• Journal of KIISE
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• v.41 no.12
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• pp.1050-1057
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• 2014

Electroencephalogram (EEG)-based brain-computer interfaces (BCI) can be used for a number of purposes in a variety of industries, such as to replace body parts like hands and feet or to improve user convenience. In this paper, we propose a method to decompose and extract motor imagery EEG signal using Empirical Mode Decomposition (EMD) and Fast Fourier Transforms (FFT). The EEG signal classification consists of the following three steps. First, during signal decomposition, the EMD is used to generate Intrinsic Mode Functions (IMFs) from the EEG signal. Then during feature extraction, the power spectral density (PSD) is used to identify the frequency band of the IMFs generated. The FFT is used to extract the features for motor imagery from an IMF that includes mu rhythm. Finally, during classification, the Support Vector Machine (SVM) is used to classify the features of the motor imagery EEG signal. 10-fold cross-validation was then used to estimate the generalization capability of the given classifier., and the results show that the proposed method has an accuracy of 84.50% which is higher than that of other methods.

• Journal of KIISE
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• v.44 no.6
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• pp.587-594
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• 2017

Recently, motor imagery electroencephalogram(EEG) based Brain-Computer Interface(BCI) systems have received a significant amount of attention in various fields, including medicine and engineering. The Common Spatial Pattern(CSP) algorithm is the most commonly-used method to extract the features from motor imagery EEG. However, the CSP algorithm has limited applicability in Small-Sample Setting(SSS) situations because these situations rely on a covariance matrix. In addition, large differences in performance depend on the frequency bands that are being used. To address these problems, 4-40Hz band EEG signals are divided using nine filter-banks and Regularized CSP(R-CSP) is applied to individual frequency bands. Then, the Mutual Information-Based Individual Feature(MIBIF) algorithm is applied to the features of R-CSP for selecting discriminative features. Thereafter, selected features are used as inputs of the classifier Least Square Support Vector Machine(LS-SVM). The proposed method yielded a classification accuracy of 87.5%, 100%, 63.78%, 82.14%, and 86.11% in five subjects("aa", "al", "av", "aw", and "ay", respectively) for BCI competition III dataset IVa by using 18 channels in the vicinity of the motor area of the cerebral cortex. The proposed method improved the mean classification accuracy by 16.21%, 10.77% and 3.32% compared to the CSP, R-CSP and FBCSP, respectively The proposed method shows a particularly excellent performance in the SSS situation.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.46 no.3
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• pp.265-272
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• 2020

The body odor as well as the skin changes has become an important issue. Humans leave respective specific odors dependent on sex and age; The odor has been known to affect social relationship of human. In this study, we wanted to confirm the possibility as parameters to evaluate body odor into existing odor evaluation methods by experts. The 15 subjects, aged from 50 to 61 years wore cotton t-shirts for 72 hours and collected body odor. The cotton t-shirts containing body odor were used for sensory evaluation and EEG measurement by the odor experts. In order to evaluate body odors of each subject, an odor sensory evaluation and electroencephalogram (EEG) were conducted by odor exports and the correlation in between two assessments was analyzed. Pearson correlation analysis shows negative correlation of the sensory evaluation versus 'Excitement' in EEG parameter (r = - 0.649, p = 0.009) and positive correlation of the sensory evaluation versus 'Stress' in EEG parameter (r = 0.704, p = 0.003). In conclusion, it is considered that the evaluation of body odor through EEG measurement can be used as a method to complement the odor evaluation by experts.

• Journal of Oriental Neuropsychiatry
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• v.21 no.3
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• pp.45-64
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• 2010

Objectives : The purpose of this study was to examine the effects of auricular acupuncture at the Shinmun on patients with dementia by using power spectrum analysis. Electroencephalogram(EEG) power spectrum show site-specific and state-related differences in various frequency bands. Methods : Study was carried out in 38 subjects.(31 patients with dementia and 7 normal people, 27 females and 11 males ; mean age=75.9 years old). To know the effect of auricular acupuncture at the Shinmun in patients with dementia, we measured electroencephalogram(EEG) of normal people and patients with dementia about 'before', 'after' and 'after 15 minutes' giving auricular acupuncture at the Shinmun, and compared the brain wave($\alpha$, $\beta$, $\delta$, $\theta$) of EEG 30 channels. Results : In the case of patients with dementia, in the power values of $\alpha$(alpha) band at F4, FP2, FCZ, F8, FC4 channels (p<0.05) during the Shinmun-auricular acupuncture treatment increased significantly. In $\beta$(beta) band, the power values at T8 channel(p<0.05) during the Shinmun-auricular acupuncture treatment increased significantly. In $\delta$(delta) band, the power values at F7, FP1, FZ, FP2, FCZ, C4, FC4, CP4, T8, P7, P3, O1, OZ, O2, P4, P8 channels(p<0.05) during the Shinmun-auricular acupuncture treatment decreased significantly. In $\Theta$(theta) band, the power values at O1, OZ, O2channel(p<0.05) during the Shinmun-auricular acupuncture treatment decreased significantly. Conclusions : Through these results, we concluded that auricular acupuncture at the Shinmun on patients with dementia could have effect the functional mechanism of the cerebral cortex.

• Physical Therapy Rehabilitation Science
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• v.1 no.1
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• pp.13-16
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• 2012

Objective: Electroencephalogram (EGG)-feedback is a training procedure aimed at altering brain activity, and is used as a treatment for disorders like attention. The purpose of this study was to determine the effects of external focus of attention by EGG-feedback on balance in young adults. Design: Cross-sectional study. Methods: Subject were students in Sahmyook University. Fifty young adults in their twenties and thirties. Subjects were performed both with and without external focus of attention by EEG-feedback on the posture of standing and tandem standing. Participants were educated effort to maintain static posture when they were under internal focus of attention. Good Balance System was used for measurement of postural consistency upon the following force platforms. Results: Body sway decreased significantly both normal standing and tandem standing with external focus of attention by EEG-feedback (p<0.05). Conclusions: The results demonstrate that the benefits of an external attentional focus are generalizable to young adults. The external focus of attention outperformed the internal focus of attention on the postural balance (p<0.05). It is showed that external focus of attention significant effects on balance by revoked automatic postural control of movement. Furthermore balance might be improved by training with an external focus. Further study is required to develop for training as a method of preventing fall in elderly peoples.

• Journal of Internet Computing and Services
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• v.10 no.5
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• pp.127-133
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• 2009

This paper presents an approach to classify normal and epilepsy from electroencephalogram(EEG) using a neural network with weighted fuzzy membership functions(NEWFM). To extract input features used in NEWFM, wavelet transform is used in the first step. In the second step, the frequency distribution of signal and the amount of changes in frequency distribution are used for extracting twenty-four numbers of input features from coefficients and approximations produced by wavelet transform in the previous step. NEWFM classifies normal and epilepsy using twenty four numbers of input features, and then the accuracy rate is 98%.

• KIEAE Journal
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• v.11 no.3
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• pp.49-56
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• 2011

The effect of soundscape has been analyzed by questionnaire results. Nowadays, EEG is used to identify the human reponses due to exterior stimuli such as soundscape sound sources. So, it is very meaningful to know the EEG response to soundscape sound sources. In the present study, the sound sources of soundscape were heard to subjects in order to find out the relationship between questionnaire results and electroencephalogram results through lab test. And stimulated part of brain for evaluating words were sought in this experiment too. The results of the study are as follows : the sound source of bird+music causes more ${\alpha}$-wave rise than other sound sources and the ${\alpha}$-wave stimulated region of brain is occipitallobe. In case of ${\beta}$-wave, the left part of brain is excited. ${\delta}$-wave is on frontallobe and ${\Theta}$-wave is on right part of brain. The evaluating words for soundscape can be categorized into four groups. These results could be used for basic materials of soundscape effects analysis.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.503-508
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• 1992

A new method for making automatic electroencephalogram(EEG) report based on the automatic quantitative interpretation of awake EEG was developed. We first analysed a. relationship between EEG reports and quantitative EEG interpretation done by a qualified electroencephalographer(EEGer) for 22 subjects. Based on the analysed relationship and usual process of report making by the EEGer, we defined all terminology necessary for EEG report and established rules for EEG report making. By the combined use of the proposed EEG report making and the method for automatic quantitative EEG interpretation presented at '90 KACC, we were able to make the automatic EEG reports which were equivalent to the EEG reports written by the EEGer. As all the procedures were programmed in a personal computer equipped with an AD (analogue-to-digital) converter, the automatic EEG reports were obtained in almost real time in usual actual EEG recording situation with only a few seconds time lag for the analysis in the computer. The proposed report making method and the quantitative EEG interpretation method will be effectively applicable to the clinical use as an assistant tool for physicians.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.11a
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• pp.41-47
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• 2006

In this study, the influences of environmentally friendly visual and auditory information on traffic noise perception were surveyed by the using electroencephalogram Green rural region image and CBD image in urban city were used as visual informations. And traffic noise, signal and environmental music were used to detect the impact on electroencephalogram variance. It was revealed that green rural region image caused a-wave ratio increase about 10% and environmental music increased $\alpha$-wave ratio approximately $40{\sim}50%$. The results of this study improved that environmentally friendly visual and auditory information had an effect on decreasing traffic noise loudness to some extents.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.17 no.2 s.119
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• pp.160-167
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• 2007

In this study, the influences of environmentally friendly visual and auditory information on traffic noise perception were surveyed by the using electroencephalogram. Green rural region image and CBD (central business district) image in urban city were used as visual informations. And traffic noise, signal and environmental music were used to detect the impact on electroencephalogram variance. It was revealed that green rural region image caused ${\alpha}-wave$ ratio increase about 10% and environmental music increased ${\alpha}-wave$ ratio approximately $40{\sim}50%$. The results of this study improved that environmentally friendly visual and auditory information had an effect on decreasing traffic noise loudness to some extents.