• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2011.05a
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• pp.39-40
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• 2011

• 대한임상약리학회:학술대회논문집
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• 1994.12a
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• pp.26-26
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• 1994

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2013.04a
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• pp.112-113
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• 2013

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.680-680
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• 2022

• Korean Journal of Clinical Laboratory Science
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• v.37 no.2
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• pp.123-128
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• 2005

Magnetoencephalography (MEG) is the measurement of the magnetic fields produced by electrical activity in the brain, usually conducted externally, using extremely sensitive devices such as Superconducting Quantum Interference Device (SQUID). MEG needs complex and expensive measurement settings. Because the magnetic signals emitted by the brain are on the order of a few femtoteslas (1 fT = 10-15T), shielding from external magnetic signals, including the Earth's magnetic field, is necessary. An appropriate magnetically shielded room is very expensive, and constitutes the bulk of the expense of an MEG system. MEG is a relatively new technique that promises good spatial resolution and extremely high temporal resolution, thus complementing other brain activity measurement techniques such as electroencephalography (EEG), positron emission tomography (PET), single-photon emission computed tomography (SPECT) and functional magnetic resonance imaging (fMRI). MEG combines functional information from magnetic field recordings with structural information from MRI. The clinical uses of MEG are in detecting and localizing epileptic form spiking activity in patients with epilepsy, and in localizing eloquent cortex for surgical planning in patients with brain tumors. Magnetoencephalography may be used alone or together with electroencephalography, for the measurement of spontaneous or evoked activity, and for research or clinical purposes.

• Physical Therapy Rehabilitation Science
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• v.6 no.4
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• pp.164-169
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• 2017

Objective: This research investigated the effects of simple visual imagery and mental rotation imagery on neural activity of adults who are at high risk of smart phone addiction by measuring their electroencephalography (EEG). Design: Cross-sectional study. Methods: Thirty people with a high risk of smart phone addiction was selected and then were evaluated for their neural activation patterns using EEG after reminding them about simple visual imagery and mental rotation imagery. A simple visual image was applied for 20 seconds using a smartphone. This was followed by a resting period of 20 seconds. Mental rotation imagery was applied for 20 seconds. During mental rotation imagery, the rotational angle was selected at random. We compared activation patterns according to the analyzed EEG with hemisphere reminding them about imagery. Results: On the EEG, theta rhythm from the left hemisphere parietal area increased when the subjects were reminded of mental rotation imagery, and sensorimotor rhythm from close to the left hemisphere area increased when the subjects were reminded of simple visual imagery. Conclusions: Neural activation from the left hemisphere occurs for motor imagery in adults who are at high risk of smart phone addiction. These results identify a neural mechanism of adults who a have high risk of smart phone addiction, which may provide contribute to the development of motor rehabilitation for smartphone users.

• Journal of Biomedical Engineering Research
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• v.30 no.1
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• pp.10-17
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• 2009

Accurate electroencephalography (EEG) forward calculation is of importance for the accurate estimation of neuronal electrical sources. Conventional studies concerning the EEG forward problems have investigated various factors influencing the forward solution accuracy, e.g. tissue conductivity values in head compartments, anisotropic conductivity distribution of a head model, tessellation patterns of boundary element models, the number of elements used for boundary/finite element method (BEM/FEM), and so on. In the present paper, we investigated the influence of modeling errors in the boundary element volume conductor models upon the accuracy of the EEG forward solutions. From our simulation results, we could confirm that accurate construction of boundary element models is one of the key factors in obtaining accurate EEG forward solutions from BEM. Among three boundaries (scalp, outer skull, and inner skull boundary), the solution errors originated from the modeling error in the scalp boundary were most significant. We found that the nonuniform error distribution on the scalp surface is closely related to the electrode configuration and the error distributions on the outer and inner skull boundaries have statistically meaningful similarity to the curvature distributions of the boundary surfaces. Our simulation results also demonstrated that the accumulation of small modeling errors could lead to considerable errors in the EEG source localization. It is expected that our finding can be a useful reference in generating boundary element head models.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.1
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• pp.12-18
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• 2013

Motor imagery classification in electroencephalography (EEG)-based brain-computer interface (BCI) systems is an important research area. To simplify the complexity of the classification, selected power bands and electrode channels have been widely used to extract and select features from raw EEG signals, but there is still a loss in classification accuracy in the state-of- the-art approaches. To solve this problem, we propose a discriminative feature extraction algorithm based on power bands with principle component analysis (PCA). First, the raw EEG signals from the motor cortex area were filtered using a bandpass filter with ${\mu}$ and ${\beta}$ bands. This research considered the power bands within a 0.4 second epoch to select the optimal feature space region. Next, the total feature dimensions were reduced by PCA and transformed into a final feature vector set. The selected features were classified by applying a support vector machine (SVM). The proposed method was compared with a state-of-art power band feature and shown to improve classification accuracy.

• Biomolecules & Therapeutics
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• v.26 no.4
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• pp.343-349
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• 2018

Although drugs such as barbiturates and benzodiazepines are often used for the treatment of insomnia, they are associated with various side effects such as habituations, tolerance and addiction. Alternatively, natural products with minimal unwanted effects have been preferred for the treatment of acute and/or mild insomnia, with additional benefits of overall health-promotion. Basic and clinical researches on the mechanisms of action of natural products have been carried out so far in insomnia treatments. Recent studies have been focusing on diverse chemical components available in natural products, with an interest of developing drugs that can improve sleep duration and quality. In the last 15 years, our co-workers have been actively looking for candidate substances from natural products that can relieve insomnia. This review is, therefore, intended to bring pharmacological data regarding to the effects of natural products on sleep duration and quality, mainly through the activation of $GABA_A$ receptors. It is imperative that phytochemicals will provide useful information during electroencephalography (EEG) analysis and serve as an alternative medications for insomnia patients who are reluctant to use conventional drugs.

• Journal of Multimedia Information System
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• v.2 no.2
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• pp.215-222
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• 2015

Epileptic Seizure is a popular brain disease in the world. It affects the nervous system and the activities of brain function that make a person who has seizure signs cannot control and predict his actions. Based on the Electroencephalography (EEG) signals which are recorded from human or animal brains, the scientists use many methods to detect and recognize the abnormal activities of brain. Tucker model is investigated to solve this problem. Tucker decomposition is known as a higher-order form of Singular Value Decomposition (SVD), a well-known algorithm for decomposing a matric. It is widely used to extract good features of a tensor. After decomposing, the result of Tucker decomposition is a core tensor and some factor matrices along each mode. This core tensor contains a number of the best information of original data. In this paper, we used Tucker decomposition as a way to obtain good features. Training data is primarily applied into the core tensor and the remained matrices will be combined with the test data to build the Tucker base that is used for testing. Using core tensor makes the process simpler and obtains higher accuracies.