• Therapeutic Science for Rehabilitation
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• v.7 no.2
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• pp.9-16
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• 2018

Objective : The aim of this research was to provide EEG (electroencephalogram) basic data in clinical areas through identifying measurement tools, measurement methods, and evaluation and analysis method of the EEG which is a neurological change measurement of patients with brain injury. Methods : Previous studies were found in an electronic database (e.g., PubMed, Science Direct). The keyword search terms were 'Electroencephalography', 'stroke', 'intervention OR training'. Results : Utilitizing brain-computer interface, the EEG, which is a tool for measuring the effects of rehabilitation through changes of brain activation state. Also, it could identify functional brain reorganization mechanism. Whenever a research utilized the EEG, which is composed of various channels, different types of electrode, and varied electrode locations. Conclusions : Through this review, we found that Electroencephalography is possible to neurologically verify the effectiveness of intervention and formulate an intervention strategy for efficient occupational therapy.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.7
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• pp.508-514
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• 2018

An electroencephalogram (EEG) evaluates the electrical activity generated by brain cell interactions that occur during brain activity, and an EEG can evaluate the brain activity caused by hand movement. In this study, a 16-channel EEG was used to measure the EEG generated before and after hand movement. The measured data can be classified as a supervised learning model, a support vector machine (SVM). To shorten the learning time of the SVM, a feature extraction and vector dimension reduction by filtering is proposed that minimizes motion-related information loss and compresses EEG information. The classification results showed an average of 72.7% accuracy between the sitting position and the hand movement at the electrodes of the frontal lobe.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.5
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• pp.18-23
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• 2010

In this paper, we propose a computationally efficient method detecting the P300 wave for brain-machine interface. Electrophysiological researches have shown that the P300 wave's potential is decreased when human intention matches visual stimulation. Motivated by this fact, we can infer human intention for brain-machine interface by detecting the P300 wave's potential decrease. The P300 wave is recorded from EEG(electroencephalogram) electrodes attached on human brain skull after giving alphabetical stimulation. To detect the potential decrease in P300, firstly we statistically model the P300 wave's negative potential. Then we infer human intention based on maximum likelihood estimation. The proposed method was evaluated on the data recorded from three healthy human subjects. The method achieved an averaging accuracy of 98% from subject k, 90% from subject j and 79.8% from subject h.

• Weed & Turfgrass Science
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• v.7 no.2
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• pp.130-139
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• 2018

The colors of packaging of herbicides and non-selective herbicides on the market in Korea are defined as brown and red, respectively, according to the notification of RDA. The present study aimed to understand consumer's electroencephalographic (EEG) response when looking at brown and red colors of herbicide and non-selective herbicide packaging papers. The EEG cap was placed on the scalp of each participant (men and women, 10 to 20 years old) and white (control) - brown - white - red colors were sequentially displayed for 5 seconds using the computer monitor. The EEG was measured and statistical analysis was performed using SPSS. For the brown color of the herbicide, men showed a decrease in concentration and a distracting response due to a decrease in the ratio of mid beta to theta (RMT) and the spectral edge of frequency (SEF90). In women, an increase in the ratio of SMR to theta (RSMT) and the spectral edge frequency 50% of the alpha (ASEF) was observed in different brain regions and these EEG changes may enhance the relaxation, stabilization and awakening states of the brain. For the red color of the non-selective herbicide, ASEF increased psychological stability in men. In women, a decrease in absolute high beta (AHB) may associate with a decrease in attention state of the brain. Overall data of the present study clearly revealed that the colors of two herbicides showed significantly different EEG response and gender difference.

• 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.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.2
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• pp.172-177
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• 2013

With Brain Computer Interface (BCI) system, a person with disabled limb could use this direct brain signal like electroencephalography (EEG) to control a device such as the artifact arm. The precise force control for the artifact arm is necessary for this artificial limb system. To understand the relationship between control EEG signal and the gripping force of hands, We proposed a study by measuring EEG changes of three grades (25%, 50%, 75%) of hand grip MVC (Maximal Voluntary Contract). The acquired EEG signal was filtered to obtain power of three wave bands (alpha, beta, gamma) by using fast fourier transformation (FFT) and computed power spectrum. Then the power spectrum of three bands (alpha, beta and gamma) of three classes (MVC 25%, 50%, 75%) was classified by using PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis). The result showed that the power spectrum of EEG is increased at MVC 75% more than MVC 25%, and the correct classification rate was 52.03% for left hand and 77.7% for right hand.

• Korean Journal of Clinical Laboratory Science
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• v.50 no.4
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• pp.492-500
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• 2018

This study examined the effects of a dual-task virtual reality program on the cognitive function and EEG for patients with mild cognitive impairment. A dual-task virtual reality program was performed in the experimental groups while conventional occupational therapy was carried out in the control group for 30 minutes per session, which was done five days per week for 6 weeks. The results were as follows. First, the memory of the cognitive function and balance was improved significantly in the experimental group with the dual-task virtual reality program compared to the control group with the traditional occupational therapy. Second, EEG was also increased significantly in the experimental group compared to the control group. The results of this study suggest that the dual-task virtual reality program was an effective treatment method for the elderly with mild cognitive impairment and would be a cornerstone of basic data that will be helpful to those suffering from a range of diseases.

• The Journal of the Korea Contents Association
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• v.21 no.2
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• pp.140-147
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• 2021

EEG(electroencephalogram) are measured to accurately determine the level of sleep in various sleep examinations. In general, measurements are more accurate as the number of sensor channels increases. EEG can interfere with sleep by attaching electrodes to the skin when measuring. It is necessary for self sleep care to select the minimum number of EEG channels that take into account both the user's discomfort and the accuracy of the measurement data. In this paper, we proposed a sleep stage analysis model based on machine learning and conducted experiments for using from one channel to four channels. We obtained estimation accuracy for sleep stage as following 82.28% for one channel, 85.77% for two channels, 80.33% for three channels and 68.87% for four channels. Although the measurement location is limited, the results of this study compare the accuracy according to the number of channels and provide information on the selection of channel numbers in the EEG sleep analysis.

• Journal of People, Plants, and Environment
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• v.22 no.2
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• pp.127-143
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• 2019

This study investigates the color stimuli of two varieties of foliage plants by extracting electroencephalogram, electrocardiogram and physiology activity data from 30 participants in their 50s or older. Changes in the physiological activity of subjects against six color stimuli were examined. The stimulus to real green plants 'Silver Queen' was set as the control group, and was compared with other groups including the stimulus to real 'Angel' plants and four stimuli to artificial colors (two color images and color schemes of the same green and red plants). Compared to the five groups, the relative theta power spectrum (RT) and the ratio of alpha to high beta (RAHB) increased in the subjects exposed to real green plants. This result demonstrates that the green plant ('Silver Queen') increases the stability, relaxation, and internal concentration of subjects in a proper state of awakening. The result of this experiment showed a statistically significant difference in the level of RT when subjects were exposed to the groups of real green and red plants. This finding indicates that the green plant increases internal concentration more than the red plant. RT and the relative low beta power spectrum (RLB) in the groups of natural colors were higher than the groups of artificial colors when subjects focused their mind on the two types of real plants. However, the level of relative mid beta power spectrum (RMB), ratio of SMR to theta (RST), ratio of mid beta to theta (RMT), relative high beta power spectrum (RHB), and spectral edge frequency 95% were higher when subjects were exposed to the photos and colors scheme of plants than when they were exposed to real plants. The subjects experienced more "comfortable" emotions when they were looking at plants with green colors. Overall, it is recommended to use the natural colors of real plants in places where which stability and relaxation are required. On the contrary, the artificial colors of plants such as their photos and color schemes are useful in places where a high level of concentration is required in a short period of time.

• Sleep Medicine and Psychophysiology
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• v.25 no.2
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• pp.82-91
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• 2018

Objectives: Synchronous electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) has been used to explore sleep stage dependent functional brain networks. Despite a growing number of sleep studies using EEG-fMRI, few studies have conducted network analysis on whole night sleep due to difficulty in data acquisition, artifacts, and sleep management within the MRI scanner. Methods: In order to perform network analysis for whole night sleep, we proposed experimental procedures and data processing techniques for EEG-fMRI. We acquired 6-7 hours of EEG-fMRI data per participant and conducted signal processing to reduce artifacts in both EEG and fMRI. We then generated a functional brain atlas with 68 brain regions using independent component analysis of sleep fMRI data. Using this functional atlas, we constructed sleep level dependent functional brain networks. Results: When we evaluated functional connectivity distribution, sleep showed significantly reduced functional connectivity for the whole brain compared to that during wakefulness. REM sleep showed statistically different connectivity patterns compared to non-REM sleep in sleep-related subcortical brain circuits. Conclusion: This study suggests the feasibility of exploring functional brain networks using sleep EEG-fMRI for whole night sleep via appropriate experimental procedures and signal processing techniques for fMRI and EEG.