• Proceedings of the KOSOMBE Conference
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• v.1995 no.11
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• pp.278-281
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• 1995

In this paper, we have developed EEG (electroencephalography) analyzer for monitoring the condition of brain in neurological surgery. This system is composed of EEG amplifier. personal-computer and BSP (Digital Signal Processor). By parallel processing of DSP, this system can analysis the power spectral density change of EEG in real-time and display the CSA(Compressed Spectral Array) and CDSA(Color Density Spectral array) of EEG. This system was tested by real EEG and showed the change of EEG.

• Journal of Biomedical Engineering Research
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• v.17 no.2
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• pp.179-188
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• 1996

A clinically oriented 32-channel electroencephalogram (EEG) and evoked potential (EP) mapping system has been developed EEG and EP signals acquired from 32-channel electrodes attached on the heroid surface are amplified by a pre-amplifier which is separated from main amplifier and is located near the patient to reduce signal attenuation and noise contamination between electrodes and the amplifier. The amplified signals are further amplified by a main amplifier where various filtering and gain contr61 are achieved An automatic artifact rejection scheme is employed using neural network-based EEG and artifact classifier, by which examination time is substantially reduce4 The continuously measured EEG sigrlals are used for spectral mapping, and auditory and visual evoked potentials measured in synchronous to the auditory and visual stimuli are used for temporal evoked potential mapping. A user-friendly graphical interface based on the Microsoft Window 3.1 is developed for the operation of the system. Statistical databases for comparisons of group and individual are included to support a statistically-based diagnosis.

• Nuclear Engineering and Technology
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• v.51 no.3
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• pp.683-691
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• 2019

In this paper, we propose a novel method to quantify the neural synchronization between subjects in the collaborative process through electroencephalogram (EEG) hyperscanning. We hypothesized that the neural synchronization in EEGs will increase when the communication of the operators is smooth and the teamwork is better. We quantified the EEG signal for multiple subjects using a representative EEG quantification method, and studied the changes in brain activity occurring during collaboration. The proposed method quantifies neural synchronization between subjects through bispectral analysis. We found that phase synchronization between EEGs of multi subjects increased significantly during the periods of collaborative work. Traditional methods for a human error analysis used a retrospective analysis, and most of them were analyzed for an unspecified majority. However, the proposed method is able to perform the real-time monitoring of human error and can directly analyze and evaluate specific groups.

• Investigative Magnetic Resonance Imaging
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• v.8 no.1
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• pp.1-8
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• 2004

Purpose : Information about electrical activity inside the brain during fMRl scans is very useful in monitoring physiological function of the patient or locating the spatial position of the activated region in the brain. However, many additional noises appear in the EEG signal acquired during the MRI scan. Gradient induced noise is the biggest one among the noises. In this work, we propose a gradient noise reduction method using the independent component analysis (ICA) method. Materials and Methods : We used a 29-channel MR-compatible EEG measurement system and a 3.0 Tesla MRI system. We measured EEG signals on a subject lying inside the magnet during EPI scans. We selectively removed the gradient noise from the measured EEG signal using the ICA method. We compared the results with the ones obtained with conventional averaging method and PCA method. Results : All the noise reduction methods including the averaging and PCA methods were effective in removing the noise in some extent. However, the proposed ICA method was found to be superior to the other methods. Conclusion : Gradient noise in EEG signals acquired during fMRI scans can be effectively reduced by the ICA method. The noise-reduced EEG signal can be used in fMRI studies of epileptic patients or combinatory studies of fMRI and EEG.

• Journal of Korea Multimedia Society
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• v.6 no.1
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• pp.167-175
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• 2003

In this paper, a new electrode configuration and EOG removal method are proposed in order to measure EEG effectively on the frontal lobe and remove EOG in the measured raw EEG. The method of measuring EEG is proposed using four electrodes and a ground electrode on the frontal lobe with a reference electrode at the left earlobe. And also, the separation method using ICA is proposed for EOG removal from the measured EEG, Through the experiments of measuring EEG it was demonstrated that a subject can attach the electrodes by himself easily to measure his own EEG without any assistant and the proposed methods were suitable for removing EOG signal from the measured EEG.

• Journal of Biomedical Engineering Research
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• v.43 no.4
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• pp.185-192
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• 2022

Early detection of mild cognitive impairment can help prevent the progression of dementia. The purpose of this study was to design and validate a machine learning model that automatically differential diagnosed patients with mild cognitive impairment and identified cognitive decline characteristics compared to a control group with normal cognition using resting-state quantitative electroencephalogram (qEEG) with eyes closed. In the first step, a rectified signal was obtained through a preprocessing process that receives a quantitative EEG signal as an input and removes noise through a filter and independent component analysis (ICA). Frequency analysis and non-linear features were extracted from the rectified signal, and the 3067 extracted features were used as input of a linear support vector machine (SVM), a representative algorithm among machine learning algorithms, and classified into mild cognitive impairment patients and normal cognitive adults. As a result of classification analysis of 58 normal cognitive group and 80 patients in mild cognitive impairment, the accuracy of SVM was 86.2%. In patients with mild cognitive impairment, alpha band power was decreased in the frontal lobe, and high beta band power was increased in the frontal lobe compared to the normal cognitive group. Also, the gamma band power of the occipital-parietal lobe was decreased in mild cognitive impairment. These results represented that quantitative EEG can be used as a meaningful biomarker to discriminate cognitive decline.

• Journal of the Ergonomics Society of Korea
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• v.13 no.2
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• pp.43-48
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• 1994

Among many kinds of odors, some are known to have effects of sedation or stimulation on brain activity. In this study, brain activity levels affected by four kinds of fragrance0lemon, lavender, jasmine, and rose-were tested using EEG recording. In the first experiment, the quality of alpha wave was examined under controlled rest condition. In the second experiment, the event-related potential (ERP) and contingent negative variation (CNV) were investigated during a simple reaction tasks (SRT) against auditory signal. EEG data obtained for the rest condition were analyzed suing "3-Dimensional Viewer)" which was developed by ourselves to show the chaotic attractor of the signal. Power spectrum were also calculated using FET. EEG data obtained during the SRT were analyzed by comparing CNV amplitudes about each odor condition. Results confirmed the sedative effect of the lemon and the lavender, and the stimulative effect of the jasmine and the rose.

• Journal of Biomedical Engineering Research
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• v.6 no.1
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• pp.1-4
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• 1985

Background EEG signals can be represented as the sum of a conventional AR process and an innovation process. It is know that conventional estimation techniques, such as least square estimates (LSE) or Gauasian maximum likelihood estimates (MLE-G) are optimal when the innovation process satisfies the Gaussian or presumed distribution. When the data are contaminated by outliers, however, these assumptions are not met and the power spectrum estimated by conventional estimation techniques may be fatally biased. EEG signal may be affected by artifacts, which are outliers in the statistical term. So the robust filtering estimation technique is used against those artifacts and it performs well for the contaminated EEG signal.

• Journal of IKEEE
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• v.24 no.1
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• pp.269-274
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• 2020

This paper is to study the exoskeleton robot for the walking of the elderly and the disabled. We developed and tested an Exoskeletal robot with two axes of freedom for joint motion. The EEG and EMG signals were used to move the joints of the Exoskeletal robot. By analyzing the EMG signal, the control signal was extracted and applied to the robot to facilitate the walking operation of the walking assistance robot. In addition, the brain-computer interface technology is applied to perform the operation of the robot using brain waves, spontaneous electrical activities recorded on the human scalp. These two signals were fused to study the walking recognition method of the supporting robot leg.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.2083-2085
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• 2009

Brain Computer Interface (BCI) is a communication pathway between devices (computers) and human brain. It treats brain signals in real-time basis and discriminates some information of what human brain is doing. In this work, we develop a EEG BCI system using a feature extraction such as common spatial pattern (CSP) and a classifier using Fisher linear discriminant analysis (FLDA). Two-class EEG motor imagery movement datasets with both cued and uncued are tested to verify its feasibility.