• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.736-740
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• 1991

EEG(Electroencephalogram) background signals can be represented as the sun of a conventional AR(Autoregressive) process and an innovation process, or a prediction error process. We have seen that conventional estimation techniques. such as least square estimates(LSE) or Gaussian maximum likelihood estimates(MLE-G) are optimal when the innovation process satisfies the Gaussian or presumed distribution. But when the data are contaminated by outliers, or artifacts, these assumptions are not met and conventional estimation techniques can badly fall and be strongly biased. It is known that EEG can be easily affected by artifacts. So we suggest a robust estimation technique which considerably performs well against those artifacts.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1190-1195
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• 1990

A new system for automatic interpretation of the awake electroencephalogram(EEG) was developed in this work. We first clarified all the necessary items for EEG interpretation in accordance with an analysis of visual inspection of the rhythms by a qualified electroencephalographer (EEGer), and then defined each item quantitatively. Concerning the automatic interpretation, we made an effort to find out specific EEG parameters which faithfully represent the procedure of visual interpretation by the qualified EEGer. Those specific EEG parameters were calculated from periodograms of the EEG time series. By using EEG data of 14 subjects, the automatic EEG interpretation system was constructed and compared with the visual interpretation done by the EEGer. The automatic EEG interpretation thus established was proved to be in agreement with the visual interpretation by the EEGer.

• Proceedings of the IEEK Conference
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• 2000.06e
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• pp.127-130
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• 2000

Classification of Electroencephalogram(EEG) makes one of key roles in the field of clinical diagnosis, such as detection for epilepsy. Spectrum analysis using the fourier transform(FT) uses the same window to signals, so classification rate decreases for nonstationary signals such as EEG's. In this paper, wavelet power spectrum method using wavelet transform which is excellent in detection of transient components of time-varying signals is applied to the classification of three types of Event Related Potential(EP) and compared with the result by fourier transform. In the experiments, two types of photic stimulation, which are caused by eye opening/closing and artificial light, are used to collect the data to be classified. After choosing a specific range of scales, scale-averaged wavelet spectrums extracted from the wavelet power spectrum is used to find features by Back-Propagation(13P) algorithm. As a result, wavelet analysis shows superiority to fourier transform for nonstationary EEG signal classification.

• The Journal of Korean Physical Therapy
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• v.24 no.4
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• pp.262-267
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• 2012

Purpose: This study was designed to analyze the differences in cerebral cortex activity of the elderly after extracting the movement related cortical potentials (MRCPs) from electroencephalogram (EEG) during a concentric and eccentric contraction of the elbow joint flexors, and entering them into the brain-mapping program to make the images. Methods: Right-dominant normal elderly people were divided into an eccentric contraction group and a concentric contraction group. Then, their MRCPs were measured using EEG and sEMG, during an eccentric and concentric contraction. Then, they were converted into images using the brain-mapping program. Results: Eccentric contraction group's $C_3$ and Cz showed statistically higher mean values of MRCP positive potential than the concentric contraction group. Conclusion: Researching a cerebral cortex activity, using MRCP, would provide basic data for clinical neuro-physiological researches on aging or neural plasticity of patients with a central nervous system injury.

• Journal of Korean Physical Therapy Science
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• v.15 no.3
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• pp.9-17
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• 2008

Background : The conventional electroencephalography(EEG) is commonly used as aid in the diagnosis of dementia. Recently developed quantitative electroencephalography(qEEG) provides data that are not achievable by conventional EEG. The aim of this study was to find out the usefulness of quantified-EEG in dementia. Method : Twenty elderly women(10 normal elderly, 10 demented elderly) were participated in this study. EEG power and coherence was computed over 21 channels; right and left frontal, central, parietal, temporal and occipital areas. Result : The activity of ${\alpha}$ wave was more higher than others significantly at frontal and parietal areas in normal elderly, but the activity of ${\theta}$ wave was higher in demented elderly. And the activity of ${\theta}$ wave in demented elderly women was more higher than normal elderly women significantly. Conclusion : In conclusion, we discovered that quantitative EEG was used to diagnose dementia.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2217-2220
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• 2003

It is known that an EEG is characterized by the unique and personal characteristics of an individual. Little research has been done to take into account these personal characteristics when analyzing EEG signals. Often the EEG has frequency components which can describe most of the significant characteristics. These combinations are often unique like individual human beings and yet they have an underlying basic characteristics as well. We think that these combinations are the personal characteristics frequency components of the EEG. In this seminar, the EEG analysis method by using the Genetic Algorithms (GA), Factor Analysis (FA), and the Neural Networks (NN) is proposed. The GA is used for selecting the personal characteristic frequency components. The FA is used for extracting the characteristics data of the EEG. The NN is used for estimating the characteristics data of the EEG. Finally, in order to show the effectiveness of the proposed method, classifying the EEG pattern is carried out via computer simulations. The EEG pattern is evaluated under 4 conditions: listening to Rock music, Schmaltzy Japanese ballad music, Healing music, and Classical music. The results, when personal characteristics frequency components are NOT used, gave over 80 % accuracy versus a 95 % accuracy when personal characteristics frequency components are used. This result of our experiment shows the effectiveness of the proposed method.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.12
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• pp.587-594
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• 2021

As the diagnosis using encephalography(EEG) has been expanded, various studies have been actively performed for classifying EEG automatically. This paper proposes a CNN model that can effectively classify EEG signals acquired from healthy persons and patients with epilepsy. We segment the EEG signals into sub-signals with smaller dimension to augment the EEG data that is necessary to train the CNN model. Then the sub-signals are segmented again with overlap and they are used for training the CNN model. We also propose ensemble strategy in order to improve the classification accuracy. Experimental result using public Bonn dataset shows that the CNN can detect the epileptic seizure with the accuracy above 99.0%. It also shows that the ensemble method improves the accuracy of 3-class and 5-class EEG classification.

• Journal of the Korean Society for Library and Information Science
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• v.56 no.1
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• pp.95-117
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• 2022

This study developed and evaluated audio-visual (AV) semantics-based video summarization methods using eye tracking and electroencephalography (EEG) data. For this study, twenty-seven university students participated in eye tracking and EEG experiments. The evaluation results showed that the average recall rate (0.73) of using both EEG and pupil diameter data for the construction of a video summary was higher than that (0.50) of using EEG data or that (0.68) of using pupil diameter data. In addition, this study reported that the reasons why the average recall (0.57) of the AV semantics-based personalized video summaries was lower than that (0.69) of the AV semantics-based generic video summaries. The differences and characteristics between the AV semantics-based video summarization methods and the text semantics-based video summarization methods were compared and analyzed.

• The Journal of the Society of Korean Medicine Diagnostics
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• v.11 no.2
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• pp.96-115
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• 2007

Background and Purpose: Electroencephalogram(EEG) is a multi-scaled signal consisting of several components of time series with different origins. Recently, because of the absence of an identified metric which quantifies the complex amount of information, there are many limitations in using such a linear method. According to chaos theory, irregular signals of EEG can also result from low dimensional deterministic chaos. Chaotic nonlinear dynamics in the EEG can be studied by calculating the correlation dimension. The aim of this study is to analyze correlation between the correlation dimension of EEG and psychological Test (TCI). Methods: Before and after moxibustion treatment, EEG raw data were measured by moving windows during 15 minutes. The correlation dimension(D2) was calculated from stabilized 40 seconds in 15 minutes data. 8 channels EEG study on the Fp, F, T, P was carried out in 30 subjects. Results: Correlation analysis of TCI test is calculated with deterministic non-linear data and stochastic non-linear data. 1. Novelty seeking in temperament is positive correlated with D2 of EEG on Fp. 2. reward dependence in temperament is positive correlated with D2 of EEG on T3,T4 and negative correlated with D2 of EEG on P3,P4. 3. self directedness in character is positive correlated with D2 of EEG on F4, P3. 4. Harm avoidance is negative correlated with D2 of EEG on Fp2, T3, P3. Conclusion: These results suggest that nonlinear analysis of EEG can quantify dynamic state of brain abolut psychological Test (TCI).

• Korean Journal of Clinical Laboratory Science
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• v.47 no.4
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• pp.225-229
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• 2015

Epilepsy is a disorder that causes recurring seizures, and the most objective and useful test for detecting epilepsy is the electroencephalogram (EEG). The subjects of this study are 244 patients who received an EEG after being diagnosed with epilepsy at Seoul National University Hospital in 2014, and who have agreed to the purpose of the study. Based on the EEG results, subjects were divided into normal and abnormal groups with 122 subjects in each group, regardless of their gender and age, to investigate the correlation of EEG and complete blood cell count (CBC) test results. The four significant categories that displayed significant correlation between EEG results and CBC hematological measurements in this study were the white blood cell (WBC), red blood cell (RBC), neutrophil, and lymphocyte tests. The WBC (p<0.05) and neutrophil (p<0.01) showed a positive correlation with EEG results, while RBC (p<0.05) and lymphocyte (p<0.01) showed a negative correlation. One of the limitations of this study is that it is lacking the blood test result analysis according to the types of anti-epilepsy medicine. However, the analysis of EEG results by the same disease has significant meaning. Therefore, further studies are needed to statistically analyze more data in the future.