• PNF and Movement
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• v.12 no.2
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• pp.97-106
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• 2014

Purpose: The purpose of this study was to investigate changes in EEG through attention. Concentration training and performing tasks are important factors in the improvement of motor learning ability. Methods: In the experiment, 22 healthy people were divided into two groups: the trail making test (TMT) group and the computerized neurocognitive function test (CNT) group. A one-way Neuro Harmony M test to see whether there was a significant difference among the groups. Results: The TMT group showed a significant increase in ${\alpha}$ wave, ${\alpha}$ wave sequence, and ${\beta}$ wave sequence; however, there were no significant differences in SMR wave, SMR wave sequence, and ${\beta}$ wave. The CNT group showed increases in ${\alpha}$ wave, ${\alpha}$ wave sequence, SMR wave, SMR wave sequence, and ${\beta}$ wave sequence; however, there was no significant difference in ${\beta}$ wave. In EEGs before and after two performance tasks were changed, there were significant differences in ${\beta}$ wave, SMR wave, SMR wave sequence; however, there were no significant differences in ${\alpha}$ wave sequence, ${\beta}$ wave, and ${\beta}$ wave sequence. Conclusion: Attention training and concentration training offer feedback and repetition for constant stimulus and response. Moreover, attention training and concentration training can contribute to new studies and motivation by developing fast sensory and motor skills through acceptable visual and auditory stimulation.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.3
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• pp.178-184
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• 2007

The EEG signal in general can be categorized as the Alpha wave, the Beta wave, the Theta wave, and the Delta wave. The alpha wave, showed in stable state, is the dominant wave for a human EEG and the beta wave displays the excited state. The subject of this paper was to recognize the stable state of EEG quantitatively using wavelet transform and power spectrum analysis. We decomposed EEG signal into the alpha wave and the beta wave in the process of wavelet transform, and calculated each power spectrum of EEG signal, using Fast Fourier Transform. And then we calculated the stable state quantitatively by stable state ratio, defined as the power spectrum of the alpha wave over that of the beta wave. The study showed that it took more than 10 minutes to reach the stable state from the normal activity in 69 % of the subjects, 5 -10 minutes in 9%, and less than 5 minutes in 16 %.

• Korean Journal of Mathematics
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• v.16 no.1
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• pp.41-49
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• 2008

We show the existence of a negative solution for the system of the following nonlinear wave equations with critical growth, under Dirichlet boundary condition and periodic condition $$u_{tt}-u_{xx}=au+b{\upsilon}+\frac{2{\alpha}}{{\alpha}+{\beta}}u_+^{\alpha-1}{\upsilon}_+^{\beta}+s{\phi}_{00}+f,\\{\upsilon}_{tt}-{\upsilon}_{xx}=cu+d{\upsilon}+\frac{2{\alpha}}{{\alpha}+{\beta}}u_+^{\alpha}{\upsilon}_+^{{\beta}-1}+t{\phi}_{00}+g,$$ where ${\alpha},{\beta}>1$ are real constants, $u_+={\max}\{u,0\},\;s,\;t{\in}R,\;{\phi}_{00}$ is the eigenfunction corresponding to the positive eigenvalue ${\lambda}_{00}$ of the wave operator and f, g are ${\pi}$-periodic, even in x and t and bounded functions.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1809-1810
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• 2007

본 논문에서는 뇌파인식을 위한 입력패턴을 추출하고 패턴 인식을 위한 뇌파 학습 알고리즘을 설계하였다. 입력패턴의 구성은 일반적인 상황에서 인식률을 더욱 높이기 위하여 기존의 Alpha-wave, Beta-wave, Theta-wave, Delta-wave등의 비율을 비교하는 방식에서 Delta-wave와 Theta-wave의 합, Alpha-wave, Delta-wave와 Theta-wave의 합에 Alpha-wave로 나눈 값, Beta-wave의 4가지 입력패턴으로 구성하였다. 그리고 신경망의 한 종류인 역전파 알고리즘을 이용하여 동일 조건이나 비슷한 조건에서의 수면과 비수면의 구분이 아닌 각기 다른 조건 상태에서의 수면과 비수면에 대한 패턴분류를 시뮬레이션 하였고 일반적인 조건에서도 감성 상태를 분류 할 수 있음을 보였다.

• The Research Journal of the Costume Culture
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• v.12 no.5
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• pp.702-706
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• 2004

Sensorial tests were executed to find the sensibility and texture of the fabrics. The physiological responses employed in this study was electroencephalogram(EEG). The purpose of this study is to find out how the sample groups responded to the texture of the woven silks and the woven ramie. The sample groups are of 10 males and females, age of 25. EEG was recorded a fast and slow alpha wave according to the texture of the textiles. The sample fabrics are of woven silk and woven ramie. The results obtained as be lows. When the sample groups touched the woven silk, they responded and showed more slow alpha wave than the woven ramie. The slow alpha wave raised when the sample groups felt comfort and relax. The fast alpha wave were more in the woven ramie, it raised when the people felt the tension and the anxiety. There was no significant difference between the male and the female. Woven silk has the soft and smoothness it causes comfort. The sensation of tactile was recorded through the EEG.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.6
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• pp.1800-1805
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• 2008

Shiro-abhyanga is one of treatments of Ayurveda, which is the ancient system of health care and medicine in India. In this essay, I examined the effect of Shiro-abhyanga for relaxing stress of women in twentieth by the means of the brain wave test. The brain wave test showed that Shiro-abhyanga reduced Delta and Theta waves of the left brain but increased $\alpha$, SMR, Low-$\beta$ waves of the right brain. Delta and Theta waves are usually found in sleeping time. Their high measurement in awakening instructs the stress and depressing situations of objects. However the treatment of Shiro-abhyanga made the decrease of Delta and Theta waves and the increase of Alpha wave in working time. Alpha wave appears while objects are comfortable and peaceful from the relaxation of body and mind. Therefore the growth of Alpha wave says that the treatment of Shiro-abhyanga has an effect on mitigation of stress.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.879-880
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• 2006

The subject of this paper is to recognize the stable state of EEG using wavelet transform and power spectrum analysis. An alpha wave, showed in stable state, is dominant wave for a human EEG and a beta wave displayed excited state. We decomposed EEG signal into an alpha wave and a beta wave in the process of wavelet transform. And we calculated each power spectrum of EEG signal, an alpha wave and a beta wave using Fast Fourier Transform. We recognized the stable state by making a comparison between power spectrum ratios respectively.

• Journal of the Korean Society of Physical Medicine
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• v.4 no.3
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• pp.201-207
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• 2009

Purpose：The purpose of this study was to investigate the effect of $\alpha$-wave music and art appreciation on hand function in stroke with hemiplegia. Methods：A total of 32 stroke with hemiplegia participated in this study experimental group(16 subjects) received $\alpha$-wave music and art appreciation with general neurologic therapy. Control group(16 subjects) received general neurologic therapy. All subjects were assessed for hand function(manual dexterity, power grip, pinch grip, two point discrimination(parm, finger), tactile sense(parm, finger) using a purdue pegboard, dynamometer, pinch gauge, two-point anethesiometer and semmes-weinstein monofilament wire. The data were analyzed using paired and independent t-test. Results：The results were as follows : 1. In the experimental group, manual dexterity were significantly increased between pre and post intervention(p<.05). 2. In the experimental group, tactile sesne in finger were sifnificantly increased between pre and post intervention(p<.05). Conclusion：The results of this study shows that $\alpha$-wave music and art appreciation affect the hand function of hemiplegic side with regard to manual dexterity and tactile sense.

• The Journal of Korean Physical Therapy
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• v.20 no.1
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• pp.75-79
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• 2008

Purpose: The purpose of this study was to investigate the effect of $\alpha$-wave music and art appreciation on hand function. Methods: A total of 22 university students participated in this study (10 males and 12 females). Twelve subjects received $\alpha$-wave music and art appreciation. The other subjects received neither. All subjects were assessed for hand function (manual dexterity, power grip, pinch, lateral pinch, tactile sense) using a Purdue pegboard, dynamometer, pinch gauge, and Semmes-Weinstein monofilament wire. The data were analyzed using paired and independent t-tests. Results: The results were as follows: 1. In the experimental group, manual dexterity and tactile sense were significantly increased between pre- and post-intervention (p<0.05). Within the control group, manual dexterity and power grip were significantly increased between pre- and post-test (p<0.05). 2. With regard to dexterity and tactile sense, the experimental group experienced a significant post-intervention increase compared to the control group (p<0.05). There was no significant difference in power grip, pinch, or lateral pinch changes between the two groups (p>0.05). Conclusion: The results of this study show that $\alpha$-wave music and art appreciation affect hand function with regard to manual dexterity and tactile sense.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.467-479
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• 2022

Working memory (WM), which plays a vital role in daily activities, is a memory system that temporarily stores and processes information when people are engaged in complex cognitive activities. The influence of music on WM has been widely studied. In this work, we conducted a series of n-back memory experiments with different task difficulties and multiple trials on 14 subjects under the condition of no music and Alpha wave leading music. The analysis of behavioral data show that the change of music condition has significant effect on the accuracy and time of memory reaction (p<0.01), both of which are improved after the stimulation of Alpha wave music. Behavioral results also suggest that short-term training has no significant impact on working memory. In the further analysis of electrophysiology (EEG) data recorded in the experiment, auto-regressive (AR) model is employed to extract features, after which an average classification accuracy of 82.9% is achieved with support vector machine (SVM) classifier in distinguishing between before and after WM enhancement. The above findings indicate that Alpha wave leading music can improve WM, and the combination of AR model and SVM classifier is effective in detecting the brain activity changes resulting from music stimulation.