• 한국의학물리학회:학술대회논문집
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• 한국의학물리학회 2002년도 Proceedings
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• pp.341-344
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• 2002

Neurodegenerative disorders, like Alzheimer's disease, are often accompanied by reduced brain perfusion (cerebral blood flow). Using the intrinsic magnetic properties of water, arterial spin labeling magnetic resonance imaging (ASLMRI) can map brain perfusion without injection of radioactive tracers or contrast agents. However, accuracy in measuring perfusion with ASL-MRI can be limited because of contributions to the signal from stationary spins and because of signal modulations due to transient magnetic field effects. The goal was to optimize ASL-MRI for perfusion measurements in the aging human brain, including brains with Alzheimer's disease. A new ASL-MRI sequence was designed and evaluated on phantom and humans. Image texture analysis was performed to test quantitatively improvements. Compared to other ASL-MRI methods, the newly designed sequence provided improved signal to noise ratio improved signal uniformity across slices, and thus, increased measurement reliability. This new ASL-MRI sequence should therefore provide improved measurements of regional changes of brain perfusion in normal aging and neurodegenerative disorders.

• 한국전자통신학회논문지
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• 제19권1호
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• pp.137-142
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• 2024

최근, 의료 데이터 표현 분야에서 딥러닝 방법들이 사실상의 표준으로 자리잡고 있다. 하지만, 딥러닝 기술은 내재적으로 많은 양의 학습 데이터를 필요로 하므로 대규모의 데이터를 확보하기 쉽지 않은 의료 분야에서는 직접적인 적용이 어려운 실정이다. 특히 뇌신호 모달리티의 경우, 변동성이 크기 때문에 여전히 데이터 부족 문제를 가진다. 이에, 최근 연구에서는 뇌신호의 시간-공간-주파수 특징을 적절하게 추출할 수 있는 딥 뉴럴 네트워크 구조를 설계하거나, 혹은 자가-지도 학습 방법을 도입하여 뇌신호의 신경생리학적 특징을 미리 학습하도록 한다. 본 논문에서는, 최근 각광받는 기술인 뇌-컴퓨터 인터페이스 및 피험자 상태 예측 등의 관점에서 소규모데이터를 다루기 위해 적용되는 방법론에 대한 분석 및 향후 기술 방향성을 제시한다. 먼저 현재 제안되고 있는 뇌신호 표현을 위한 딥 뉴럴 네트워크 구조에 대해 분석한다. 또한 뇌신호의 특성을 잘 학습하기 위한 자가-지도 학습 방법론을 분석한다. 끝으로, 딥러닝 기반 뇌신호 분석을 위한 중요 시사점 및 방향성에 관하여 논한다.

• 한국HCI학회:학술대회논문집
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• 한국HCI학회 2009년도 학술대회
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• pp.642-645
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• 2009

Brain Computer Interface (BCI) is a communication pathway between devices (computers) and human brain. It treats brain signals in real-time basis and discriminates some information of what human brain is doing. In this work, we develop a EEG BCI system using a feature extraction such as common spatial pattern (CSP) and a classifier using Fisher linear discriminant analysis (FLDA). Two-class EEG motor imagery movement datasets with both cued and uncued are tested to verify its feasibility.

• Journal of Electrical Engineering and Technology
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• 제9권3호
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• pp.1060-1071
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• 2014

Signals of the Electroencephalogram (EEG) can reflect the electrical background activity of the brain generated by the cerebral cortex nerve cells. This has been the mostly utilized signal, which helps in effective analysis of brain functions by supervised learning methods. In this paper, an approach for improving the accuracy of EEG signal classification is presented to detect epileptic seizures. Moreover, Independent Component Analysis (ICA) is incorporated as a preprocessing step and Short Time Fourier Transform (STFT) is used for denoising the signal adequately. Feature extraction of EEG signals is accomplished on the basis of three parameters namely, Standard Deviation, Correlation Dimension and Lyapunov Exponents. The Artificial Neural Network (ANN) is trained by incorporating Levenberg-Marquardt(LM) training algorithm into the backpropagation algorithm that results in high classification accuracy. Experimental results reveal that the methodology will improve the clinical service of the EEG recording and also provide better decision making in epileptic seizure detection than the existing techniques. The proposed EEG signal classification using feed forward Backpropagation Neural Network performs better than to the EEG signal classification using Adaptive Neuro Fuzzy Inference System (ANFIS) classifier in terms of accuracy, sensitivity, and specificity.

• 한국멀티미디어학회논문지
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• 제21권5호
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• pp.609-616
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• 2018

In this paper, we designed a multimodal bio-signal measurement system to observe changes in the brain nervous system and vascular system during sleep. Changes in the nervous system and the cerebral blood flow system in the brain during sleep induce a unique correlation between the changes in the nervous system and the blood flow system. Therefore, it is necessary to simultaneously observe changes in the brain nervous system and changes in the blood flow system to observe the sleep state. To measure the change of the nervous system, EEG, EOG and EMG signal used for the sleep stage analysis were designed. We designed a system for measuring cerebral blood flow changes using functional near-infrared spectroscopy. Among the various imaging methods to measure blood flow and metabolism, it is easy to measure simultaneously with EEG signal and it can be easily designed for miniaturization of equipment. The sleep stage was analyzed by the measured data, and the change of the cerebral blood flow was confirmed by the change of the sleep stage.

• 인지과학
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• 제21권2호
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• pp.309-338
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• 2010

본 논문에서는 사지가 마비되어 신체를 움직이지 못하지만 뇌의 기능은 살아있는 장애인들을 위하여, 생각만으로 외부의 장치를 제어할 수 있도록 하는 뇌-컴퓨터 인터페이스(BCI: Brain-Computer Interface) 기술을 연구하였다. 신경생리학 분야에서의 연구 결과에 의하면, 신체를 움직이는 상상을 할 경우, 뇌의 운동/감각 피질 영역에서는 $\beta$파(14-26 Hz)와 $\mu$파(8-12 Hz)가 억제/증가되는 ERD/ERS(Event-Related Desynchronization / Synchronization) 현상이 발생한다고 알려져 있다. 본 연구에서는 이를 기반으로 혀, 발, 왼손, 오른손의 동작 상상을 자극으로 이용하여 변화하는 뇌 신호 패턴을 실시간으로 분석하여 피험자의 생각을 읽을 수 있도록 하였으며, 상 하 좌 우의 네 방향으로 이동할 수 있도록 하는 마우스 제어 인터페이스를 구현하였다. 동작 상상 시 발생하는 뇌 신경 활동의 변화를 관측하기 위해서 뇌에 손상을 주지 않으면서도 높은 시간 해상도로 측정이 가능한 비침습적 뇌전도(EEG: ElectroEncephaloGraphy)를 이용하였다. 그러나 뇌전도 신호는 특성상 신호의 크기가 미약하고, 잡음의 영향을 많아 분석이 어렵다. 따라서 이를 극복하기 위해 통계적 방법을 기반으로 한 기계학습 기법인 CSP(Common Spatial Pattern)와 선형판별 분석(Linear Discriminant Analysis)을 이용하여 서로 다른 동작 상상에 의해 발생하는 뇌 신호들 간의 분산이 최대가 되도록 신호를 변환하여 인식 성능을 높일 수 있었다. 또한 분석된 뇌 신호의 시각화를 통해, 기존에 알려진 뇌의 해부학적, 신경생리학적 지식과 일치하는 ERD/ERS 현상이 발생하는 것을 확인할 수 있었다.

• 전기전자학회논문지
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• 제22권4호
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• pp.1195-1201
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• 2018

본 논문은 인간의 신체활동에 있어서 뇌의 신호가 연결된 근육으로 정보를 제공하고 받아오는 과정에서 유의미한 결과를 나타내는지에 관한 관계를 해석한다. 사람의 의식적 활동은 활동에 필요한 근육의 동작을 위하여 뇌로부터 생성된 전기신호의 전달에 의해 가능해 진다. 근육의 활성정보를 가지고 있는 근전도 신호는 근육활성화의 결과를 전기적인 신호로 출력하는데, 이 출력은 보통 근육의 수축과 이완에 따른 근육활성 정보를 출력한다. 본 연구에서는 이런 뇌전도와 근전도를 실시간으로 추출하여 데이터를 획득하고, 데이터 분석을 통해 눈으로 쉽게 확인하기 어려운 두 신호간의 관계를 분석하는데 목적이 있다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2009년도 정보 및 제어 심포지움 논문집
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• pp.145-147
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• 2009

This research measures EEG signals which are generating on head skin and extracts brain concentration level related with brain activity. We have developed concentration wireless transmission system by displaying this EEG signal on PDA mobile device. The front head was used for measuring EEG signal and INA128 with TL084 and analog elements was used for measuring EEG signal, amplifying and filtering the signal. Measured analog EEG signals changed into digital signals by using ADC of PIC24FJ192 with 10bit resolution and 500Ks/s sampling rate. So The changed digital signals have transmitted to the PDA by using bluetooth. LabView 8.5 was also used for FFT transformation, frequency and spectrum analysis of the transferred EEG signal. As a result, $\alpha$ wave, $\beta$ wave, $\theta$ wave and $\delta$ wave were classified. we extracted the concentration index by adapting concentration extraction algorithm. This concentration index was transferred into PDA by wireless module and displaying.

• 한국HCI학회:학술대회논문집
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• 한국HCI학회 2009년도 학술대회
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• pp.2083-2085
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• 2009

Brain Computer Interface (BCI) is a communication pathway between devices (computers) and human brain. It treats brain signals in real-time basis and discriminates some information of what human brain is doing. In this work, we develop a EEG BCI system using a feature extraction such as common spatial pattern (CSP) and a classifier using Fisher linear discriminant analysis (FLDA). Two-class EEG motor imagery movement datasets with both cued and uncued are tested to verify its feasibility.

• Journal of Korean Neurosurgical Society
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• 제58권2호
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• pp.131-136
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• 2015

Objective : To investigate the incidence of corpus callosum injury (CCI) in patients with mild traumatic brain injury (TBI) using brain MRI. We also performed a review of the clinical characteristics associated with this injury. Methods : A total of 356 patients in the study were diagnosed with TBI, with 94 patients classified as having mild TBI. We included patients with mild TBI for further evaluation if they had normal findings via brain computed tomography (CT) scans and also underwent brain MRI in the acute phase following trauma. As assessed by brain MRI, CCI was defined as a high-signal lesion in T2 sagittal images and a corresponding low-signal lesion as determined by axial gradient echo (GRE) imaging. Based on these criteria, we divided patients into two groups for further analysis : Group I (TBI patients with CCI) and Group II (TBI patients without CCI). Results : A total of 56 patients were enrolled in this study (including 16 patients in Group I and 40 patients in Group II). Analysis of clinical symptoms revealed a significant difference in headache severity between groups. Over 50% of patients in Group I experienced prolonged neurological symptoms including dizziness and gait disturbance and were more common in Group I than Group II (dizziness : 37 and 12% in Groups I and II, respectively; gait disturbance : 12 and 0% in Groups I and II, respectively). Conclusion : The incidence of CCI in patients with mild TBI was approximately 29%. We suggest that brain MRI is a useful method to reveal the cause of persistent symptoms and predict clinical prognosis.