• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2000.11a
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• pp.38-43
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• 2000

In this present study, the correlation between subjective preference of essential odors and EEG response were investigated quantitatively. EEG signals were measured from 19 electrodes according to the International 10-20 system (Fpl, Fp2, F2/4, F7/8, Fz, C3/4, Cz, P3/4, Pz, T3/4, T5/6, Ol/2) from 8 healthy males subjects in four odor conditions. Four odor conditions (Rose oil bulgarian, Lemon oil misitano, Jasmin abs, Laverder iol france (KIMEX co. Ltd)) were applied for each subject in the experiment. Through the subjective evaluation, the most pleasant odor for each subject was determined. The power spectrum of ${\alpha}$/${\beta}$ of EEG signals from the most pleasant odor was compared with those from the control condition, which has no odor at all. It was observed that the power spectrum of ${\alpha}$/${\beta}$ of EEG from the most pleasant odor was increased significantly on F3, F3, F4,74 comparing to the control condition. This result indicates that the power spectrum of ai${\beta}$ could be a new index for measuring the levels of pleasantness of odors.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.24 no.65
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• pp.65-73
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• 2001

In this study, psychophysiological stress was quantitatively evaluated at various car interior noise levels by using Electroencephalogram(EEG). An experiment was performed to investigate the most comfortable range of noise level during simulated driving condition. Twelve healthy volunteers participated in the experiment. They were asked to operate the driving simulator while six levels of interior noise were given, such as 45dB(A), 50dB(A), 55dB(A), 60dB(A), 70dB(A), 80dB(A), and maximal subjective noise level. EEG signals were recorded for 60 seconds in each noise level. The power spectral analysis was performed to analyze EEG signal. At the same time, psychological stress was also measured subjectively by using a magnitude estimation method. The results showed that subjective stress and EEG spectrum indicated a statistically significant difference between noise levels. In particular, high level noise produced an increase in beta power at temporal(T3, T4) areas. It was also found that beta activity was highly correlated with subjective perception of discomfort, and subjects responded to car interior noise as arousing or negative stimuli. Moreover, beta power remained stable above 70dB(A), whereas subjective discomfort continued to increase even above 70dB(A) We concluded that brain waves could provide psychophysiological information of drivers emotional reaction to car interior noise. Thus, EEG parameters could be a new measure to determine optimal noise level in ergonomic workplace design after further verification in various experimental conditions.

• Journal of Biomedical Engineering Research
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• v.30 no.4
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• pp.327-332
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• 2009

In this paper, new index was developed to estimate the depth of anesthesia during general anesthesia using EEG. Analysis of the power spectral density(PSD) of EEG was used to develop new parameters because EEG signal tends to have slow wave during anesthesia. Classifier for index creator was developed by using SEF, BDR and BTR parameters, which are calculated by power spectral density. EEG data were obtained from 7 patients (ASA I, II) during general anesthesia with Sevoflurane. The anesthetic depth evaluation indexes ranged from 0 to 100. The average were $86.05{\pm}10.1$, $36.98{\pm}20.2$, $15.33{\pm}13.6$, $50.87{\pm}16.5$ and $87.72{\pm}11.7$ for the states of pre-operation, induction of anesthesia, operation, awaked and post-operation, respectively. The results show that while the depth of anesthesia was evaluated, more accurate information can be provided for anesthetician.

• Journal of Korean Elementary Science Education
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• v.25 no.2
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• pp.159-166
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• 2006

Changes in the brain activities following pre-service elementary teachers' learning hypothesis-generation were investigated using the analysis of EEG relative power and EEG coherence. In this study, the experimental group (n=16) were trained using learning methods for hypothesis-generation and the control group(n=16) were trained using learning methods for hypothesis-reception over the course of 8 weeks. EEG was measured before and following the learning process for both groups. Decreased theta ($4{\sim}7.9Hz$)/alpha 1 ($8{\sim}9.9Hz$) power and increased alpha 2 ($10{\sim}l2.9Hz$)/beta ($13{\sim}29.9Hz$)/gamma ($30{\sim}50Hz$) power were showed in the experimental group. Additionally, many changes in brian activities were observed for theta, beta and gamma coherence in the experimental group. In particular, fronto-parietal coherence increased in the experimental group. These differences in brain activities between the two groups suggest that the learning for subjects' hypothesis generation presumably leads to interesting changes in some types of brain activities in pre-service elementary teachers.

• Korean Journal of Acupuncture
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• v.28 no.3
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• pp.85-98
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• 2011

Objectives : The aim of this thesis is to examine the effect of acupuncture treatment at the GB37 on normal humans by using the power spectral analysis of the EEG. Methods : EEG (Electroencephalogram) power spectrum exhibits site-specific and state-related differences in specific frequency bands. In this thesis, the power spectrum was measured by the complexity. the 32 channels EEG study was carried out in the 13 subjects (12 males ; age=22.58 years old, 1 females ; 22 years old). Results : In the ${\alpha}$ (alpha) band, the power values at F7, F3, F4, F8, FTC2, C4, T4, CP1, CP2, TCP2, TT2, Pz, P4, Po1, Po2, O1, Oz, O2 channels (p<0.05) during the GB37-acupoint treatment were significantly changed. And in many channels were decreased. In the ${\beta}$ (beta) band, the power values at Cz, C4, T4, Tcp1, T6, Po1, O1, Oz, O2 channels (p<0.05) during the GB37-acupoint treatment were significantly changed. And in many channels were decreased. In the ${\delta}$(delta) band, the power values at Fp1, TT2 channels (p<0.05) during the GB37-acupoint treatment were significantly changed. And in many channels were decreased. In the $\theta$ (theta) band, the power values at Fp1, F8, FTC2, Pz channels (p<0.05) during the GB37-acupoint treatment were significantly changed. And in many channels were decreased. Conclusions : This results suggest that the acupuncture treatment at the GB37 significantly mostly change the power spectrum value on the alpha (18 channels), beta (9 channels) bands.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.2
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• pp.367-372
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• 2023

In this paper, we analyzed through the EEG signals how musical stimulus affects learning activities. Musical stimuli were divided into sedative and stimulative tendency music, preferred and non-preferred music, and the learning activity tasks were divided into mathematics tasks and memorization tasks. The signals measured in the EEG experiments were analyzed with the power spectrum of SMR waves known to be related to human concentration. Those spectra used for quantitative comparison in this paper. As a result the power of the EEG signals was observed to be greater than the case where music was given as a stimulus. Regardless of the type of task, the power of the EEG signals was observed to be greater in the case of sedative tendency than in the case of stimulative tendency, and the power of the EEG signals was observed to be greater in the case of favorite music than in the case of unfavorite music. From these results, it is estimated that if the musical stimulus exists, in the case of sedative tendency music, and in the case of favorite music, concentration can be increased than in the relative case.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.1
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• pp.12-18
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• 2013

Motor imagery classification in electroencephalography (EEG)-based brain-computer interface (BCI) systems is an important research area. To simplify the complexity of the classification, selected power bands and electrode channels have been widely used to extract and select features from raw EEG signals, but there is still a loss in classification accuracy in the state-of- the-art approaches. To solve this problem, we propose a discriminative feature extraction algorithm based on power bands with principle component analysis (PCA). First, the raw EEG signals from the motor cortex area were filtered using a bandpass filter with ${\mu}$ and ${\beta}$ bands. This research considered the power bands within a 0.4 second epoch to select the optimal feature space region. Next, the total feature dimensions were reduced by PCA and transformed into a final feature vector set. The selected features were classified by applying a support vector machine (SVM). The proposed method was compared with a state-of-art power band feature and shown to improve classification accuracy.

• Journal of the Korean Society of Safety
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• v.23 no.3
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• pp.65-70
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• 2008

Electroencephalogram(EEG) would be the most objective psychophysiological research technique on human errors though few research has been taken yet. This study aimed to get characteristics of human error while committing simple Odd-Ball tasks by utilizing the power spectrum technique of EEG data. Each experiment was composed of 3 tasks with different rules, and three young undergraduate students participated in this study as paid subjects. The result showed that subject and the interaction of subject and task factors were statistically significant on variation of power of $\alpha$ and $\beta$ bands which implied there would exist groups with homogeneity in their response. And though the variation of band powers due to task factors were not so great as to get statistical significance, it implied that the task requiring decoding process would be more strange to human beings than the task merely requiring psychological recall process.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1271-1275
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• 1987

With the stochastic process which consists of the harmonic sinusoid and the white nosie, the power spectrum of background EEG is estimated by the Pisarenko Harmonic Decomposition. The estimating results are examined and compared with the results from the maximum entropy spectral estimation, and the optimal order of this model can be determined from the eigen value's fluctuation of autocorrelation of background EEG. From the comparing results, this paper ensures that this method is possible to analyze the power spectrum of background EEG.

• Journal of Biomedical Engineering Research
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• v.8 no.1
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• pp.69-74
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• 1987

The power spectrum of background EEG is estimated by the Plsarenko Harmonic Decomposition with the stochastic process whlch consists of the nonhamonic sinus Bid and the white nosie. The estimation results are examined and compared with the results from the maximum entropy spectral extimation, and the optimal order of this from the maximum entropy spectral extimation, and the optimal order of this model can be determined from the eigen value's fluctuation of autocorrelation of background EEG. From the comparing results, this method is possible to estimate the power spectrum of background EEG.