• Journal of Biomedical Engineering Research
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• v.27 no.2
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• pp.64-72
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• 2006

The present study introduces a new cortical patch-based source model for EEG/MEG cortical source imaging to consider anatomical constraints more precisely. Conventional source models for EEG/MEG cortical source imaging have used coarse cortical surface mesh or sampled small number of vertices from fine surface mesh, and thus they failed to utilize full anatomical information which nowadays we can get with sub-millimeter modeling accuracy. Conventional ones placed a single dipolar source on each cortical patch and estimated its intensity by means of various inverse algorithms; whereas the suggested cortical patch-based model integrates whole cortical area to construct lead field matrix and estimates current density that is assumed to be constant in each cortical patch. We applied the proposed and conventional models to realistic EEG data and compared the results quantitatively. The quantitative comparisons showed that the proposed model can provide more precise spatial descriptions of neuronal source distribution.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.34 no.12
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• pp.85-94
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• 2018

The purpose of this paper was to propose a model that recognizes potential users' emotional response toward design by classifying Electroencephalography(EEG). Studies in neuroscience and psychology have made an effort to recognize subjects' emotional response by analyzing EEG data. And this approach has been adopted in design since it is critical to monitor users' subjective response in the preface of design. Moreover, the building design process cannot be reversed after construction, recognizing clients' affection toward design alternatives plays important role. An experiment was conducted to record subjects' EEG data while they view their most/least liked images of small-house designs selected by them among the eight given images. After the recording, a subjective questionnaire, PANAS, was distributed to the subjects in order to describe their own affection score in quantitative way. Google TensorFlow was used to build and train the model. Dataset for model training and testing consist of feature columns for recorded EEG data and labels for the questionnaire results. After training and testing, the measured accuracy of the model was 0.975 which was higher than the other machine learning based classification methods. The proposed model may suggest one quantitative way of evaluating design alternatives. In addition, this method may support designer while designing the facilities for people like disabled or children who are not able to express their own feelings toward alternatives.

• Journal of IKEEE
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• v.12 no.4
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• pp.204-210
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• 2008

Generally, among the EEG signal, alpha wave is said to be strongly appeared in stable state and beta wave in active state. And in oriental medicine, it is said that relative long and regular respiration shows stable state rather than short and irregular respiration. In this paper, we tried to find out relation between respiration and EEG in stable state using quantitative parameters such as stable state ratio and equivalent ratio of respiration which was defined to indicate the degree of stable state quantitatively. And we verified our proposal by the real experiment for 20 persons.

• Journal of the Institute of Convergence Signal Processing
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• v.5 no.4
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• pp.263-270
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• 2004

Recently, wavelet transform have been applied to various kinds of problems in many fields. In this paper, we propose method of Daubechies wavelet to detect several kinds of important characteristic waves in tasks EEG that are needed to diagnose EEG. We show that our system could be attained higher performance in detecting characteristic waves than the other methods. In this system, the architecture of the neural network is a three layered feed-forward networks with one hidden layer which implements the error back propagation teaming algorithm. Applying the algorithms to 4 subjects show 92% classification rates. The proposed system shows a little more accurate diagnosis for task EEG by Wavelet and neural network. From the simulation results by the implemented system, we demonstrated this research can be reduce doctor's labors and quantitative diagnosis of task EEG.

• 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 %.

• Journal of Veterinary Clinics
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• v.23 no.1
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• pp.31-35
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• 2006

This study was performed to evaluate sedation with quantitative electroencephalography (EEG) analysis in dogs. EEG is used to evaluate objectively the effects of CNS acting with brain and behavioral changes. Especially, spectral edge frequency 95 (SEF 95) parameter is an effective method to determine the sedative status. The SEF 95 is the frequency below 95% of the total power. Twelve healthy intact male Miniature Schnauzer dogs, which did not show any neurological abnormalities and disease, were used for the study. EEG electrodes were inserted in subcutaneous tissue over the calvaria without entering adjacent muscles. The EEG data were acquired and analyzed by EEG raw wave and spectral edge frequency 95 analysis. After the administration of sedatives, the SEF 95 values were shown the significant changes compared with the normal state In all groups (p<0.05). It is suggested that SEF 95 analysis is useful method for assessing the state of sedation in dogs.

• Korean Journal of Veterinary Research
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• v.45 no.1
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• pp.131-137
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• 2005

To assess anesthetic depth using quantitative electroencephalography (q-EEG), we recorded processed EEG (raw EEG) till 100 minutes in beagle dogs anesthetized for 60 minutes with tiletamine/zolazepam (n=5, TZ group), xylazine/ketamine (n=5, XK group) and propofol (n=5, PI group) by intermittent bolus injection. Raw EEG was converted into 95% spectral edge frequency (SEF) and median frequency (MF) through fast fourier transformation (FFT) method. 95% SEF value of TZ group was significantly higher (p<0.05) than the XK group from 10 minutes to 100 minutes. 95% SEF value of PI group was significantly higher (p<0.05) than the XK group from 10 minutes to 40 minutes, and significantly low (p<0.05) than XK group at 90 and 100 minutes. MF was significantly higher (p<0.05) in TZ group from 60 minutes to 100 minutes. Based on these results, using dissociative agent with ${\alpha}_2$-adrenergic agent is more potent in CNS depressed than using dissociative agent alone, and low doses of propofol has a disinhibitory effect on CNS.

• Journal of Biomedical Engineering Research
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• v.15 no.1
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• pp.105-110
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• 1994

In this paper, we present a method to evaluate source locations and distributed region which is specified brain activity, as indicated by locations and strengths of intracranial sources, using potential gradients of interpolation polynomials and topographic mapping of the EEG records. This method can analyze the variance of source temporally or spatially and leads to enable a quantitative evaluation of potential gradients drawing methods which is now being used in the clinic. In the result, we obtained the overall potentials distribution on the entire scalp and the information of potential source locations from the EEG records of a patient which was known to epilepsy.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.4
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• pp.1038-1047
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• 1996

The effect of electromagnetic wave on the biobody is to make a possibility not increasing temperature but also inducing physiological and psychological damage which is head or backbone. Generally a vital function of hean can be estimated by the measureing of Electro Encephalogram(EEG), and achieved a response of a find stimulus which can not be defected a subjective sympton and out of reaction. In this paper, the head can be modeled according to its medium and dervated the equation of SAR distritution, and the head of rabbit is approximately modeled three layers practically and the result of each SAR distribution is illustrated. Measured EEG from the nervous system of rabbit on a plane wave irradiated an be used quantitative analysis for the electrophysiogical effect of the biobody.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.967-971
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• 1989

A new system for automatic scoring of 'organization' of the EEG dominant rhythm was constructed and applied to 18 normal subjects and 15 patients. Organization parameters which best represented the 'organization' as judged by 5 neurologists' visual inspection were calculated and the automatic organization scoring was obtained by a linear regression of those organization parameters. Furthermore, values of the regression coefficients were used to study the characteristics of EEG interpretation by each neurologist, and this scoring technique can also be applied to the training of EEG interpretation.