• Proceedings of the ESK Conference
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• 1996.10a
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• pp.56-56
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• 1996

인간의 사고 기능과 학습기능이 '뇌'를 바탕으로 이루어진다고 할 때, 뇌의 긴장을 풀어 원래의 건강하고 창조적인 이완 상태에서의 학습 상태가 유효하게 된다. 즉, 뇌파가 " $\alpha$" 상태 가 되었을 때 긴장된 신체의 각 부분이 충분히 이완되고 두뇌는 맑고 건강한 상태를 유지하여 활발하고 창조적인 상태가 된다. 뇌파의 측정 및 분석된 기존 연구에 의하면, 인간의 심리상태와 신체 상태, 행동 패턴에도 직, 간접적인 영향을 주어 뇌파의 조절이 인간 잠재능력 개발의 첩경이라는 결과가 보고되어진다. 이러한 관점에서 본 연구에서는 뇌의 특성을 고려하여 뇌를 이완시킴으로써 학생들의 학습능력을 향상시키 기 위한 새로운 접근 방법을 시도한다. 뇌파 조절이 가해지는 환경하에 학습 효율의 변화 정도를 정성적( 심리적, 학습적, 신체적) 및 정량적(영어 단어 암기력 TEST, 뇌파 특정 등)으로 평가 및 분석을 통해 뇌파 조절 효과가 학생들의 학습 효율을 더욱 향상 시킬 수 있는 지의 타당성을 검증하고 그 결과를 바탕으로 새로운 학습 방법을 모색하고자 한다. 한편, 본 연구에서는 실험 대상을 본 대학 임의의 2학년 학생 13 명 을 대상으로 하고 실험기간은 약 4개월에 걸쳐 실험 하였다. 뇌파 측정은 13명중 임의의 학생 7명을 선정 하여 각 40분씩 측정 분석하였다. 또한 영어 단어 암기력 TEST를 실시하여 그 결과를 뇌파 조절 전,후로 나누어 비교 분석하였다. 정성적 분석으로서 종합 설문지를 이용한 15 개 항목의 5점 척도를 사용하여 분석하였으며 가가 통계 이론을 이용하여 검증하였다. 뇌파 측정은 수행 전후 비교 결과 " .alpha. " 노출 비율이 수행 전보다 수행 후가 다소 높은 비유로 나타났으며, 특히 영어 단어 암기력은 평균적으로 크게 상승되는 것으로 나타났다. 정성적 분석 결과에서는 많은 심리적 변화 상태가 나타나고 있지만 전체적으로 마음의 안정감, 몸의 긴장 이완에 따른 건강 상태 유지, 수업 집중도 향상 등이 나타났다. 위와 같은 종합 적 분석 결과에 따라, 본 연구는 제조 현장의 생산성 향상 및 품질 향상과 연계하여 작업자의 작업 집중도 향상, 작업자의 육체적, 심리적 변화에 따른 생산성 및 품질 향상 변화 정도 등의 산업공학(인간공학) 제 분야의 여러 측면에서 연구 및 적용이 가능하리라 사료된다.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.2177-2178
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• 2006

학습 행동에서의 뇌파 측정은 실시간으로 두뇌 기능 상태를 연구하는데 유용한 연구 방법이며 대뇌의 부위 중 전두엽은 새로움에 대한 지향 반응과 사고 활동에 중요한 역할을 한다. 본 연구에서는 중학교 2학년 학생에게 새로운 시청각 학습 자료를 제시하고 5회의 반복학습이 이루어지는 과정에서의 전두부($Fp_2,Fp_2$)의 뇌파를 측정하고 Fourier, Wavelet 변환을 하여 정량적으로 분석하였다. 주의 집중, 정서 등 인지와 관련지어 특정파의 조절 능력 및 파의 특성을 이용한 여러 연구들을 종합해보면, 기억력, 주의지속과 연관되어 알파파, 베타파와 세타파가 발생되는 것을 볼 수 있다. 이 중 알파파는 기존의 뇌 상태를 동기화시키고 주의나 기억의 과정에 영향을 미칠 수 있는 것으로 증명되었다. 본 논문에서는 신호 처리에 높은 효율을 보이는 Wavelet 변환을 이용하여, 학습이 됨에 따라 변화하는 EEG 신호 가운데 알파파의 패턴과 활성도를 분석하고자 한다.

• Journal of The Korea Institute of Healthcare Architecture
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• v.29 no.3
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• pp.29-36
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• 2023

Purpose: The purpose of this study is to find out whether the color change of the LED light source has a significant effect on the beta wave activity of EEG in the learning state. Methods: The subjects of the experiment were 20 male and female college students between the ages of 19 and 25 who routinely perform their studies. In the created learning environment, the EEG change according to the change in the lighting color was measured while solving the Mensa thinking ability problem while sitting on a desk with LED lights installed on the top and a chair with a footrest to stabilize the legs. The light source consisted of 3 ready-made colors and 6 newly created colors. A total of 9 color light stimuli were given for 2 minutes each, and the EEG change of the subject was observed. After the experiment, the correlation was analyzed based on the mid-beta wave data recorded on the QEEG according to the color change of light and the Mensa problem score. Results: It was found that the activation of mid-beta waves was stimulated in the temporal lobes (T5, T3, T6, T4) and occipital lobes (O1, O2) of all subjects who focused on solving Mensa thinking problems. As a result of comparing the top 20% and the bottom 20% of problem solving scores, the upper group had no effect of lighting, while the lower group showed increased beta wave activity in response to color light stimulation in the order of T4, T6, and T5. Implications: It was confirmed that the color of light that activates the middle beta wave varies greatly depending on the subject's attention and learning ability, and it is judged that the color of light including the green wavelength is helpful in activating the middle beta wave in the group with low learning ability.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.3
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• pp.372-377
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• 2006

In this paper, we propose sensibility recognition model that recognize user's sensibility using brain waves. Method to acquire quantitative data of brain waves including priority living body data or sensitivity data to recognize user's sensitivity need and pattern recognition techniques to examine closely present user's sensitivity state through next acquired brain waves becomes problem that is important. In this paper, we used pattern recognition techniques to use Multi Layer Perceptron (MLP) that is pattern recognition techniques that recognize user's sensibility state through brain waves. We measures several subject's emotion brain waves in specification space for an experiment of sensibility recognition model's which propose in this paper and we made a emotion DB by the meaning data that made of concentration or stability by the brain waves measured. The model recognizes new user's sensibility by the user's brain waves after study by sensibility recognition model which propose in this paper to emotion DB. Finally, we estimates the performance of sensibility recognition model which used brain waves as that measure the change of recognition rate by the number of subjects and a number of hidden nodes.

• Journal of The Korean Association For Science Education
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• v.21 no.3
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• pp.516-528
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• 2001

In the educational study, the measure of EEG(brain waves) can be useful method to study the functioning state of brain during learning behaviour. This study investigated the changes of neuronal response according to four times repetition of audio-visual learning. EEG data at the prefrontal$(Fp_{1},Fp_{2})$ were obtained from twenty subjects at the 8th grade, and analysed quantitatively using FFT(fast Fourier transform) program. The results were as follows: 1) In the first audio-visual learning, the activities of $\beta_{2}(20-30Hz)$ and $\beta_{1}(14-19Hz)$ waves increased highly, but the activities of $\theta(4-7Hz)$ and $\alpha$ (8-13Hz) waves decreased compared with the base lines. 2). According to the repetitive audio-visual learning, the activities of $\beta_{2}$ and $\beta_{1}$ waves decreased gradually after the 1st repetitive learning. And, the activity of $\beta_{2}$ wave had the higher change than that of $\beta_{1}$ wave. 3). The activity of $\alpha$ wave decreased smoothly according to the repetitive audio-visual learning, and the activity of $\theta$ wave decreased radically after twice repetitive learning. 4). $\beta$ and $\theta$ waves together showed high activities in the 2nd audio-visual learning(once repetition), and the learning achievement increased highly after the 2nd learning. 5). The right prefrontal$(Fp_{2})$ showed higher activation than the left$(Fp_{1})$ in the first audio-visual learning. However, there were not significant differences between the right and the left prefrontal EEG activities in the repetitive audio-visual learning. Based on these findings, we can conclude that the habituation of neuronal response shows up in the repetitive audio-visual learning and brain hemisphericity can be changed by learning experiences. In addition, it is suggested once repetition of audio-visual learning be effective on the improvement of the learning achievement and on the activation of the brain function.

• 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.