• 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.

• Proceedings of the KOSOMBE Conference
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• v.1998 no.11
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• pp.193-194
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• 1998

This paper describes a multi-functional EEG (electroencephalogram) software. The software manages the patient's EEG data systematically and analyzes the signal and display the parameters on a PC monitor in real-time. Since the software provides various parameters simultaneously, user can observe patients multilaterally. Reference patterns of CSA and DSA can be captured and displayed on top of the monitor. And user can mark events of surgical operation or patient's conditions, so it is possible to jump to the points of events directly, when reviewing the recorded file afterwards. Many convenient functions are equipped and these are operated by mouse clicks.

• Proceedings of the Safety Management and Science Conference
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• 2011.11a
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• pp.583-591
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• 2011

A number of Brain-Computer Interface (BCI) studies have been performed to assess the cognitive status through EEG signal. However, there are a few studies trying to prevent user from unexpected safety-accident in BCI study. The EEGs were collected from 19 subjects who participated in two experiments (rest & event-related potential measurement). There was significant difference in EEG changes of both spontaneous and event-related potential. Beta power and P300 latency may be useful as a biomarker for prevention of response to safety-accident.

• Journal of the Korea Safety Management & Science
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• v.14 no.1
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• pp.1-6
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• 2012

A number of Brain-Computer Interface (BCI) studies have been performed to assess the cognitive status through EEG signal. However, there are a few studies trying to prevent user from unexpected safety-accident in BCI study. The EEGs were collected from 19 subjects who participated in two experiments (rest & event-related potential measurement). There was significant difference in EEG changes of both spontaneous and event-related potential. Beta power and P300 latency may be useful as a biomarker for prevention of response to safety-accident.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.12
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• pp.452-458
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• 2017

In this study, a device was developed for diagnosing depression using EEG signals from July 2016 to June 2017. For normal people, the left alpha rhythm is more activated than the right alpha rhythm, but for the depressed patients, the right alpha rhythm is more activated than the left one. An analog circuit and digital low pass filter were used for noise removal and amplification of EEG, and the Hamming window function was applied to eliminate the signal leakage generated by the fast Fourier transform. To verify the validity of the developed diagnosis system, the EEG of 20 university students in the 3rd and 4th grade with an average age of 24 years was measured. Calculations of the relative value of the left and right alpha rhythm for the depression diagnosis revealed a minimum, maximum, and mean value of 66.7, 113.3, and 92.2, respectively. In addition, 7 out of 20 subjects were between 90 and 95, and those with a higher mean deviation of approximately 20 tended to have mild depression. These results can provide meaningful data for the development of depression treatment equipment by solving the left and right brain asymmetry problem, and it may be applied usefully to diagnose depression after clinical trials on a large number of depressed patients.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.12
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• pp.254-260
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• 2013

Control signal generated from EEG (electro-encephalogram) can be used in many applications. In our study, for the purpose of developing practical instruments, a single channel system of providing reliable on/off signals was investigated since a multi-channel system can be bulky and expensive. Brainwaves in alpha, beta and theta bands were analyzed in order to extract reliable control signals when the concentration state reached. Rest and concentration states were differentiated based on power spectrum and histogram analysis. A better performance was obtained when the ratio between the beta and theta bands was used compared to the theta band only. In general, the longer the rest period before concentration, the lower success rate was. In addition, longer rest time produced longer detection time. Though there were individual differences, in case of 10-second rest time, a success rate of 91% and a detection time of 20.2 seconds was achieved on average.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.6
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• pp.891-898
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• 2012

The study for the passenger's comfortableness of vehicles and the arousal of car drivers has been done widely. On the other hand, there are few studies for the locomotive engineers. Human error means that the mistakes made by human, recently it receives attention in the field of safety engineering and human engineering. Comparing the operating condition of train with car, because of the simplification of the visual stimulus, the arousal level on the train goes down easily. The arousal level down makes judgement down, the accident risk from human error is getting bigger. In this study, we measured bio-signals(ECG, EDA, PPG, respiration and EEG) from 6 locomotive engineers to evaluate their arousal state while they operated the train. Also we recorded the 3 axes acceleration signal showing the vibration state of train. Also, the existence of tunnels were simultaneously measured. At the station section where the train speed goes down, the size of vector's sum decreases because of reduced vibration. Beta component in EEG tends to increase at the entering point of each station and tunnel. It is due to the arousal reaction and tension growth. The mean SCR(skin conductance response) was more increased in neutral section. As the button control movement (body movement) increases in the neutral section, it is appeared that SCR increase. RR interval tends to gradually increase during train operation for 1 hour 40 minutes. However, It tends to sharply decrease at the stop station because strong concentration needed to stop train on the exact point. The engineer's arousal reaction can be checked through analysing the bio-signal change during train operation. Therefore, if this analysing result is adopted to the sleepiness prevention caution system, it will be useful for the safety train operation.