• Journal of Korean Physical Therapy Science
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• v.15 no.3
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• pp.9-17
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• 2008

Background : The conventional electroencephalography(EEG) is commonly used as aid in the diagnosis of dementia. Recently developed quantitative electroencephalography(qEEG) provides data that are not achievable by conventional EEG. The aim of this study was to find out the usefulness of quantified-EEG in dementia. Method : Twenty elderly women(10 normal elderly, 10 demented elderly) were participated in this study. EEG power and coherence was computed over 21 channels; right and left frontal, central, parietal, temporal and occipital areas. Result : The activity of ${\alpha}$ wave was more higher than others significantly at frontal and parietal areas in normal elderly, but the activity of ${\theta}$ wave was higher in demented elderly. And the activity of ${\theta}$ wave in demented elderly women was more higher than normal elderly women significantly. Conclusion : In conclusion, we discovered that quantitative EEG was used to diagnose dementia.

• Korean Journal of Clinical Laboratory Science
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• v.51 no.4
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• pp.397-405
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• 2019

Quantitative electroencephalography (QEEG) has been widely used in research and clinical fields. QEEG has been widely used to objectively document cerebral changes for the purpose of identifying the electrophysiological biomarkers across various clinical symptoms and for the stimulation of specific cortical regions associated with cognitive function. In electroencephalography (EEG), the difference in quantitative and qualitative analyses is discriminated not by its measurement methods and relevant clinical or research environments, but by its analysis methods. When performing a qualitative analysis, it is possible for a medical technologist or experienced researchers to read the EEG waveforms to exclude artifacts. However, the quantitative analysis is still based on mathematical modeling, and all EEG data are included for the analysis, leading the results to be affected by unexpected artifacts. In the hospital setting, the case that the medical technologists in charge of the EEG test perform academic research has been little reported, compared to other clinical physiological measurement-based research. This is because there are few laboratories specialized in clinical physiological research. In this respect, this study is expected to be utilized as a basic reference material for medical technologists, students, and academic researchers, all of whom would like to conduct a quantitative analysis.

• 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.

• Annals of Clinical Neurophysiology
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• v.22 no.2
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• pp.82-91
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• 2020

Electroencephalography (EEG) produces time-series data of neural oscillations in the brain, and is one of the most commonly used methods for investigating both normal brain functions and brain disorders. Quantitative EEG analysis enables identification of frequencies and brain activity that are activated or impaired. With studies on the structural and functional networks of the brain, the concept of the brain as a complex network has been fundamental to understand normal brain functions and the pathophysiology of various neurological disorders. Functional connectivity is a measure of neural synchrony in the brain network that refers to the statistical interdependency between neural oscillations over time. In this review, we first discuss the basic methods of EEG analysis, including preprocessing, spectral analysis, and functional-connectivity and graph-theory measures. We then review previous EEG studies of brain network characterization in several neurological disorders, including epilepsy, Alzheimer's disease, dementia with Lewy bodies, and idiopathic rapid eye movement sleep behavior disorder. Identifying the EEG-based network characteristics might improve the understanding of disease processes and aid the development of novel therapeutic approaches for various neurological disorders.

• Korean Journal of Occupational Health Nursing
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• v.30 no.3
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• pp.101-109
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• 2021

Purpose: This study aimed to measure COVID-19 stress and the quantitative electroencephalography (QEEG) stress quotient and identify the coping styles of face-to-face service industry employees during the COVID-19 pandemic. Methods: This cross-sectional study administered structured questionnaires consisting of sections on general characteristics, COVID-19 stress, and coping style for stress to 21 face-to-face service industry employees between April 1 and April 18, 2021. The physical tension & stress quotient and psychological distraction & stress quotient were measured in the prefrontal lobe with QEEG. Results: Emotional easiness (r=.62, p=.002) and escape-avoidance (r=.55, p=.009) as a passive coping style were associated with COVID-19 stress, and seeking social support as an active coping style was associated with the left physical tension & stress quotient (r=.47, p=.031). Conclusion: These findings provide evidence regarding the objective status of the mental health of face-to-face service industry employees using both a self-reported scale and neuroscientific indicators, including brain quotients.

• Sleep Medicine and Psychophysiology
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• v.2 no.1
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• pp.31-43
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• 1995

Recently, the methods that measure and analyze brain electrical activity quantitatively have been available with the rapid development of computer technology. The quantitative electroencephalography(QEEG) is a method of computer-assisted analyzing brain electrical activity. The QEEG allows for a more sensitive, precise and reproducible examination of EEG data than that can be accomplished by conventional EEG. It is possible to compare various EEG parameters each other by using QEEG. Neurometrics, a kind of the quantitative EEG. is to compare EEG characteristics of the patient with normative data to determine in what way the patient's EEG deviates from normality and to discriminate among psychiatric disorders. Nowadays, QEEG is far superior to conventional EEG in its detection of abnormality and in its usefulness in psychiatric differential diagnosis. The abnormal findings of QEEG in various psychiatric disorders are also discussed.

• Proceedings of the Korean Society of Veterinary Clinics Conference
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• 2006.05a
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• pp.133-133
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• 2006

• Journal of the Architectural Institute of Korea Planning & Design
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• v.36 no.4
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• pp.41-49
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• 2020

The purpose of this study was to investigate the use of the Deep Neural Networks(DNN) model to classify user's emotions, in particular Electroencephalography(EEG) toward Virtual-Reality(VR) based 3D design alternatives. Four different types of VR Space were constructed to measure a user's emotion and EEG was measured for each stimulus. In addition to the quantitative evaluation based on EEG data, a questionnaire was conducted to qualitatively check whether there is a difference between VR stimuli. As a result, there is a significant difference between plan types according to the normalized ranking method. Therefore, the value of the subjective questionnaire was used as labeling data and collected EEG data was used for a feature value in the DNN model. Google TensorFlow was used to build and train the model. The accuracy of the developed model was 98.9%, which is higher than in previous studies. This indicates that there is a possibility of VR and Fast Fourier Transform(FFT) processing would affect the accuracy of the model, which means that it is possible to classify a user's emotions toward VR based 3D design alternatives by measuring the EEG with this model.

• 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.

• Psychiatry investigation
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• v.15 no.11
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• pp.1087-1093
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• 2018

Objective Posttraumatic stress disorder (PTSD) is distinct from anxiety disorders in its etiology and clinical symptomatology, and was reclassified into trauma- and stressor-related disorders in DSM-5. This study aimed to find neurophysiological correlates differentiating PTSD from anxiety disorders using resting-state quantitative electroencephalography (qEEG). Methods Thirty-six patients with either PTSD or acute stress disorder and 79 patients with anxiety disorder were included in the analysis. qEEG data of absolute and relative powers and patients' medication status on the day of qEEG examination were obtained. Electrodes were grouped into frontal, central, and posterior regions to analyze for regional differences. General linear models were utilized to test for group differences in absolute and relative powers while controlling for medications. Results PTSD patients differed from those with anxiety disorders in overall absolute powers [F(5,327)=2.601, p=0.025]. Specifically, overall absolute delta powers [F(1,331)=4.363, p=0.037], and overall relative gamma powers [F(1,331)=3.965, p=0.047] were increased in PTSD group compared to anxiety disorder group. Post hoc analysis regarding brain regions showed that the increase in absolute delta powers were localized to the posterior region [F(1,107)=4.001, p=0.048]. Additionally, frontal absolute gamma powers [F(1,107)=4.138, p=0.044] were increased in PTSD group compared to anxiety disorder group. Conclusion Our study suggests increased overall absolute delta powers and relative gamma powers as potential markers that could differentiate PTSD from anxiety disorders. Moreover, increased frontal absolute gamma and posterior delta powers might pose as novel markers of PTSD, which may reflect its distinct symptomatology.