• Fashion & Textile Research Journal
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• v.6 no.6
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• pp.810-814
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• 2004

This study is one of the fundamental researches for the development of future smart clothing and textile products using silver(Ag) nano powder. Our study was focused on the blocking or insulating effects of nano-processed textiles from electromagnetic waves. Also, for the surveying of the actual effect to human body, we measure the variation of electroencephalogram which is an indication of human physical symptoms. Among various textiles in this experiment, nano silver processed case has shown the best blocking performance from the electromagnetic waves, which decreases depending on the distance. As a reference model of working environment, we setup the visual stimuli object on the computer that is a source of electromagnetic wave. The power spectrum distribution and the incidence of electroencephalogram was measured. The analysed data has shown that, with nano-processed textiles, ${\beta}$ wave does not appear very often where ${\beta}$ wave appears only to illustrate the stable states of human's body. However, as for the materials without nano processing, the ratio of ${\gamma}$ waves in the total level of electroencephalogram becomes higher in spite of short exposure to visual stimuli in work environment, which shows that the worker becomes stressed. The ${\beta}$ wave electroencephalogram of all materials is drawn in calcarine fissure of occipital lobe to show the convergent distribution, and stronger with block-processed Nano Silver Silk(NSS). The study based on the potential risks of human diseases such as physical fatigue by electromagnetic waves, and has shown that the application of Nano Silver textile for human uses require a proper particle size of it which would not penetrate cellular tissues, and a proper binder and binding treatment for it. However, it is highly required for back-up researches to verify various aspects in applying nano silver to textile products.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.1
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• pp.38-46
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• 2024

In general, EEG signal analysis has been the subject of several studies due to its ability to provide an objective mode of recording brain stimulation, which is widely used in brain-computer interface research with applications in medical diagnosis and rehabilitation engineering. In this study, we developed EEG reception hardware to measure electroencephalograms and implemented a processing system, classifying it into server and data processing. It was conducted as an intermediate-stage research on the implementation of a brain-computer interface using electroencephalograms, and was implemented in the form of predicting the user's arm movements according to measured electroencephalogram data. Electroencephalogram measurements were performed using input from four electrodes through an analog-to-digital converter. After sending this to the server through a communication process, we designed and implemented a system flow in which the server classifies the electroencephalogram input using a convolutional neural network model and displays the results on the user terminal.

• Journal of the Korean Applied Science and Technology
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• v.35 no.4
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• pp.1175-1184
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• 2018

The purpose of this study was to investigate effects of 12-weeks finswimming exercise on electroencephalogram(EEG), SBP, DBP, and RHR in male adolescents. Eighteen male adolescents participated in this study. They were separated into a Control group(CG; n=9) and Finswimming training group(FG; n=9). FG participated in Finswimming training for 12weeks, 60 minutes per day, 3 times a week. All data of electroencephalogram were analyzed by repeated measures two-way ANOVA and Data of SBP, DBP and RHR were analyzed by ANCOVA and Paired t-test. As a result, Alpha and SMR waves were significantly increased in FG; however, Alpha wave was significantly decreased in CG and Theta wave was significantly decreased in FG. There were significant interaction in Alpha, Theta, and SMR waves. SBP, DBP, and RHR were significantly decreased in FG and there were significant differences of RHR and SBP between groups; otherwise, there were no significant differences of DBP between groups. The results of this study showed that 12 weeks of Finswimming training positively effects on electroencephalogram(EEG), SBP, DBP, and RHR in male adolescents.

• Journal of Integrative Natural Science
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• v.11 no.4
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• pp.167-183
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• 2018

Electroencephalogram (EEG) recording provides a new way to support human-machine communication. It gives us an opportunity to analyze the neuro-dynamics of human cognition. Machine learning is a powerful for the EEG classification. In addition, machine learning can compensate for high variability of EEG when analyzing data in real time. However, the optimal EEG electrode location must be prioritized in order to extract the most relevant features from brain wave data. In this paper, we propose an intelligent system model for the extraction of EEG data by training the optimal electrode location of EEG in a specific problem. The proposed system is basically a fuzzy system and uses a neural network structurally. The fuzzy clustering method is used to determine the optimal number of fuzzy rules using the features extracted from the EEG data. The parameters and weight values found in the process of determining the number of rules determined here must be tuned for optimization in the learning process. Genetic algorithms are used to obtain optimized parameters. We present useful results by using optimal rule numbers and non - symmetric membership function using EEG data for four movements with the right arm through various experiments.

• Korean Journal of Acupuncture
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• v.29 no.3
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• pp.467-475
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• 2012

Objectives : The aim of this study was to examine the effects of Acupuncture at the GV20 and GV22 on normal human beings using power spectrum analysis. Methods : Electroencephalogram(EEG) power spectrum exhibits site-specific and state-related differences in various frequency bands. 8 channels Background Electroencephalogram (EEG) was carried out in 30 subjects(24 females and 4 males). Results : In ${\delta}$(theta) band, the power values decreased significantly at the 8-channel average value(p=0.03) and especially at T3(p=0.02), T4(p=0.001) and P3(p=0.03). In ${\alpha}$(alpha) band, the power values have no significant changes. In ${\beta}$(beta)band, the power values increased significantly at the 8-channel average value (p=0.02) and especially at T4(p=0.003), P3 (p= 0.03) and P4(0.02). In ${\beta}/{\delta}$(beta/theta) ratio, the value increased significantly at the 8-channel average value(p=0.002) and especially at Fp2(p=0.05), F4(p=0.007), T3(0.012), T4(0.005), P3 (0.007) and P4(0.03) Conclusions : Through this data, we conclude that acupuncture at the GV20 and GV22 on normal human beings could have possibility to awake the cerebral cortex by the functional mechanism.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.2
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• pp.178-183
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• 2013

In this study, we proposed a method for electroencephalogram (EEG) classification using invariant CSP at special channels for improving the accuracy of classification. Based on the naive EEG signals from left and right hand movement experiment, the noises of contaminated data set should be eliminate and the proposed method can deal with the de-noising of data set. The considering data set are collected from the special channels for right and left hand movements around the motor cortex area. The proposed method is based on the fit of the adjusted parameter to decline the affect of invariant parts in raw signals and can increase the classification accuracy. We have run the simulation for hundreds time for each parameter and get averaged value to get the last result for comparison. The experimental results show the accuracy is improved more than the original method, the highest result reach to 89.74%.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.1
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• pp.59-64
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• 2015

In this paper, we found the usefulness of the deep belief network (DBN) in the fields of brain-computer interface (BCI), especially in relation to imagined speech. In recent years, the growth of interest in the BCI field has led to the development of a number of useful applications, such as robot control, game interfaces, exoskeleton limbs, and so on. However, while imagined speech, which could be used for communication or military purpose devices, is one of the most exciting BCI applications, there are some problems in implementing the system. In the previous paper, we already handled some of the issues of imagined speech when using the International Phonetic Alphabet (IPA), although it required complementation for multi class classification problems. In view of this point, this paper could provide a suitable solution for vowel classification for imagined speech. We used the DBN algorithm, which is known as a deep learning algorithm for multi-class vowel classification, and selected four vowel pronunciations:, /a/, /i/, /o/, /u/ from IPA. For the experiment, we obtained the required 32 channel raw electroencephalogram (EEG) data from three male subjects, and electrodes were placed on the scalp of the frontal lobe and both temporal lobes which are related to thinking and verbal function. Eigenvalues of the covariance matrix of the EEG data were used as the feature vector of each vowel. In the analysis, we provided the classification results of the back propagation artificial neural network (BP-ANN) for making a comparison with DBN. As a result, the classification results from the BP-ANN were 52.04%, and the DBN was 87.96%. This means the DBN showed 35.92% better classification results in multi class imagined speech classification. In addition, the DBN spent much less time in whole computation time. In conclusion, the DBN algorithm is efficient in BCI system implementation.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.768-773
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• 2012

One of the main reasons for serious road accidents is driving while drowsy. For this reason, drowsiness detection and warning system for drivers has recently become a very important issue. Monitoring physiological signals provides the possibility of detecting features of drowsiness and fatigue of drivers. One of the effective signals is to measure electroencephalogram (EEG) signals and electrooculogram (EOG) signals. The aim of this study is to extract drowsiness-related features from a set of EEG signals and to classify the features into three states: alertness, drowsiness, sleepiness. This paper proposes a drowsiness detection system using Linear Predictive Coding (LPC) coefficients and Support Vector Machine (SVM). Samples of EEG data from each predefined state were used to train the SVM program by using the proposed feature extraction algorithms. The trained SVM program was tested on unclassified EEG data and subsequently reviewed according to manual classification. The classification rate of the proposed system is over 96.5% for only very small number of samples (250ms, 64 samples). Therefore, it can be applied to real driving incident situation that can occur for a split second.

• Journal of the Ergonomics Society of Korea
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• v.27 no.2
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• pp.9-14
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• 2008

Coherence can be used to evaluate the functional cortical connections between the motor cortex and muscle. This study is to find coherence between EEG (electroencephalogram) and EMG (electromyogram) evoked by movement of a hand. Seven healthy participants were asked to perform thirty repetitive movement of right hand for ten seconds with rest for ten seconds. Specific feature of EEG components has been extracted by ICA (independent component analysis) and coherence between EEG and EMG was analyzed from data measured EEG in five local areas around central part of head and EMG in flexer carpri radialis muscle during grabbing movement. Coherence between EEG and EMG was successfully obtained at 0.025 confidence limit during hand movement and showed significant difference between rest and movement at 13-18Hz.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.4
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• pp.277-282
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• 2015

Braincomputer interface (BCI) technology is making advances in the field of humancomputer interaction (HCI). To improve the BCI technology, we study the changes in the electroencephalogram (EEG) signals for six levels of grip strength: 10%, 20%, 40%, 50%, 70%, and 80% of the maximum voluntary contraction (MVC). The measured EEG data are categorized into three classes: Weak, Medium, and Strong. Features are then extracted using power spectrum analysis and multiclass-common spatial pattern (multiclass-CSP). Feature datasets are classified using a support vector machine (SVM). The accuracy rate is higher for the Strong class than the other classes.