• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.5
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• pp.791-796
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• 2013

Advent of the brain wave health care system is considered as an important issues in the industrial and research area in these days. It is necessary to detect EEG signals in real-time in order to support the medical emergency service for the epileptic or brain infarct patients. Since the efficient network support is an essential factor for the system, several topologies using sensor node based wireless body area network is suggested and simulated in this paper. Finally the Opnet simulation result is evaluated for the efficient topology of the body area network.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.293-295
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• 1992

This Paper presents the design of an automated EEG analyzing system. The design considerations including processing speed, A/D conversion, filtering, and waveforms detection, are overviewed with the description of the associated EEG charateristics. The architecture of the currently implemented system consists of a -controller based front-end signal processing unit and a host computer system. The data acquisition procedures are described along with a couple of illustrations of the acquired EEG/EOG signal.

• Journal of Power System Engineering
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• v.16 no.5
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• pp.20-25
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• 2012

오늘날 고속 철도는 중요한 교통수단으로 사용되고 있다. 주행거리 단축을 위해 직선 선로를 만드는 것이 불가피해 졌고 그에 따라 터널과 교량의 구간이 늘어나게 되었다. 특히 터널 통과 시에 발생되는 실내 소음은 운행 속도, 운행 구간 레일의 종류 등 여러 가지 원인에 의해 야기되어 진다. 실내소음으로 인해 철도를 이용하는 승객의 쾌적한 환경에 많은 영향을 미치게 된다. 이에 본 연구에서는 KTX와 새마을호의 터널 통과 시 발생되는 소음이 피험자에게 미치는 영향을 EEG를 통해 살펴보았다. 먼저 터널 통과 시 KTX와 새마을호의 실내 소음을 실제로 측정하여 크기, 주파수별로 분석하였다. 측정된 실내 소음을 피험자에게 제시하였을 때 나타나는 EEG를 측정하였다. EEG의 분석에 대해서는 불안, 긴장 등 스트레스를 받을 때 강하게 나타나는 ${\beta}$파의 변화를 관찰한 결과를 제시하였다.

• Journal of Biomedical Engineering Research
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• v.30 no.4
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• pp.327-332
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• 2009

In this paper, new index was developed to estimate the depth of anesthesia during general anesthesia using EEG. Analysis of the power spectral density(PSD) of EEG was used to develop new parameters because EEG signal tends to have slow wave during anesthesia. Classifier for index creator was developed by using SEF, BDR and BTR parameters, which are calculated by power spectral density. EEG data were obtained from 7 patients (ASA I, II) during general anesthesia with Sevoflurane. The anesthetic depth evaluation indexes ranged from 0 to 100. The average were $86.05{\pm}10.1$, $36.98{\pm}20.2$, $15.33{\pm}13.6$, $50.87{\pm}16.5$ and $87.72{\pm}11.7$ for the states of pre-operation, induction of anesthesia, operation, awaked and post-operation, respectively. The results show that while the depth of anesthesia was evaluated, more accurate information can be provided for anesthetician.

• Journal of Korea Multimedia Society
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• v.19 no.4
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• pp.756-764
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• 2016

In this paper, we present electroencephalography (EEG)-based power spectra analysis and auditory stimuli methods as coping mechanisms for disgust affection and phobia. Disgust affection is a negative emotion generated from trying to eliminate something harmful to one. It is usually related to mental illnesses such as obsessive-compulsive disorder, specifically phobia and depression. In our experiments, participants watched videos on horrible body mutilation and disgusting creatures, with either the original sound track or relaxing and exciting music as auditory stimulation. After watching the videos with original sound track, the participants watched the same video with a different audio background, such as soothing or cheerful music. We analyzed the EEG data utilizing relative power spectra and examined survey results of the participants. The results demonstrated that disgust affection is decreased when participants watched the video with relaxing or exciting music instead of the original soundtracks. Moreover, we confirmed that human's brainwave reacts according to types of audio and sources of disgust affection.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1997.11a
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• pp.69-74
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• 1997

Sleep deprivation may affect the brain functions such as cognition and, consequentoy, dynamics of the EEG. we examiced the effects of sleep deprivation on chaoticity of EEG. Five volunteers were sleep-deprived over a period of 24 hours, They were checked by EEG during two days, the first day of baseline period, EEGs were reorded form 16 channels for nonlinear analysis. We dmployed a method of minimum cmbedding dimension to calculate the first positive Lyapunov exponent. For limited noisy data, this algorithm was strikingly faster and more accurate than previous ones. Our results show that the sleep deprived volunteers had lower values of the first positive Lyapunov exponent at ten channels (Fp$\_$1/, F$\_$4/, F$\_$8/, T$\_$4/, T$\_$5/, C$\_$3/, C$\_$4/, P$\_$3/, p$\_$4, O$\_$1/) compared with the values of baseline periods. These results suggested that sleep deprivation leads to decreawe of chaotic activity in brain and impairment of the information processing in the brain. We suggested that nonlinear analysis of the EEG before and after sleep deprivation may offer fruitful perspectives for understanding the role o f sleep deprivation on the brain function.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.5
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• pp.589-594
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• 2014

It has been known that the particular brain waves are induced when a human concentrates. In our study, we aimed to analysis the brain waves related to human concentration using visual stimulus to induce the concentration. The visual stimulus tasks were presented to subjects for concentration. We measured EEG signals with several channels and analyzed the signals into several frequency bands. In the measured EEG signals, we analyzed to focus on theta waves, SMR waves and mid-beta waves. Therefore we presented the results to investigate characteristics of the EEG signals related to the human concentration.

• Korean Institute of Interior Design Journal
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• v.25 no.3
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• pp.60-69
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• 2016

This experimental study measures the emotional and physiological responses of customers as to cafe facade design. It is done through eye-tracking and EEG response experiments. Specifically, their visual concentration and EEG concentration are analyzed in line with their facade preferences. The findings are as follows. First, the correlation between their facade preferences and visual concentration on facades is as follows: Highly preferable facades have a lower visual concentration frequency than the less preferable facades. Second, an analysis of $12{\times}12$ lattice division of facades shows that all facades have a high visual concentration for signs. The exceptions are F(6), F(7), F(8), and F(10). There is no correlation between the facade preferences and visual concentration behaviors for particular facade elements. Third, an analysis of prefrontal lobe's facade concentration shows that there is no correlation between the preferences and EEG concentration. However, there are big differences in the prefrontal lobe activity of 12 subjects depending on the facade. In particular, nine of them (3, 9, 13, 14, 15, 28, 36, 38, 43) show an activated prefrontal lobe as to the highly preferable facades-F(1), F(2), F(3), and F(4). However, such activation is not detected on the less preferable facades-F(9), F(10), F(11), and F(12).

• Journal of the Korea Society of Computer and Information
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• v.23 no.11
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• pp.129-135
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• 2018

In this study, we designed a model that can measure the level of user's concentration by measuring and analyzing EEG data of the subjects who are performing Continuous Performance Test based on visual stimulus. This study focused on alpha and beta waves, which are closely related to concentration in various brain waves. There are a lot of research and services to enhance not only concentration but also brain activity. However, there are formidable barriers to ordinary people for using routinely because of high cost and complex procedures. Therefore, this study designed the model using the portable EEG measurement device with reasonable cost and Visual Continuous Performance Test which we developed as a simplified version of the existing CPT. This study aims to measure the concentration level of the subject objectively through simple and affordable way, EEG analysis. Concentration is also closely related to various brain diseases such as dementia, depression, and ADHD. Therefore, we believe that our proposed model can be useful not only for improving concentration but also brain disease prediction and monitoring research. In addition, the combination of this model and the Brain Computer Interface technology can create greater synergy in various fields.

• Biomedical Engineering Letters
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• v.8 no.4
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• pp.373-382
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• 2018

Since epileptic seizure is unpredictable and paroxysmal, an automatic system for seizure detecting could be of great significance and assistance to patients and medical staff. In this paper, a novel method is proposed for multichannel patient-specific seizure detection applying the earth mover's distance (EMD) in scalp EEG. Firstly, the wavelet decomposition is executed to the original EEGs with five scales, the scale 3, 4 and 5 are selected and transformed into histograms and afterwards the distances between histograms in pairs are computed applying the earth mover's distance as effective features. Then, the EMD features are sent to the classifier based on the Bayesian linear discriminant analysis (BLDA) for classification, and an efficient postprocessing procedure is applied to improve the detection system precision, finally. To evaluate the performance of the proposed method, the CHB-MIT scalp EEG database with 958 h EEG recordings from 23 epileptic patients is used and a relatively satisfactory detection rate is achieved with the average sensitivity of 95.65% and false detection rate of 0.68/h. The good performance of this algorithm indicates the potential application for seizure monitoring in clinical practice.