• 한국의류산업학회지
• /
• 제6권6호
• /
• pp.810-814
• /
• 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.

• 한국정보전자통신기술학회논문지
• /
• 제17권1호
• /
• pp.38-46
• /
• 2024

일반적으로 EEG 신호 분석은 의료 진단 및 재활 공학에 적용하여 뇌-컴퓨터 인터페이스 연구에 널리 사용되는 뇌 자극을 기록하는 객관적인 모드를 제공할 수 있는 능력 때문에 여러 연구의 주제가 되어 왔습니다. 본 연구에서는 뇌전도 측정하기 위한 뇌파 수신 하드웨어 개발 및 처리 시스템 구현을 통해 서버와 데이터 처리로 분류하여 개발을 진행하였다. 뇌전도를 이용한 뇌-컴퓨터 인터페이스 구현의 중간단계 연구로 진행되었으며, 측정된 뇌전도 데이터에 따라 사용자의 팔의 움직임을 예측하는 형태로 구현되었다. 네 개의 전극으로부터의 입력을 아날로그-디지털 변환기를 통해 뇌전도 측정을 수행하였다. 이를 통신 과정을 거쳐 서버에 전송한 뒤, 서버에서 합성곱 신경망 모델로 뇌전도 입력을 분류하여 그 결과를 사용자 단말로 표시하는 시스템의 흐름을 설계하고 구현하였다.

• 한국응용과학기술학회지
• /
• 제35권4호
• /
• pp.1175-1184
• /
• 2018

본 연구는 12주간의 핀수영 운동이 남자 청소년의 뇌파, 혈압 및 안정 시 심박수에 미치는 영향을 규명하는데 그 목적이 있다. 18명의 남자 청소년을 핀수영 운동집단 9명과 통제집단 9명으로 구성하였다. 핀수영 운동집단은 12주간 주3회 60분씩 핀수영을 실시하였다. 측정된 자료 중 뇌파변인들은 이원변량 반복측정 분산분석(Two way repeated measures ANOVA)에 의해 분석되었고 안정 시 심박수와 혈압 변인들의 분석은 공분산 분석(ANCOVA)과 대응표본 t-test(Paired t-test)를 실시하였다. 결과적으로 핀수영 집단에서는 Alpha파와 SMR파의 유의한 증가가 나타났고, Theta파의 유의한 감소가 나타났다. 통제집단에서는 Alpha파의 유의한 감소가 나타났다. Alpha파, Theta파와 SMR파 모두에서 시기와 집단 간 유의한 상호작용이 나타났다. 또한 핀수영 집단에서 안정 시 심박수, 수축 및 이완기 혈압의 유의한 감소가 나타났고 안정 시 심박수와 수축기 혈압에서 집단 간 유의한 차이가 나타났다. 하지만 이완기 혈압에서는 집단 간 유의한 차이가 나타나지 않았다. 이상의 결과로 12주간의 핀수영 운동은 청소년의 뇌파, 안정 시 심박수 및 혈압에 긍정적인 영향을 미친 것으로 보인다.

• 통합자연과학논문집
• /
• 제11권4호
• /
• pp.167-183
• /
• 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
• /
• 제29권3호
• /
• pp.467-475
• /
• 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.

• 한국지능시스템학회논문지
• /
• 제23권2호
• /
• pp.178-183
• /
• 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%.

• 제어로봇시스템학회논문지
• /
• 제21권1호
• /
• pp.59-64
• /
• 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.

• 한국지능시스템학회논문지
• /
• 제22권6호
• /
• pp.768-773
• /
• 2012

운전 중 운전자의 졸음은 교통 사망사고를 일으키는 중요한 요인이며 음주운전보다도 더 위험할 수 도 있다. 이러한 이유로 운전자의 졸음을 판별하고 경고하는 시스템 개발이 최근에 매우 중요한 이슈로 떠올랐다. 그중에서도 졸음과 가장 밀접한 관련이 있는 생체 신호 분석이 많이 적용되는데 그중에서도 뇌파(Electroencephalogram, EEG)와 안구전도(Electrooculogram, EOG)를 분석하는 연구가 주류를 이루고 있다. 본 논문에서는 실험 프로토콜를 바탕으로 측정된 뇌파를 주파수별로 분석하여 운전자의 상태별 뇌파 데이터베이스를 구축하였고 선형예측(Linear Predictive Coding, LPC) 계수와 Support Vector Machine(SVM)을 이용한 운전자 졸음 감지 시스템을 제안한다. 실험결과로 졸음의 뇌파분석에서 알파파가 감소하며 세타파가 증가하는 추세를 보였으며, LPC 계수가 각성, 졸음 및 수면상태의 특징을 잘 반영하였다. 특히 제안한 시스템은 적은 샘플(250ms)에서도 96.5%의 높은 분류 결과를 얻어 짧은 순간에 일어날 운전시 돌발 상황을 실시간으로 예측할 수 있는 가능성을 보였다.

• 대한인간공학회지
• /
• 제27권2호
• /
• pp.9-14
• /
• 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
• /
• 제15권4호
• /
• pp.277-282
• /
• 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.