• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1990년도 추계학술대회
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• pp.114-117
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• 1990

EEG signal is analyzed by two methods, analysis by visual inspection of EEG recording sheets and analysis by quantative method. Generally visual inspection method is used in the clinical field. But this method has its limitation because EEG signal is random signal. Therefore it is necessary to analyze EEG signals quantatively to obtain more precise and objective information of neural and brain. In this paper, power spectrum of EEG signal was estimated by AR(AutoRegressive) model in the frequency domain. This process is useful as a preprocessing stage for tomographic brain mapping (TBM) at each frequency, band. As a method for estimating power spectral density of EEG signals, periodogram method, autocorrelation method. covariance method, modified covariance method, and Burg method are tested in this paper.

• JSTS:Journal of Semiconductor Technology and Science
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• 제15권5호
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• pp.504-510
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• 2015

This paper presents a power and area efficient SAR ADC for multi-channel near threshold-voltage (NTV) applications such as neural recording systems. This work proposes a split dual-capacitive-array (S-DCA) structure with shifted input range for ultra low-switching energy and architecture of multi-channel single-ended SAR ADC which employs only one comparator. In addition, the proposed ADC has the same amount of equivalent capacitance at two comparator inputs, which minimizes the kickback noise. Compared with conventional SAR ADC, this work reduces the total capacitance and switching energy by 84.8% and 91.3%, respectively.

• The Korean Journal of Physiology and Pharmacology
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• 제24권4호
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• pp.349-362
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• 2020

Temperature affects the firing pattern and electrical activity of neurons in animals, eliciting diverse responses depending on neuronal cell type. However, the mechanisms underlying such diverse responses are not well understood. In the present study, we performed in vitro recording of abdominal ganglia cells of Aplysia juliana, and analyzed their burst firing patterns. We identified atypical bursting patterns dependent on temperature that were totally different from classical bursting patterns observed in R15 neurons of A. juliana. We classified these abnormal bursting patterns into type 1 and type 2; type 1 abnormal single bursts are composed of two kinds of spikes with a long interspike interval (ISI) followed by short ISI regular firing, while type 2 abnormal single bursts are composed of complex multiplets. To investigate the mechanism underlying the temperature dependence of abnormal bursting, we employed simulations using a modified Plant model and determined that the temperature dependence of type 2 abnormal bursting is related to temperature-dependent scaling factors and activation or inactivation of potassium or sodium channels.

• 대한의용생체공학회:의공학회지
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• 제22권2호
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• pp.145-150
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• 2001

대뇌 피질에 삽입하여 깊이에 따라 신경 신호를 기록하기 위한 탐침형 반도체 미세전극 어레이(depth-type silicon microelectrode array, 일명 SNU probe)를 제작하였다. 붕소를 확산시켜 생성된 고농도 p-type doping된 p＋ 영역을 습식식각 정지점으로 사용하는 기존의 방법과 달리 실리콘 웨이퍼의 앞면을 건식식각하여 원하는 탐침 두께만큼의 깊이로 트렌치(trench)를 형성한 후 뒷면을 습식식각하는 방법으로 탐침 형태의 미세 구조를 만들었다. 제작된 반도체 미세전극 어레이의 탐침 두께는 30 $\mu\textrm{m}$이며 실리콘 건식식각을 위한 마스크로 6 $\mu\textrm{m}$ 두께의 LTO(low temperature oxide)를 사용하였다. 탐침의 두께는 개발된 본 공정을 이용해서 5~90 $\mu\textrm{m}$ 범위까지 쉽게 조절할 수 있었다. 탐침의 두께를 보다 쉽게 조절할 수 있게 됨에 따라 여러 신경조직에 필요한 다양한 구조의 반도체 미세전극 어레이를 개발할 수 있게 되었다. 본 공정을 이용해서 개발된 4채널 SUN probe를 사용하여 흰쥐의 제1차 체감각 피질에서 4채널 신경 신호를 동시에 기록하였으며, 전기적 특성검사에서 기존의 탐침형 반도체 미세전극, 텅스텐 전극과 대등하거나 우수한 신호대 잡음비(signal to noise ratio, SNR)특성을 가짐을 확인하였다.

• 대한의용생체공학회:의공학회지
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• 제32권3호
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• pp.207-217
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• 2011

Electrode impedances are measured to quantitatively characterize the electrode-electrolyte or cell-electrode interfaces. In the case of high-density microelectrode arrays(MEAs) that have been developed for brainmachine interface applications, the characterization process becomes a repeating and time-consuming task; a system that can perform the measurement and analysis in an automated fashion with accuracy and speed is required. However, due to the large number of channels, parasitic capacitance and off-capacitance components of the switching system become the major factors that decreased the accuracy for the measurement of high impedance microelectrodes. Here we investigated the implementation of automatic impedance measurement system with analyzing the causes of possible measurement-related problems in multichannel switching configuration. Based on our multi-channel measurement circuit model, we suggest solutions to the problems and introduce a novel impedance measurement scheme using electro-mechanical relays. The implemented measurement system could measure |Z| < 700 $k{\Omega}$ of impedance in - 10% errors, which can be widely applicable to high density neural recording MEAs.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 추계학술대회 논문집 전기물성,응용부문
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• pp.257-259
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• 2002

In investigating the characteristics of a neural network, the use of planar microelectrode array shows several advantages over normal intracellular recording[1]. A transparent indium tin oxide(ITO) multi-electrode array(MEA) was fabricated and its top surface was insulated with photodefinable polyimide(HD-8001) except the exposed area for interfacing between the ITO electrodes and the neuronal cells. The exposed ITO electrodes were platinized in order to reduce the impedance between the electrodes and electrolyte. The one-minute platinization with $0.99nA/{\mu}m^2$ current density reduced the average impedance of the electrodes from $2.5M\Omega\;to\;90k\Omega$ at 1kHz in normal ringer solution. Cardiac cells were cultured on this MEA as a pilot study before neuron culture. The signals detected by the platinized electrodes had larger amplitudes and improved signal to noise ratio(SNR) compared to non-platinized electrodes. It is clear that microelectrodes need to have lower impedance to make reliable extracellular recordings, and thus platinization is essential part of MEA fabrication. Burst spike of cultured olfactory bulb was also detected with the MEA having platinized electrodes.

• 통합자연과학논문집
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• 제11권4호
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• pp.167-183
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• 2018

Electroencephalogram (EEG) recording provides a new way to support human-machine communication. It gives us an opportunity to analyze the neuro-dynamics of human cognition. Machine learning is a powerful for the EEG classification. In addition, machine learning can compensate for high variability of EEG when analyzing data in real time. However, the optimal EEG electrode location must be prioritized in order to extract the most relevant features from brain wave data. In this paper, we propose an intelligent system model for the extraction of EEG data by training the optimal electrode location of EEG in a specific problem. The proposed system is basically a fuzzy system and uses a neural network structurally. The fuzzy clustering method is used to determine the optimal number of fuzzy rules using the features extracted from the EEG data. The parameters and weight values found in the process of determining the number of rules determined here must be tuned for optimization in the learning process. Genetic algorithms are used to obtain optimized parameters. We present useful results by using optimal rule numbers and non - symmetric membership function using EEG data for four movements with the right arm through various experiments.

• Nuclear Engineering and Technology
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• 제53권4호
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• pp.1277-1283
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• 2021

It is important for operators of poly-pipelines in petroleum industry to continuously monitor characteristics of transferred fluid such as its type and amount. To achieve this aim, in this study a dual energy gamma attenuation technique in combination with artificial neural network (ANN) is proposed to simultaneously determine type and amount of four different petroleum by-products. The detection system is composed of a dual energy gamma source, including americium-241 and barium-133 radioisotopes, and one 2.54 cm × 2.54 cm sodium iodide detector for recording the transmitted photons. Two signals recorded in transmission detector, namely the counts under photo peak of Americium-241 with energy of 59.5 keV and the counts under photo peak of Barium-133 with energy of 356 keV, were applied to the ANN as the two inputs and volume percentages of petroleum by-products were assigned as the outputs.

• 동의생리병리학회지
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• 제33권6호
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• pp.356-362
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• 2019

Scutellaria baicalensis (SB) has widely used in the treatment for various brain diseases in the field of Oriental medicine. Biofermantation of SB can make major chemical constituents of SB to pass blood-brain barrier easily and to have more potent anti-oxidant ability. There is a little information about the contribution of fermented SB (FSB) to the formation or maintenance of the neural plasticity in the hippocampus. The purpose of this study was to evaluate effects of FSB extract on hydrogen peroxide (H₂O₂) - induced impairments of the induction and maintenance of long-term potentiation (LTP), an electrophysiological marker for the neural plasticity in the hippocampus. From hippocampal slices of rats, the field excitatory postsynaptic potentials (fEPSPs) were evoked by the electrical stimulation to the Schaffer collaterals - commissural fibers in the CA1 areas and LTP by theta-burst stimulation by using 64 - channels in vitro multi-extracellular recording system. In order to induce oxidative stress to hippocampal slices two different concentrations (200, 400 μM) of H₂O₂ were given to the perfused aCSF before and after the LTP induction, respectively. The ethanol extract of FBS with concentration of 25 ㎍/ml, 50 ㎍/ml was diluted in perfused aCSF that had 200 μM H₂O₂, respectively. Oxidative stress by the treatment of H₂O₂ resulted in decrease of the induction rate of LTP in the CA1 area with a dose - dependent manner. However, the ethanol extract of FSB prevented the reduction of the induction rate of LTP caused by H₂O₂ - induced oxidative stress with a dose - dependent manner. These results may support a potential application of FSB to ameliorate impairments of hippocampal dependent neural plasticity or memory caused by oxidative stress.

• 한국해양학회지:바다
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• 제26권4호
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• pp.307-326
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• 2021

상시 관측되는 조위관측소 해수위 자료는 결측값과 오측값을 포함하고 있으며, 그 중 오측 값은 이상값으로 분류되는 전처리 대상이다. 이러한 오측을 제거하기 위해 대표적으로 3𝜎 (three standard deviations) 규칙이 적용되어왔으나, 기상이변 등에 의한 극값이 존재하거나 3𝜎 범위 안에서도 오측이 존재하는 해수위 자료에는 그 적용이 어렵다. 본 연구에서 설계된 모델은 오측에 대한 사전 정보가 필요하지 않은 비주석 학습으로 구성되며, 재귀신경망과 앙상블 기법을 이용함으로써 실시간으로 수집되는 해수위 자료가 오측일 가능성을 발생한지 20분 이내로 제시한다. 검증이 완료된 모델은 평시 및 기상이변시의 정상값과 오측값을 잘 분리하며, 학습이 이뤄지지 않은 연도의 해수위 자료에서도 이상값 탐지가 가능함을 확인하였다. 본 연구의 관측 이상치 탐지 알고리즘은 조위관측소 해수위에 국한되지 않고 다양한 해양 및 대기자료의 이상치 탐지 인공신경망 모델에 확장 적용할 수 있다.