of the signal, whore artifacts may not be acceptable in some cafes of automatic EEG processing. Thus we studied the method for cancelling these artifacts. This proposed method does not use the reference channel, and is realized by connecting the linear predictor and the fixed FIR filter for the EOG artifact, and by cascading the linear predictor and the noise canceller for the pump artifact. The simulation results illustrate the performances of the proposed method in terms of the capability of interferences suppression. In the results we obtained about 20 dB noise reduction.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.1112-1114
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• 2017

본 논문은 감정변화와 관련이 높다고 알려져 있는 생체정보인 뇌파(EEG), 심전도(ECG), 심박변이도(HRV)를 바탕으로 사용자의 감정상태를 추론하여 치유음악을 추천해주는 시스템을 제안한다. 사용자의 생체정보를 기반으로 사용자의 감정상태를 평온, 집중, 긴장, 우울의 4가지 단계로 분류하는 감성추론 시스템을 설계하고, 각각의 감정상태에 따라 적절한 카테고리의 음악을 추천함으로써 사용자의 스트레스 정도를 완화시키고자 한다.