• Journal of Biomedical Engineering Research
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• v.9 no.1
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• pp.101-108
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• 1988

In this paper the power spectrum of background EEG is estimated by the LMS PHD based on least mean square. At the power spectrum estimatiom, the stocastic process of background EEG is assumed to consist of the nonharmonic sinusoid and the white noise. In the LMS PHD the model parameters are obtained by the least mean square at optimal order which is obtained from the fact that the eigenvalue's fluctuation of autocorrelation matrix of the normal back-ground EEG is smaller at some order than at other order when the power spectrum of background EEG is esitmated by PHD. The optimal order of this model is the 6-th order when the eigenvalue's fluctuation of autocorrelation matrix of background EEG is considered. The estimation results are with compared the results from the Maximum Entropy Spectral Estimation and Pisarenko Harmonic Decomposition. From the comparison results. The LMS PHD is possible to estimate the power spectrum of background EEG.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.2
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• pp.172-177
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• 2013

With Brain Computer Interface (BCI) system, a person with disabled limb could use this direct brain signal like electroencephalography (EEG) to control a device such as the artifact arm. The precise force control for the artifact arm is necessary for this artificial limb system. To understand the relationship between control EEG signal and the gripping force of hands, We proposed a study by measuring EEG changes of three grades (25%, 50%, 75%) of hand grip MVC (Maximal Voluntary Contract). The acquired EEG signal was filtered to obtain power of three wave bands (alpha, beta, gamma) by using fast fourier transformation (FFT) and computed power spectrum. Then the power spectrum of three bands (alpha, beta and gamma) of three classes (MVC 25%, 50%, 75%) was classified by using PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis). The result showed that the power spectrum of EEG is increased at MVC 75% more than MVC 25%, and the correct classification rate was 52.03% for left hand and 77.7% for right hand.

• Journal of the Ergonomics Society of Korea
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• v.29 no.5
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• pp.741-750
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• 2010

The objective of this paper is to study if the EEG spectral relative power would be a reasonable measure of expertise level. EEG electrodes were placed on the locations of Fp1, Fp2, F3, F4, T3, T4, O1, O2 while 5 subjects were playing 4 kinds of game on PC. EEG spectral relative power was significantly related with expertise level on the locations of Fp1, T3, T4, O1, O2. And the results showed that the $\theta$ and $\alpha$ activities were decreased, while $\beta$ and $\gamma$ activities were increased. The results indicated that the EEG spectral relative power would be applicable as a quantitative measure of expertise level.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.2
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• pp.251-256
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• 2008

Many researchers are studying about human Brain-Computer Interface(BCI) that it based on electroencephalogram(EEG) signals of multichannel. The researches of EEG signals are used for detection of a seizure or a epilepsy and as a lie detector. The researches about an interface between Brain and Computer have been studied robots control and game of using human brain as engineering recently. Especially, a field of brain studies used EEG signals is put emphasis on EEG artifacts elimination for correct signals. In this paper, we measure EEG signals as human emotions and divide it into five frequence parts. They are calculated related the percentage of selecting range to total range. the calculating values are compared standard values by Bayesian Network. lastly, we show the human face avatar as human Emotion.

• 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 Acupuncture
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• v.20 no.3
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• pp.9-27
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• 2003

Objectives : The aim of this study was to examine the effects of electro-acupuncture(EA) at the SP6(Sameumgyo)-GB39(Hyeonjong) on normal human EEG using power spectral analysis. Methods : EEG power spectrum exhibit site-specific and state-related differences in specific frequency bands. In this study, power spectrum was used as a measure of complexity(LAXTHA Co., KOREA). 30 channel EEG study was carried out in 20 subjects $(20\;males;\;age=21.4{\pm}0.5\;years)$. Results : In $\alpha$(alpha) band, the power values at F4 channels(p<0.05) during the SP6-GB39 acupoints treatment was significantly increased. But, the power values at Fz channel during the non-acupoint treatment was significantly decreased . In $\beta$(beta) band, the power values at Fz, FTC1, T3 channels(p<0.05) was significantly increased during the SP6-GB39 acupoints treatment. In $\delta$(delta) band, the power values at F4, C3, Cz, CP1, Pz channels(p<0.05) during SP6-GB39 acupoints treatment were significantly decreased.

• 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.

• Korean Journal of Acupuncture
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• v.21 no.1
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• pp.29-40
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• 2004

Objectives : The aim of this study was to examine the effects of electroacupuncture(EA) at the $H_7$ (Shinmun) on normal humans using power spectral analysis. Methods : EEG power spectrum exhibit site-specific and state-related differences in specific frequency bands. In this study, power spectrum was used as a measure of complexity. 32 channel EEG study was carried out in 9 subjects (9 males; age=25,8 years). Results : In alpha band, the power values at Fp2 channels(p<0.05) during the $H_7-acupoint$ treatment significantly were decreased. In beta band, the power values at Fp1, Fp2 channels(p<0.05) during the $H_7-acupoint$ treatment significantly were decreased. In delta and theta band, the power values at the $H_7-acupoint$ treatment significantly was increased than the before-acupuncture treatment. Conclusions : This results suggest that electroacupuncture at the $H_7$ is significantly in beta band of EEG.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.9
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• pp.853-859
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• 2013

The purpose of this study was to identify the correlation between electroencephalography (EEG) and strength, using grip strength. 64-channel EEG data were recorded from five healthy subjects in tasks requiring handgrip contractions of nine levels of MVC (Maximal Voluntary Contraction). We found the ERS (Event-Related Synchronization)/ERD (Event-Related Desynchronization) at the measured EEG data using STFT (Short-Time Furier Transform) and spectral power in the EEG of each frequency range displayed in the graph. In this paper, we identified that the stronger we contracted, the greater the spectral power was increased in the ${\beta}$, ${\gamma}$ wave.

• The Journal of Advanced Prosthodontics
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• v.8 no.3
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• pp.187-193
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• 2016

PURPOSE. This pilot study was to find the influence of complete denture on the brain activity and cognitive function of edentulous patients measured through Electroencephalogram (EEG) signals. MATERIALS AND METHODS. The study recruited 20 patients aged from 50 to 60 years requiring complete dentures with inclusion and exclusion criteria. The brain function and cognitive function were analyzed with a mental state questionnaire and a 15-minute analysis of power spectral density of EEG alpha waves. The analysis included edentulous phase and post denture insertion adaptive phase, each done before and after chewing. The results obtained were statistically evaluated. RESULTS. Power Spectral Density (PSD) values increased from edentulous phase to post denture insertion adaption phase. The data were grouped as edentulous phase before chewing (EEG p1-0.0064), edentulous phase after chewing (EEG p2-0.0073), post denture insertion adaptive phase before chewing (EEG p3-0.0077), and post denture insertion adaptive phase after chewing (EEG p4-0.0096). The acquired values were statistically analyzed using paired t-test, which showed statistically significant results (P<.05). CONCLUSION. This pilot study showed functional improvement in brain function of edentulous patients with complete dentures rehabilitation.