• Journal of IKEEE
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• v.25 no.4
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• pp.679-688
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• 2021

Gait is an evaluation index used in various clinical area including brain nervous system diseases. Signal source localizing and time-frequency analysis are mainly used after extracting independent components for Electroencephalogram data as a method of measuring and analyzing brain activation related to gait. Existing treadmill-based walking EEG analysis performs signal preprocessing, independent component analysis(ICA), and source localizing by merging data after the multiple EEG measurements, and extracts representative component clusters through inter-subject clustering. In this study we propose an analysis method, without merging to single dataset, that performs signal preprocessing, ICA, and source localization on each measurements, and inter-subject clustering is conducted for ICs extracted from all subjects. The effect of data merging on the IC clustering and time-frequency analysis was investigated for the proposed method and two conventional methods. As a result, it was confirmed that a more subdivided gait-related brain signal component was derived from the proposed "non-merging" method (4 clusters) despite the small number of subjects, than conventional method (2 clusters).

• The Journal of the Korea Contents Association
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• v.14 no.11
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• pp.858-863
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• 2014

Through this study, we found out whether to measure objectively by using Transactional Analysis(TA) evaluating their own growth and benefit of health through the change of Ego-state, the personality structure and brain waves monitoring electric signals occurring in the brain of the human biological signals. According to the results of brain-wave test, M-type is brain-waves of healthy adults and a, ${\beta}$ were dominantly observed in the occipital while not sleeping. In particular, ${\beta}$ appears widely throughout the brain during nervous or concentrating metal activities and unlike N-type experimenters, ${\beta}$ was found to be dominant in M-type experimenters even in stable condition. N-type is brain waves of healthy adults and a, ${\beta}$ were dominantly observed in the occipital while not sleeping. In particular, unlike nervous or concentrating M-type, there was no noise such as tension and blink while resting. In addition, it turned out that subjects with high levels of A ego do not return quickly to the stable state and show a lot of Blinking and swallowing saliva, noise regardless of the pattern of Egogram. And brain waves of 11 people that the difference in data of OK-gram and Ego-state is the same in all items or less than -5 showed a low amplitude of $20{\mu}V$ in general. In conclusion, this study identified that the theory of personality pattern of Transactional Analysis Theory and brain-wave findings are consistent and also found out that brain waves are also associated according to each Ego-state of Egogram.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.2
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• pp.122-129
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• 2014

In this study, emotion-classification gathered from users was performed, classification-experiments were then conducted using SVM(Support Vector Machine) and K-means algorithm. Total 15 numbers of channels; CP6, Cz, FC2, T7. PO4, AF3, CP1, CP2, C3, F3, FC6, C4, Oz, T8 and F8 among 32 members of the channels measured were adapted in Brain signals which indicated obvious the classification of emotions in previous researches. To extract emotion, watching DVD and IAPS(International Affective Picture System) which is a way to stimulate with photos were applied and SAM(Self-Assessment Manikin) was used in emotion-classification to users' emotional conditions. The collected users' Brain-wave signals gathered had been pre-processing using FIR filter and artifacts(eye-blink) were then deleted by ICA(independence component Analysis) using. The data pre-processing were conveyed into frequency analysis for feature extraction through FFT. At last, the experiment was conducted suing classification algorithm; Although, K-means extracted 70% of results, SVM showed better accuracy which extracted 71.85% of results. Then, the results of previous researches adapted SVM were comparatively analyzed.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.6
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• pp.832-837
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• 2007

Many researchers are studying brain activity to using functional Magnetic Resonance Imaging (fMRI), Time Resolved Spectroscopy(TRS), Electroencephalography(EEG), and etc. They are used detection of seizures or epilepsy and deception detection in the main. In this paper, we focus on emotion recognition by recording brain waves. We specially use fMRI, TRS, and EEG for measuring brain activity Researchers are experimenting brain waves to get only a measuring apparatus or to use both fMRI and EEG. This paper is measured that we take images of fMRI and TRS about brain activity as human emotions and then we take data of EEG signals. Especially, we focus on EEG signals analysis. We analyze not only original features in brain waves but also transferred features to classify into five sections as frequency. And we eliminate low frequency from 0.2 to 4Hz for EEG artifacts elimination.

• Science of Emotion and Sensibility
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• v.8 no.4
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• pp.403-413
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• 2005

The purpose of the present study was to investigate the responses to positive/negative emotional stimuli for the different sensitivities of behavioral activation system (BAS) and behavioral inhibition system (BIS). We recorded If-channel EEG data for 8 BAS sensitive subjects an48 BIS sensitive subjects. EEGs were analyzed with LORETA (Low-resolution electromagnetic tomography) From scalp-recorded electrical potential distribution, LORETA computes the three-dimensional intracerebral distributions of current density for specified EEG frequency bands. hs results , significant differences between the BAS sensitive group ant the BIS sensitive group appeared LORETA alpha activities over the prefrontal lobe and the cingulate gyrus. Prefrontal regions and limbic system including cingulate gyrus are involved in emotional processing. Moreover, subjects with the high BAS sensitivity. responded more sensitively to the positive stimulation than subjects with the high BIS sensitivity. Therefore, our results suggest the possibility of correlation between BAS/BIS sensitivity and positive/negative emotional stimuli.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.7
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• pp.623-629
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• 2013

This paper presents the implementation result of the EEG(electroencephalogram) signal transmission protocol and its test platform. EEG measured by a dry-type electrode is directly converted into digital signal by ADC(analog-to-digital converter). Thereafter it is transferred DSP(digital signal processor) platform by $I^2C$(inter-integrated circuit) protocol. DSP conducts the pre-processing of EEG and extracts feature vectors of EEG. In this work, we implement the $I^2C$ protocol with 16 channels by using 10 or 12-bit ADC. In the implementation results, the overhead ratio for the 4 bytes data burst transmission measures 2.16 and the total data rates are 345.6 kbps and 414.72 kbps with 10-bit and 12-bit 1 ksps ADC, respectively. Therefore, in order to support a high speed mode of $I^2C$ for 400 kbps, it is required to use 16:1 and $(8:1){\times}2$ ratios for slave:master in 10-bit ADC and 12-bit ADC, respectively.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.6
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• pp.1333-1342
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• 2018

Until now, Electroencephalography(: EEG) has been the most important and convenient method for the diagnosis and treatment of epilepsy. However, it is difficult to identify the wave characteristics of an epileptic EEG signals because it is very weak, non-stationary and has strong background noise. In this paper, we analyse the effect of dimensionality reduction methods on Epileptic EEG feature selection and classification. Three dimensionality reduction methods: Pincipal Component Analysis(: PCA), Kernel Principal Component Analysis(: KPCA) and Linear Discriminant Analysis(: LDA） were investigated. The performance of each method was evaluated by using Support Vector Machine SVM, Logistic Regression(: LR), K-Nearestneighbor(: K-NN), Decision Tree(: DR) and Random Forest(: RF). From the experimental result, PCA recorded 75% of highest accuracy in SVM, LR and K-NN. KPCA recorded 85% of best performance in SVM and K-KNN while LDA achieved 100% accuracy in K-NN. Thus, LDA dimensionality reduction is found to provide the best classification result for epileptic EEG signal.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.12
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• pp.277-284
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• 2013

The analysis of phase synchronization characteristics from EEG signals is important for the understanding of information processing functionality in the brain network. In this paper, wavelet transformation(WT), Hilbert tansformation (HT), complex demodulation (CD) methods having time localization characteristics were applied to real evoked potential data and noise added simulation data with center frequencies corresponding to EEG bands for the estimation performance analysis of phase offset, phase changing point, and interband crosstalk. The WT is the best both in ${\delta}$, ${\theta}$, and ${\alpha}$ band signal decomposition, and in analyzing phase synchronization performance. The CD can be efficiently used in changing point detection under tolerant noise condition because of its abrupt performance degradation over noise endurance level. From experimental observations, the WT is the most suitable in phase synchronization application of EEG signal, and the CD can be affordable in restricted application such as changing point detection for higher bands than ${\delta}$. Particularly, WT and CD can be used to detect the changing instant of brain function by indirectly estimating the phase changing point.

• Sleep Medicine and Psychophysiology
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• v.25 no.2
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• pp.82-91
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• 2018

Objectives: Synchronous electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) has been used to explore sleep stage dependent functional brain networks. Despite a growing number of sleep studies using EEG-fMRI, few studies have conducted network analysis on whole night sleep due to difficulty in data acquisition, artifacts, and sleep management within the MRI scanner. Methods: In order to perform network analysis for whole night sleep, we proposed experimental procedures and data processing techniques for EEG-fMRI. We acquired 6-7 hours of EEG-fMRI data per participant and conducted signal processing to reduce artifacts in both EEG and fMRI. We then generated a functional brain atlas with 68 brain regions using independent component analysis of sleep fMRI data. Using this functional atlas, we constructed sleep level dependent functional brain networks. Results: When we evaluated functional connectivity distribution, sleep showed significantly reduced functional connectivity for the whole brain compared to that during wakefulness. REM sleep showed statistically different connectivity patterns compared to non-REM sleep in sleep-related subcortical brain circuits. Conclusion: This study suggests the feasibility of exploring functional brain networks using sleep EEG-fMRI for whole night sleep via appropriate experimental procedures and signal processing techniques for fMRI and EEG.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.5
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• pp.605-610
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• 2008

There are many methods for Human-Computer Interface. Recently, many researchers are studying about Brain-Signal this is because not only the disabled can use a computer by their thought without their limbs but also it is convenient to general people. But, studies about it are early stages. This paper proposes an EEG signals measurement and analysis methods for Brain-Computer Interface. Our purpose of this research is recognition of subject's intention when they imagine moving their arms. EEG signals are recorded during imaginary movement of subject's arms at electrode positions Fp1, Fp2, C3, C4. We made an analysis ERS(Event-Related Synchronization) and ERD(Event-Related Desynchronization) which are detected when people move their limbs in the ${\mu}$ waves and ${\beta}$ waves. Results of this research showed that ${\mu}$ waves are decreased and ${\beta}$ waves are increased at left brain during the imaginary movement of right hand. In contrast, ${\mu}$ waves are decreased and ${\beta}$ waves are increased at right brain during the imaginary movement of left hand.