• Science of Emotion and Sensibility
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• v.14 no.4
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• pp.503-512
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• 2011

Recently in HCI research, emotion recognition is one of the core processes to implement emotional intelligence. There are many studies using bio signals in order to recognize human emotions, but it has been done merely for the basic emotions and very few exists for the other emotions. The purpose of present study is to confirm the difference of autonomic nervous system (ANS) response in three emotions (boredom, pain, and surprise). There were totally 217 of participants (male 96, female 121), we presented audio-visual stimulus to induce boredom and surprise, and pressure by using the sphygmomanometer for pain. During presented emotional stimuli, we measured electrodermal activity (EDA), skin temperature (SKT), electrocardiac activity (ECG) and photoplethysmography (PPG), besides; we required them to classify their present emotion and its intensity according to the emotion assessment scale. As the results of emotional stimulus evaluation, emotional stimulus which we used was shown to mean 92.5% of relevance and 5.43 of efficiency; this inferred that each emotional stimulus caused its own emotion quite effectively. When we analyzed the results of the ANS response which had been measured, we ascertained the significant difference between the baseline and emotional state on skin conductance response, SKT, heart rate, low frequency and blood volume pulse amplitude. In addition, the ANS response caused by each emotion had significant differences among the emotions. These results can probably be able to use to extend the emotion theory and develop the algorithm in recognition of three kinds of emotions (boredom, surprise, and pain) by response measurement indicators and be used to make applications for differentiating various human emotions in computer system.

• Journal of IKEEE
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• v.18 no.3
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• pp.368-375
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• 2014

Brain computer interface(BCI) technology has been continuously developed due to the continuous development of interface technology and the promotion of brain wave research. In this paper, we propose a driving habit management system by adopting BCI to transportation. The proposed system consists of the electroencephalogram(EEG) measuring unit, the EEG analysis unit, the memory section for storing the state information of drivers, the speed controller unit and the alarming section for generating warnings. Our proposed system can reduce the drowsy driving, improve the driving habits of users and help to prevent traffic accidents.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.3
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• pp.645-653
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• 2011

The aim of this study was to develop a Pulsed Magnetic Fields(PMFs) system which can produce effects locally and simulate muscular tissues equally. To evaluate the PMFs system we caused DOMS(Delayed Onset Muscle Soreness) to subjects in biceps of the arm. Then, we stimulated acupoint HT2 using PMFs(20 minutes) and TEAS(20 minutes) for 2 days. The other subjects did not stimulate. Then we checked body surface temperature in biceps of the arm. All subjects had an asymmetrical body surface temperature in biceps after exercise(Non-stimulation group=$2.00{\pm}1.16^{\circ}C$, TEAS group=$1.73{\pm}0.52^{\circ}C$, PMFs group=$1.48{\pm}0.51^{\circ}C$). After 1st stimulation all subjects had decreased temperature differences(Non-stimulation group=$1.37{\pm}0.71^{\circ}C$, TEAS group=$1.08{\pm}0.43^{\circ}C$, PMFs group=$1.23{\pm}0.15^{\circ}C$). PMFs group had a symmetry body surface temperature after 24 hours($0.05{\pm}0.06^{\circ}C$) and TEAS group had that after 48 hours($0.1{\pm}0.08^{\circ}C$). Non-stimulation group did not recovery after 48 hours($0.37{\pm}0.06^{\circ}C$). Therefore, PMFs on acupoint had an therapeutic effect in DOMS.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1993-1994
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• 2008

뇌-기계 인터페이스(BMI : Brain Machine Interface)는 사람의 뇌에서 추출된 데이터를 이용하여 신체동작 없이 기계나 컴퓨터를 동작시키는 새로운 인터페이스 기술이다. 이러한 뇌-기계 인터페이스 기술은 자발전위 뇌파와 유발전위 뇌파를 이용한다. 자발전위 뇌파는 원하는 파형의 파워 값을 조절하여 새로운 인터페이스를 만드는 것이고, 유발전위 뇌파는 자극을 받았을 때 발생하는 값을 이용하여 새로운 인터페이스를 구현하는 것을 말한다. 이 중 자발전위는 사람이 스스로 뇌파의 방출량을 조절할 수 있어 집중력 향상과 같은 효과를 얻을 수 있다는 장점이 있다. 따라서 본 연구에서는 자발전위를 이용하여 뇌-기계 인터페이스 기술을 구현하였다.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.10
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• pp.606-610
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• 2003

Noise subtraction using reference channel data has been used to improve signal-to-noise ratio in magnetoencephalography. In this paper, an adaptive noise subtraction model is proposed and parameters for the model are optimized. A criterion to determine an optimal update period for the filter coefficients is proposed based on the ratio of peak amplitude of evoked field (N100m) divided by the output standard deviation. Experiments are carried out using a 40 channel MEG system. From the experiments, the proposed noise subtraction method shows superior performances over existing non-adaptive methods. Two-dimensional topographic map is shown for a diagnosis with a cubic spline interpolation.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.1
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• pp.62-68
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• 2012

Steady-State Visually Evoked Potentials (SSVEP) are natural response signal associated with the visual stimuli with specific frequency. By using SSVEP, occipital lobe region is electrically activated as frequency form equivalent to stimuli frequency with bandwidth from 3.5Hz to 75Hz. In this paper, we propose an experimental paradigm for analyzing EEGs based on the properties of SSVEP. At first, an experiment is performed to extract frequency feature of EEGs that is measured from the image-based visual stimuli associated with specific objective with affordance and object-related affordance is measured by using mirror neuron system based on the frequency feature. And then, linear discriminant analysis (LDA) method is applied to perform the online classification of the objective pattern associated with the EEG-based affordance data. By using the SSVEP measurement experiment, we propose a Brain-Computer Interface (BCI) system for recognizing user's inherent intentions. The existing SSVEP application system, such as speller, is able to classify the EEG pattern based on grid image patterns and their variations. However, our proposed SSVEP-based BCI system performs object pattern classification based on the matters with a variety of shapes in input images and has higher generality than existing system.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.7 no.2
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• pp.55-62
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• 2013

The exoskeleton robot to assist walking of hemiplegia patients or disabled persons has been studied in this paper. The exoskeleton robot with degrees of freedom of 2 axis has been developed and tested for joint motion. The obtained EMG signal from normal person was analyzed and the control signal was extracted from it for convenient and automotive performance of assistance robot to help hemiplegia patient walks as normal person does. the purpose of using FES(Functional Electrical Stimulation) for hemiplegia patient's walk is to restore damaged body function by this, but this could give fatal electrical shock to patients by wrong use or cause quick fatigue in muscle by continuous stimulation. The convenient movement of hemiplegia patients with minimum muscle fatigue was looked possibly by operation of assistance robot exoskeleton using control signal. and the walking assistance exoskeleton robot seemed works more efficiently than using FES stimulator. The experiment in this study was performed based on usual motion in our life like walking, standing-up, sitting-down, and particularly feedback control system using Piezo sensor along with button switch was applied for smooth swing motion in walking. The experiment also shows that hemiplegia patients can move conveniently by using electromyogram signal of healthy leg for the operation signal of assistance robot system attached at damaged symmetrical leg.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.4
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• pp.304-309
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• 2000

For understanding the information processing in human brain, we analyze the EEG, a spontaneous electric activity on the scalp of the human. In this paper, we used the mutual information to analyze EEG. The mutual information is used to show the stochastic correlation between signals which are generated in the communication and information theory. The used EEG is evoked by each auditory stimulus in positive and negative emotional states. As a result, we found thet there is some difference at the mutual information in each emotional state.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.215-219
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• 2000

인간의 뇌 정보처리를 이해하기 위한 일환으로서, 많은 연구자들이 사람의 두피에서 자발적으로 발생하는 전기 활동인 뇌파(EEG)를 분석하였다. 측정된 뇌파는 잡음처럼 보이는 비선형적인 거동으로 인하여 단순한 관찰만으로는 그 특징을 분석하기가 매우 어렵다. 따라서 이러한 뇌파를 분석하고 이해하기 위한 방법으로 파워스펙트럼, 바이스펙트럼 등과 같은 스펙트럼 분석과 상관차원, 프랙탈 차원과 같은 비선형 카오스 분석 등과 같은 해석법들이 활발히 연구되어왔다. 본 논문에서는 이러한 기존의 방법 외에 두 신호사이의 통계적 의존성을 측정하는 양인 상호정보를 이용하여 뇌파의 특징을 분석하였다. 뇌파간의 상호정보 분석을 통해 두뇌에서의 정보의 흐름에 관한 특징을 알아보았고, 감성자극에 반응하는 두뇌의 활동영역을 알 수 있었다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.4
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• pp.819-825
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• 2003

The purpose of this study is to develop the speaker recognition system which can select one of operating modes in FES for general paralysis patients. As spiral injury by traffic accident, industrial disaster, or stroke has been increased, the development of FES(Functional Electrical Stimulator) system is urgent to prevent paralysis and atrophy, and to assist the patients walking. For these patients we developed FES system(1). To operate this system one of several operating modes must be selected. As this can not be done by general paralysis patients, an attempt has been tried in this study to select the mode by speaker recognition system. RSC-300 of sensory co. has been chosen as a speaker recognition chip, and PIC16F84 is adapted to interface RSC-300 and FES system.