• Journal of Korea Game Society
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• v.15 no.2
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• pp.105-114
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• 2015

We propose a real time user interface that utilizes emotion recognition by physiological signals. To improve the problem that was low accuracy of emotion recognition through the traditional EEG(ElectroEncephaloGram), We developed a physiological signals-based emotion recognition system mixing relative power spectrum values of theta/alpha/beta/gamma EEG waves and autonomic nerve signal ratio of ECG (ElectroCardioGram). We propose both a data map and weight value modification algorithm to recognize six emotions of happy, fear, sad, joy, anger, and hatred. The datamap that stores the user-specific probability value is created and the algorithm updates the weighting to improve the accuracy of emotion recognition corresponding to each EEG channel. Also, as we compared the results of the EEG/ECG bio-singal complex data and single data consisting of EEG, the accuracy went up 23.77%. The proposed interface system with high accuracy will be utillized as a useful interface for controlling the game spaces and smart spaces.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.958-961
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• 2015

Emotional expression is universal and emotional state impacts important areas in our life. Until now, analyzing the acquired EEG signals under circumstances caused by invoked feelings and efforts to define their emotional state have been made mainly by psychologists based on the results. But, recently emotion-related information was released by research results that it is possible to identify mental activity through measuring and analyzing the brain EEG signals. So, this study has compared and analyzed emotional expressions of human by using brain waves. To get EEG difference for a particular emotion, we showed specific subject images to the people for changing emotions that peace, joy, sadness and stress, etc. After measured EEG signals were converged into frequence domain by FFT signal process, we have showed EEG changes in emotion as a result of the performance analyzing each respective power spectrum of delta, theta, alpha, beta and gamma waves.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.1
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• pp.35-41
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• 2006

Although reachers have studied for a long time, they don't make criteria for anesthesia depth. anesthetists can't make a prediction about patient's reaction. Therefor, patients have potential risk such as poisonous side effect late-awake, early-awake and strain reaction. EEG are received from twenty-five patients who agreed to investigate themselves during operation with Enflurane-anesthesis in progress of anesthesia. EEG are divided pre-anesthesia, before incision of skin, operation 1, operation 2, awaking, post-anesthesia by anesthesia progress step. EEG is applied pre-processing, base line correct, linear detrend to get more reliable data. EEG data are handled by electronic processing and the EEG data are calculated by bicoherence. During pre-anesthesia and post anesthesia, appearance rate of bicoherence value is observed strong appearance rate in high frequency range($15\~30Hz$). During the anesthesia of patient, a strong appearance rate is revealed the low frequency area(0~10Hz). After bicoherence is calculated by percentage of a appearance rate, that is, Bicpara$\#$1, Bicpara$\#$2, Bicpara$\#$3 and Bicpara$\#$4 parameter are extracted. In result of bicoherence analysis, Bicpara$\#$2 and Bicpara#4 are considered that the best parameter showed progress of anesthesia effectively. And each separated bicoherence are calculated by average bicoherence's numerical value, divide by 2 area, appear by each BicHz$\#$1, BicHz$\#$2, and observed BicHz$\#$1/BicHz$\#$2's change. In result of bicoherence analysis, BicHz$\#$1, BicHz$\#$2 and BicHz$\#$1/BicHz$\#$2 are considered that the best parameter showed progress of anesthesia effectively. In conclusion, I confirmed the anesthesia progress phase, concluded to usefulness of parameter on bispectrum and bicoherence analysis and evaluated the depth of anesthesia. In the future, it is going to use for doctor's diagnosis and apply to protect an medical accident owing to anesthesia.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.774-779
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• 2012

Brain-computer interface based on motor imagery is a system that transforms a subject's intention into a control signal by classifying EEG signals obtained from the imagination of movement of a subject's limbs. For the new paradigm, we do not know which positions are activated or not. A simple approach is to use as many channels as possible. The problem is that using many channels causes other problems. When applying a common spatial pattern (CSP), which is an EEG extraction method, many channels cause an overfit problem, in addition there is difficulty using this technique for medical analysis. To overcome these problems, we suggest a binary particle swarm optimization with channel impact factor in order to select channels close to the most important channels as channel selection method. This paper examines whether or not channel impact factor can improve accuracy by Support Vector Machine(SVM).

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.2
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• pp.367-372
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• 2023

In this paper, we analyzed through the EEG signals how musical stimulus affects learning activities. Musical stimuli were divided into sedative and stimulative tendency music, preferred and non-preferred music, and the learning activity tasks were divided into mathematics tasks and memorization tasks. The signals measured in the EEG experiments were analyzed with the power spectrum of SMR waves known to be related to human concentration. Those spectra used for quantitative comparison in this paper. As a result the power of the EEG signals was observed to be greater than the case where music was given as a stimulus. Regardless of the type of task, the power of the EEG signals was observed to be greater in the case of sedative tendency than in the case of stimulative tendency, and the power of the EEG signals was observed to be greater in the case of favorite music than in the case of unfavorite music. From these results, it is estimated that if the musical stimulus exists, in the case of sedative tendency music, and in the case of favorite music, concentration can be increased than in the relative case.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.6
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• pp.527-533
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• 2013

BCI (Brain-Computer Interface) is a system that transforms a subject's brain signal related to their intention into a control signal by classifying EEG (electroencephalograph) signals obtained during the imagination of movement of a subject's limbs. The BCI system allows us to control machines such as robot arms or wheelchairs only by imaging limbs. With the exact same experiment environment, activated brain regions of each subjects are totally different. In that case, a simple approach is to use as many channels as possible when measuring brain signals. However the problem is that using many channels also causes other problems. When applying a CSP (Common Spatial Pattern), which is an EEG extraction method, many channels cause an overfitting problem, and in addition there is difficulty using this technique for medical analysis. To overcome these problems, we suggest an optimal channel selection method using a BPSO (Binary Particle Swarm Optimization), BPSO with channel impact factor, and GA. This paper examined optimal selected channels among all channels using three optimization methods and compared the classification accuracy and the number of selected channels between BPSO, BPSO with channel impact factor, and GA by SVM (Support Vector Machine). The result showed that BPSO with channel impact factor selected 2 fewer channels and even improved accuracy by 10.17~11.34% compared with BPSO and GA.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.7
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• pp.71-78
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• 2017

There are several algorithms to classify emotion, such as Support-vector-machine (SVM), Bayesian decision rule, etc. However, many researchers have insisted that these methods have minor problems. Therefore, in this paper, we propose a novel method for emotion recognition based on Electroencephalogram (EEG) signal using the Gradient method which was proposed by Han. We also utilize a database for emotion analysis using physiological signals (DEAP) to obtain objective data. And we acquire four channel brainwaves, including Fz (${\alpha}$), Fp2 (${\beta}$), F3 (${\alpha}$), F4 (${\alpha}$) which are selected in previous study. We use 4 features which are power spectral density (PSD) of the above channels. According to performance evaluation (4-fold cross validation), we could get 85% accuracy in valence axis and 87.5% in arousal. It is 5-7% higher than existing method's.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.316-318
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• 2004

In this study, the influence of electromagnetic wave effected on human body of light sensing was classified. Subjects of the eye was stimulated by the rays of LED and the measured electrical signals(EEG, EOG and ERG) in human body were compared and analyzed in the case of exposed at electromagnetic wave or not. The result show that when the subjects were not exposed at electromagnetic wave, the ratio of a wave has a large percentage in the EEG signal and the ratio of $\beta$ wave has come to good.

• Journal of Korea Multimedia Society
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• v.22 no.12
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• pp.1491-1499
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• 2019

Insufficient sleep time and bad sleep quality causes many illnesses and it's research became more and more important. The most common method for measuring sleep quality is the polysomnography(PSG). The PSG is a test used to diagnose sleep disorders. The most common PSG data is obtained from the examiner, which attaches several sensors on a body and takes sleep overnight. However, most of the sleep stage classification in PSG are low accuracy of the classification. In this paper, we have studied algorithm for sleep level classification based on machine learning which can replace PSG. EEG, EOG, and EMG channel signals are studied and tested by using CNN algorithm. In order to compensate the performance, a mixed model using both CNN and DNN models is designed and tested for performance.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.61 no.1
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• pp.55-58
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• 2012

By using EEG and HRV, subjects were estimated on their psychological and physiological reaction when reading psychrometric chart in 7 point century font, in an environmental test room in the condition of temperature 25[$^{\circ}C$], relative humidity 50[RH%], air current speed 0.02[m/sec], giving variation in illuminance to 0.1, 300, 600, 1000, 1300 and 1600[lux]. As a result, it was at 1300[lux] that absolute ${\alpha}$ wave, SMR, SDNN were most vitalized, and also both sides ${\alpha}$ wave asymmetry index, SEF50, HRT, stress and fatigue degree were at the lowest. It was found that a certain illuminance which minimizes psychological stress and fatigue degree while enhancing concentration and task achievement stably does exist.