• The Magazine of the IEIE
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• v.42 no.2
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• pp.45-56
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• 2015

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.1656-1658
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• 2012

이 논문에서는 스마트 폰에서 오디오 신호의 스펙트럼을 가시광선 스펙트럼으로 매핑하는 방법을 제안한다. 음악이 연주되고 있을때 그 음악과 어울리는 색을 비추면 뇌가 느끼는 감성은 더욱 증폭될 수 있다. 본 논문에서 제안하는 기술은 스마트폰을 통해 색을 플레이 되고 있는 음악과 매칭이다. 오디오의 신호가 가지고 있는 주파수와 파형을 색과 밝기로 매핑하여 음악에 맞춰 색을 전환 할 수 있다.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.39 no.2
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• pp.169-176
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• 2015

The mechanical properties of living tissues have been major subjects of interest in biomechanics. In particular, the characteristics of very soft materials such as the brain have not been fully understood because experiments are often severely limited by ethical guidelines. There are increasing demands for studies on remote medical operations using robots. We conducted compression tests on brain-like specimens made of gelatin to find substitutes with the mechanical properties of brain tissues. Using a finite element analysis, we compared our experimental data with existing data on the brain in order to establish material models for brain tissues. We found that our substitute models for brain tissues effectively simulated their mechanical behaviors.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.11
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• pp.195-205
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• 2013

The analysis of electroencephalographic signal associated with the attention is essential for the understanding of human cognition. In this paper, the characteristic differences between the attention and inattention status in the brain were inspected by nonlinear analysis. The recurrence quantification analysis was applied to the relatively small number of samples of evoked potential having time varying characteristics, where the recurrence plot (RP), the color recurrence plot (CRP), and mean and time-sequential trend parameters were extracted. The dimension and the time delay in phase transformation can be determined by the paired set of extracted parameters. It is observed from RP, CRP, and parameters that the brain dynamics in attention is more complex than that in the inattention, as well as the synchronized brain response is stable in the mean sense but locally time varying. It is feasible that the non-linear analysis method can be useful for the analysis of complex brain dynamics associated during visual attentional task.

• Smart Media Journal
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• v.13 no.4
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• pp.86-93
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• 2024

The development of functional magnetic resonance imaging (fMRI) has significantly contributed to mapping brain functions and understanding brain networks during rest. This paper proposes a CNN-LSTM-based classification model to classify the progression stages of Alzheimer's disease. Firstly, four preprocessing steps are performed to remove noise from the fMRI data before feature extraction. Secondly, the U-Net architecture is utilized to extract spatial features once preprocessing is completed. Thirdly, the extracted spatial features undergo LSTM processing to extract temporal features, ultimately leading to classification. Experiments were conducted by adjusting the temporal dimension of the data. Using 5-fold cross-validation, an average accuracy of 96.4% was achieved, indicating that the proposed method has high potential for identifying the progression of Alzheimer's disease by analyzing fMRI data.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.351-353
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• 2021

자폐 스펙트럼 장애는 이질적인 신경 발달 장애로, 뇌기능영상에 기반한 기능적 연결성 행렬을 이용해 연구가 활발하게 진행된다. 기능적 연결성 행렬을 분석하기 위해 주성분 분석방법을 이용하며, 이를 통해 뇌의 기능적 경향성 패턴을 확인할 수 있다. 이 때, 서로 다른 연결성 성분 비율과 주성분 벡터를 이용해서 다양한 기능적 경향성 패턴을 얻을 수 있다. 패턴에 따른 랜덤 포레스트 분류 모델의 성능이 달라지는데 이를 비교해본 결과, 상위 50%의 성분을 이용하여 만든 기능적 경향성 패턴 1 이 데이터의 설명 비율도 높고, 우수한 분류 성능을 보이는 것을 확인했다.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.7
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• pp.110-120
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• 2016

A power-efficient neuromodulator is needed for implantable systems. In spite of their stimulation signal's simplicity of wave shape and waiting time of MCU(micro controller unit) much longer than execution time, there is no consideration for low-power design. In this paper, we propose a novel of low-power algorithm based on the characteristics of stimulation signals. Then, we designed and implement a neuromodulation software that we call NMS(neuro modulation simulation). In order to implement low-power algorithm, first, we analyze running time of every function in existing NMS. Then, we calculate execution time and waiting time for these functions. Subsequently, we estimate the transition time between active mode (AM) and low-power mode (LPM). By using these results, we redesign the architecture of NMS in the proposed low-power algorithm: a stimulation signal divided into a number of segments by using characteristics of the signal from which AM or LPM segments are defined for determining the MCU power reduces to turn off or not. Our experimental results indicate that NMS with low-power algorithm reducing current consumption of MCU by 76.31 percent compared to NMS without low-power algorithm.

• Journal of Life Science
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• v.33 no.6
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• pp.455-462
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• 2023

During pregnancy, maternal immune activation (MIA) from infection increases the risk of neurodevelopmental diseases, including schizophrenia and autism spectrum disorders. MIA induced by polyinosinic-polycytidylic acid (poly (I:C)) and lipopolysaccharide (LPS) in animal experiments has led to offspring with abnormal behaviors and brain development. In addition, it has recently been reported that microglia, which reside in the brain and function as immune cells, play an important role in behavioral abnormalities and brain development in MIA-induced offspring. However, the underlying mechanism remains unclear. In this study, we investigated whether microglia-specific inhibition of GPR56, a member of the G protein-coupled receptor (GPCR) family, causes behavioral abnormalities in brain development. First, MIA induction did not affect the microglia population, but when examining the expression of microglial GRP56 in MIA-induced fetuses, GPR56 expression was inhibited between embryonic days 14.5 (E14.5) and E18.5 regardless of sex. Furthermore, microglial GPR56-suppressed mice showed abnormal behaviors in the MIA-induced offspring, including sociability deficits, repetitive behavioral patterns, and increased anxiety levels. Although abnormal cortical development such as that in the MIA-induced offspring were not observed in the microglial GPR56-suppressed mice, their brain activity was observed through c-fos staining. These results suggest that microglia-specific GPR56 deficiency may cause abnormal behaviors and could be used as a biomarker for the diagnosis and/or as a therapeutic target of behavioral deficits in MIA offspring.

• Journal of the Korean Society of Radiology
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• v.84 no.3
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• pp.638-652
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• 2023

Purpose To investigate the MRI markers for the prediction of amyloid β (Aβ)-positivity in mild cognitive impairment (MCI) and Alzheimer's disease (AD), and to evaluate the differences in MRI markers between Aβ-positive (Aβ [+]) and -negative groups using the machine learning (ML) method. Materials and Methods This study included 139 patients with MCI and AD who underwent amyloid PET-CT and brain MRI. Patients were divided into Aβ (+) (n = 84) and Aβ-negative (n = 55) groups. Visual analysis was performed with the Fazekas scale of white matter hyperintensity (WMH) and cerebral microbleeds (CMB) scores. The WMH volume and regional brain volume were quantitatively measured. The multivariable logistic regression and ML using support vector machine, and logistic regression were used to identify the best MRI predictors of Aβ-positivity. Results The Fazekas scale of WMH (p = 0.02) and CMB scores (p = 0.04) were higher in Aβ (+). The volumes of hippocampus, entorhinal cortex, and precuneus were smaller in Aβ (+) (p < 0.05). The third ventricle volume was larger in Aβ (+) (p = 0.002). The logistic regression of ML showed a good accuracy (81.1%) with mini-mental state examination (MMSE) and regional brain volumes. Conclusion The application of ML using the MMSE, third ventricle, and hippocampal volume is helpful in predicting Aβ-positivity with a good accuracy.

• 전자공학회논문지 IE
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• v.43 no.4
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• pp.21-27
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• 2006

This paper is for the segmentation and volume calculation of the white matter and gray matter from brain MRI. We segment white matter, gray matter and CSF from the Brain image in the normal and abnormal person, and calculate the volume of segmented tissue. In this paper, we present a new method of extracting white matter, gray matter and CSF and calculation its volume from MR images for brain. And we have developed the determining method of threshold that can extract white matter and gray matter from MR image for brain through the analysis of gray values represented by ratio of each component. We proposed the calculation method of volume for white matter and gray matter by using number of extracted pixels in each slice. This algorithm input CSF/Head volume ratio and age of patient and calculates discriminant value through discriminant expression, classifies normal and abnormal using calculated discriminant value. As a result, we could blow that white matter and gray matter volume decrease and CSF volume increase as we grow gold.