• 대한수의학회지
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• 제46권1호
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• pp.47-55
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• 2006

To evaluate method for monitoring anesthetic depth with quantitative electroencephalography (q-EEG), we recorded processed EEG (raw EEG) and pain score till 100 minutes in beagle dogs anesthetized for 60 minutes with propofol (n = 5, PRO group), isoflurane (n = 5, ISO group) and propofol-ketaminefentanyl (n = 5, PFK group). Raw EEG was converted into 95% spectral edge frequency (SEF) by fast Fourier transformation (FFT) method. We investigated anesthetic depth by comparing relationship (Pearson's correlation) between q-EEG (95% SEF) and pain score. Pearson's correlation coefficients are +0.2372 (p = 0.0494, PRO group), +0.79506 (p < 0.001, ISO group) and +0.49903 (p = 0.0039, PFK group).

• Journal of Information Processing Systems
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• 제16권5호
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• pp.1034-1047
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• 2020

The personal authentication technique is an essential tool in this complex and modern digital information society. Traditionally, the most general mechanism of personal authentication was using alphanumeric passwords. However, passwords that are hard to guess or to break, are often hard to remember. There are demands for a technology capable of replacing the text-based password system. Graphical passwords can be an alternative, but it is vulnerable to shoulder-surfing attacks. This paper looks through a number of recently developed graphical password systems and introduces a personal authentication system using a machine learning technique with electroencephalography (EEG) signals as a new type of personal authentication system which is easier for a person to use and more difficult for others to steal than other preexisting authentication systems.

• 한국HCI학회:학술대회논문집
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• 한국HCI학회 2009년도 학술대회
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• pp.330-334
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• 2009

본 연구는 뇌의 편측 영역 에서의 EEG(Electroencephalography) coherence 로 손동작 의도를 예측하고자 하는 연구이다. 손 동작 예측을 위한 실험에 신체에 이상이 없는 6 명의 피실험자가 참여 하였다. 실험은 데이터 트레이닝 6 분과 동작 의도 판단 6 분으로 진행되었으며 무작위 순서로 손 동작을 지시한 후 편측적 영역 5 개 지점의 EEG 와 동작 시점을 알기 위한 오른손 EMG(Electromyography)를 측정하였다. 측정된 EEG 데이터를 분석하기 위해 주파수 별 Alpha 와 Beta 를 분류하였고 EMG 신호를 기준으로 동작과 휴식으로 분류된 Alpha 와 Beta 데이터를 5 개의 측정 영역별 Coherence 분석을 하였다. 그 결과 동작과 휴식을 구분할 수 있는 통계적으로 유효한 EEG Coherence 영역을 통하여 동작 판단을 할 수 있음을 확인하였다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 하계종합학술대회 논문집(5)
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• pp.133-136
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• 2002

EEG(Electroencephalography) is generated by electrical activity between neurons in cortical. Waveform of EEG is changed according to body and mental states. Therefore EEG is used to diagnosis of encephalophyma and epilepsy, etc. Also EEG is used to HCI(Human-Computer Interface). This paper describes estimation of orientation and location of dipole sources. The forward model is three-layer spherical head model and current dipole model. Using analytical solution, EEG is generated. Using MNLS(Minimum-Norm Least-Square) method, orientation and location of dipole moment is estimated.

• 한국멀티미디어학회논문지
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• 제24권10호
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• pp.1336-1345
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• 2021

Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common neurological disorders in children. The diagnosis of ADHD in children is based on the interviews and observation reports of parents or teachers who have stayed with them. Since this approach cannot avoid long observation time and the bias of observers, another approach based on Electroencephalography(EEG) is emerging. The goal of this study is to develop an assistive tool for diagnosing ADHD by EEG classification. This study explores the frequency bands of EEG and extracts the implied features in them by using the proposed CNN. The CNN architecture has three Convolution-MaxPooling blocks and two fully connected layers. As a result of the experiment, the 30-60 Hz gamma band showed dominant characteristics in identifying EEG, and when other frequency bands were added to the gamma band, the EEG classification performance was improved. They also show that the proposed CNN is effective in detecting ADHD in children.

• 대한의용생체공학회:의공학회지
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• 제39권1호
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• pp.55-61
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• 2018

The purpose of this study was to investigate the effect of a pillow simulated with cranial sacral therapy (CST) on sleep induction using electroencephalography (EEG). This study included 12 voluntary participants divided into experimental group (CST group) and control group (Non-CST group) to observe EEG changes. The position of the electrode for EEG measurement consists of 8 channels electrodes (Fp1, Fp2, F3, F4, T3, T4, P3 and P4). In this study, we measured the fall asleep time, change of brain activity and sleep wave ratio using EEG wave (${\delta}$, ${\theta}$, ${\alpha}$, ${\beta}$ and ${\gamma}$). As a result, the mean fall asleep time of the experimental group was shorter than that of the control group significantly (p < 0.001). Also in comparison with the control group, both the delta (d) and theta (q) wave corresponding to the slow waves showed a larger increase and the alpha (a) wave showed a larger decrease significantly. The slow waves of experimental group showed a higher rate of significant increase than the control group (p < 0.001). Therefore this study showed that pillow based on CST had an effective in improving sleep induction and quality.

• 한국임상수의학회지
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• 제18권3호
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• pp.226-231
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• 2001

We investigated the effects of interactions of medetomidine and atipamezole on electroencephalography (EEG) in seven dogs. The dogs were sedated with medetomidine at dose of 30$\mu\textrm{g}$/kg, IM. Atipamezole was injected 15 min later at dose of 30$\mu\textrm{g}$/kg, IV. Recording electrode was positioned at Cz, which was applied to International 10-20 system. Heart rates, arterial blood pressures and behavioral changes were also measured. EEG was recorded in 6 stages(S1: before medetomidine injection, S2: prior to head-down movement after medetomidine injection, S3: 5 minutes after medetomidine injection, S4: 10 minutes after medetomidine injection, S5: 15 minutes after medetomidine injection, S6: prior to head-up movement after atipamezole injection), and heart rates and arterial pressures were recorded at S1, S5 and S6. All results were compared with those of control(S1). After medetomidine injection, the power spectra of EEG were gradually decreased and those of the frequency over 13 Hz were significantly decreased(p<0.05), which were still in the significantly decreased state after atipamezole injection. In the band powers (Band1; 1-2.5 Hz, Band2; 2.5-4.5 Hz, Band3; 4-8Hz, Band4; 8-13 Hz, Band5; 13-20 Hz, Band6; 20-30 Hz, Band7; 30-50 Hz, Band8; 1-50 Hz), band 1, 2, 3, 4, 8 were not significantly changed in any stages. Band 5, 6, 7 were significantly decreased in S 3, 4, 5, 6. That is, medetomidine affects the frequency band over 13 Hz on EEG, and atipamezole does not restored the decreased band powers until dogs showed head-up movement.

• 대한건축학회논문집:계획계
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• 제34권12호
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• pp.85-94
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• 2018

The purpose of this paper was to propose a model that recognizes potential users' emotional response toward design by classifying Electroencephalography(EEG). Studies in neuroscience and psychology have made an effort to recognize subjects' emotional response by analyzing EEG data. And this approach has been adopted in design since it is critical to monitor users' subjective response in the preface of design. Moreover, the building design process cannot be reversed after construction, recognizing clients' affection toward design alternatives plays important role. An experiment was conducted to record subjects' EEG data while they view their most/least liked images of small-house designs selected by them among the eight given images. After the recording, a subjective questionnaire, PANAS, was distributed to the subjects in order to describe their own affection score in quantitative way. Google TensorFlow was used to build and train the model. Dataset for model training and testing consist of feature columns for recorded EEG data and labels for the questionnaire results. After training and testing, the measured accuracy of the model was 0.975 which was higher than the other machine learning based classification methods. The proposed model may suggest one quantitative way of evaluating design alternatives. In addition, this method may support designer while designing the facilities for people like disabled or children who are not able to express their own feelings toward alternatives.

• 한국조경학회:학술대회논문집
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• 한국조경학회 2021년도 춘계학술대회 논문집
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• pp.71-72
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• 2021

• Journal of Multimedia Information System
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• 제2권2호
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• pp.215-222
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• 2015

Epileptic Seizure is a popular brain disease in the world. It affects the nervous system and the activities of brain function that make a person who has seizure signs cannot control and predict his actions. Based on the Electroencephalography (EEG) signals which are recorded from human or animal brains, the scientists use many methods to detect and recognize the abnormal activities of brain. Tucker model is investigated to solve this problem. Tucker decomposition is known as a higher-order form of Singular Value Decomposition (SVD), a well-known algorithm for decomposing a matric. It is widely used to extract good features of a tensor. After decomposing, the result of Tucker decomposition is a core tensor and some factor matrices along each mode. This core tensor contains a number of the best information of original data. In this paper, we used Tucker decomposition as a way to obtain good features. Training data is primarily applied into the core tensor and the remained matrices will be combined with the test data to build the Tucker base that is used for testing. Using core tensor makes the process simpler and obtains higher accuracies.