• Journal of Yeungnam Medical Science
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• v.41 no.4
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• pp.261-268
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• 2024

Owing to a lack of appropriate biomarkers for accurate diagnosis and treatment, psychiatric disorders cause significant distress and functional impairment, leading to social and economic losses. Biomarkers are essential for diagnosing, predicting, treating, and monitoring various diseases. However, their absence in psychiatry is linked to the complex structure of the brain and the lack of direct monitoring modalities. This review examines the potential of electroencephalography (EEG) as a neurophysiological tool for identifying psychiatric biomarkers. EEG noninvasively measures brain electrophysiological activity and is used to diagnose neurological disorders, such as depression, bipolar disorder (BD), and schizophrenia, and identify psychiatric biomarkers. Despite extensive research, EEG-based biomarkers have not been clinically utilized owing to measurement and analysis constraints. EEG studies have revealed spectral and complexity measures for depression, brainwave abnormalities in BD, and power spectral abnormalities in schizophrenia. However, no EEG-based biomarkers are currently used clinically for the treatment of psychiatric disorders. The advantages of EEG include real-time data acquisition, noninvasiveness, cost-effectiveness, and high temporal resolution. Challenges such as low spatial resolution, susceptibility to interference, and complexity of data interpretation limit its clinical application. Integrating EEG with other neuroimaging techniques, advanced signal processing, and standardized protocols is essential to overcome these limitations. Artificial intelligence may enhance EEG analysis and biomarker discovery, potentially transforming psychiatric care by providing early diagnosis, personalized treatment, and improved disease progression monitoring.

• Proceedings of the KOSOMBE Conference
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• v.1998 no.11
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• pp.251-252
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• 1998

Most of Electroencephalography(EEG) systems currently being used in hospitals don't support a standardized communication protocol for the exchange of orders, data and results. ASTM: E1467 protocol was proposed to expedite the EEG data exchange between different EEG systems and eventually between hospitals. In this paper, we present an Electroencephalography Information System using ASTM: E1467 protocol, with which patient registration, orders, interpretation, and review can be performed electronically. The system is designed using a component-based methodology. Most of the components are written in Visual C++ and Visual Basic. JAVA is also used to implement some components.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.7-10
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• 2007

많은 과학자들은 인간의 사고를 functional Magnetic Resonance Imaging (fMRI), Time Resolved Spectroscopy(TRS), Electroencephalography(EEG)등을 이용해서 두뇌 활동 영역을 연구하고 있다. 주로 의학 분야와 심리학의 영역에서 두뇌의 활동을 연구하여 간질이나 발작을 알아내고 거짓말 탐지 분야에서도 사용된다. 본 논문에서는 사람의 두뇌활동을 측정하여 인간의 감정을 인식하는 연구에 중점을 두었다. 특히 fMRI와 TRS 그리고 EEG를 이용해서 사람의 두뇌활동을 측정하는 연구를 하였다. 많은 연구자들이 한 가지 측정 장치만을 사용하여서 측정하거나 fMRI와 EEG를 동시에 측정하는 연구를 진행하고 있다. 현재에는 단순히 두뇌의 활동을 측정하거나 측정시 발생하는 잡음들을 제거하는 연구들에 중점을 두고 진행되고 있다. 본 연구에서는 fMRI와 TRS를 동시에 측정하여 얻은 두뇌 활동 데이터를 가지고 감정에 따른 활동영역의 EEG신호를 측정하였다. EEG 신호분석에 있어서 기존의 뇌파만을 가지고 특정을 찾아내는 것을 넘어서 각각의 채널에서 기록되는 뇌파의 파형을 주파수에 따라서 분류하고 정확한 측정을 위해 낮은 주파수를 제거하고 연구자가 필요한 부분의 뇌파를 분석하였다.

• 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.

• 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.

• Therapeutic Science for Rehabilitation
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• v.7 no.2
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• pp.9-16
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• 2018

Objective : The aim of this research was to provide EEG (electroencephalogram) basic data in clinical areas through identifying measurement tools, measurement methods, and evaluation and analysis method of the EEG which is a neurological change measurement of patients with brain injury. Methods : Previous studies were found in an electronic database (e.g., PubMed, Science Direct). The keyword search terms were 'Electroencephalography', 'stroke', 'intervention OR training'. Results : Utilitizing brain-computer interface, the EEG, which is a tool for measuring the effects of rehabilitation through changes of brain activation state. Also, it could identify functional brain reorganization mechanism. Whenever a research utilized the EEG, which is composed of various channels, different types of electrode, and varied electrode locations. Conclusions : Through this review, we found that Electroencephalography is possible to neurologically verify the effectiveness of intervention and formulate an intervention strategy for efficient occupational therapy.

• 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.

• Annals of Clinical Neurophysiology
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• v.19 no.2
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• pp.113-117
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• 2017

The performance of electroencephalogram (EEG) recordings is affected by electrode type, electronic parameters such as filtering, amplification, signal conversion, data storage; and environmental conditions. However, no single method has been identified for optimal EEG recording quality in all situations. Therefore, we aimed to provide general principles for EEG electrode selection as well as electronic noise reduction, and to present comprehensive information regarding the acquisition of satisfactory EEG signals. The standards provided in this document may be regarded as Korean guidelines for the clinical recording of EEG data. The equipment, types and nomenclature of electrodes, and the details for EEG recording are discussed.

• Proceedings of the KOSOMBE Conference
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• v.1995 no.11
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• pp.278-281
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• 1995

In this paper, we have developed EEG (electroencephalography) analyzer for monitoring the condition of brain in neurological surgery. This system is composed of EEG amplifier. personal-computer and BSP (Digital Signal Processor). By parallel processing of DSP, this system can analysis the power spectral density change of EEG in real-time and display the CSA(Compressed Spectral Array) and CDSA(Color Density Spectral array) of EEG. This system was tested by real EEG and showed the change of EEG.

• Journal of Korea Multimedia Society
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• v.18 no.3
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• pp.330-338
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• 2015

Gradually increasing the value of the technology, the techniques of the various security systems to protect the core technology have been developed. The proposed security scheme, which uses both a Password and the various devices, is always open by malicious user. In order to solve that problem, the biometric authentication systems are introduced but they have a problem which is the secondary damage to the user. So, the authentication methods using EEG(Electroencephalography) signals were developed. However, the size of EEG signals is big and it cause a lot of problems for the real-time authentication. And the encryption method is necessary. In this paper, we proposed an efficient real-time authentication system applied encryption scheme with junk data using chaos map on the EEG signals.