• Progress in Medical Physics
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• v.13 no.3
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• pp.156-162
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• 2002

In this study, we attempted in preparing high precision EEG measuring equipment. To measure EEG in high efficiency, pre-amplifier should get high performance common mode rejection ratio. Also, separation amplifier is essential to eliminate common line noise. So, our study were pointed at elevating the efficiency of eliminating noise, user safety and low noise characteristics. Prepared high precision pre-amplifier for EEG was A/D converted to automatically classify $\alpha$ wave, $\beta$ wave and $\theta$ wave. And converted data were Fast Fourier Transformed with real time DSP (Digital Signal Processing). Clinical demonstrations were carried out with healthy students, aged between 20 to 26 who has no histories of illness. To recognize the efficiency of the EEG, prepared EEG were used with MS equipment in low stimulated state and high stimulated state. Then, we studied at the effect of sensitivity on brain wave. From this study, it is known that our EEG equipment is efficient in sensitivity evaluation and suitable stimulations for each psychological state are required.

• Korean Journal of Oriental Medicine
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• v.16 no.1
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• pp.157-171
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• 2010

This study aimed to investigate the effect of four common types of Qigong position (standing, sitting, supine, and horse-riding position) on the autonomic nervous system. Thirty healthy subjects participated in this study once a week for four weeks. Electroencephalogram (EEG) was measured three times (before, during, and after the position) while the subject maintained one of four positions for ten minutes. There were significant changes in HRV components compared with EEG power spectra in the standing position. Especially, the ratio of low-to-high frequency (LF/HF) which represents a state of balance of autonomic nervous system was increased. In the sitting position, $\beta$ wave which reflects a state of alert consciousness was increased and both the sympathetic and parasympathetic nerves were activated. On the other hand, in the spine position, $\theta$ wave which signifies a state of relaxation was increased and heart rate (HR) was decreased. Activation of sympathetic and parasympathetic nerves was also observed in this position. Significant increases of indices related to awakening and concentration were observed accompanied by increase of HR and a sympathetic nerve was activated in the riding-horse position. In the present study, it was shown that each Qigong position caused various and significant changes in autonomic nervous system. It would be expected that these results can be applied in the choice of appropriate Qigong position according to objective of Qigong therapy although it is remained to further evaluate the effects of long-term maintenance of Qigong positions and repeated Qigong training.

• Journal of Internet Computing and Services
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• v.20 no.5
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• pp.27-36
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• 2019

In this study, experiments on the improvement of the emotion classification, analysis and accuracy of EEG data were proceeded, which applied DEAP (a Database for Emotion Analysis using Physiological signals) dataset. In the experiment, total 32 of EEG channel data measured from 32 of subjects were applied. In pre-processing step, 256Hz sampling tasks of the EEG data were conducted, each wave range of the frequency (Hz); Theta, Slow-alpha, Alpha, Beta and Gamma were then extracted by using Finite Impulse Response Filter. After the extracted data were classified through Time-frequency transform, the data were purified through Independent Component Analysis to delete artifacts. The purified data were converted into CSV file format in order to conduct experiments of Machine learning algorithm and Arousal-Valence plane was used in the criteria of the emotion classification. The emotions were categorized into three-sections; 'Positive', 'Negative' and 'Neutral' meaning the tranquil (neutral) emotional condition. Data of 'Neutral' condition were classified by using Cz(Central zero) channel configured as Reference channel. To enhance the accuracy ratio, the experiment was performed by applying the attributes selected by ASC(Attribute Selected Classifier). In "Arousal" sector, the accuracy of this study's experiments was higher at "32.48%" than Koelstra's results. And the result of ASC showed higher accuracy at "8.13%" compare to the Liu's results in "Valence". In the experiment of Random Forest Classifier adapting ASC to improve accuracy, the higher accuracy rate at "2.68%" was confirmed than Total mean as the criterion compare to the existing researches.