• 한국정보전자통신기술학회논문지
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• 제12권5호
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• pp.521-528
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• 2019

Epilepsy is one of the most prevalent neurological diseases. Electroencephalogram (EEG) signals are widely used for monitoring and diagnosis tool for epileptic seizure. Typically, a huge amount of EEG signals is needed, where they are visually examined by experienced clinicians. In this study, we propose a simple automatic seizure detection framework using intracranial EEG signals. We suggest a sparse approximation based classification (SAC) scheme by solving overdetermined system. L1-norm minimization algorithms are utilized for efficient sparse signal recovery. For evaluation of the proposed scheme, the public EEG dataset obtained by five healthy subjects and five epileptic patients is utilized. The results show that the proposed fast L1-norm minimization based SAC methods achieve the 99.5% classification accuracy which is 1% improved result than the conventional L2 norm based method with negligibly increased execution time (42msec).

• 정보과학회 논문지
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• 제44권9호
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• pp.887-892
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• 2017

뇌-컴퓨터 인터페이스는 정신 작업 동안 다채널에서 생성된 뇌파의 신호를 측정, 분석하여 컴퓨터를 제어하거나 의사를 전달하는 기술이다. 이때 최적의 뇌파 채널 선택은 뇌-컴퓨터 인터페이스의 편의성과 속도뿐만 아니라 정확도 향상을 위해 필요하다. 최적의 채널은 중복 채널들 또는 노이즈 채널들을 제거함으로써 얻는다. 이 논문에서는 최적 뇌파 채널을 선택하기 위해 이중 filter-기반의 채널 선택 방법을 제안한다. 제안한 방법은 먼저 채널들 간의 중복성을 제거하기 위해 spearman's rank correlation을 사용하여 중복 채널들을 제거한다. 그 뒤, F score를 이용하여 채널과 클래스 라벨 간의 적합성을 측정하여 상위 m개의 채널들만을 선택한다. 제안한 방법은 클래스 라벨과 관련되고 중복이 없는 채널들을 사용함으로써 좋은 분류 정확도를 이끌어 낼 수 있다. 제안한 채널 선택 방법은 채널의 수를 상당히 줄임과 동시에 평균 분류 정확도를 향상시켰다.

• Korean Journal of Acupuncture
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• 제28권3호
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• pp.85-98
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• 2011

Objectives : The aim of this thesis is to examine the effect of acupuncture treatment at the GB37 on normal humans by using the power spectral analysis of the EEG. Methods : EEG (Electroencephalogram) power spectrum exhibits site-specific and state-related differences in specific frequency bands. In this thesis, the power spectrum was measured by the complexity. the 32 channels EEG study was carried out in the 13 subjects (12 males ; age=22.58 years old, 1 females ; 22 years old). Results : In the ${\alpha}$ (alpha) band, the power values at F7, F3, F4, F8, FTC2, C4, T4, CP1, CP2, TCP2, TT2, Pz, P4, Po1, Po2, O1, Oz, O2 channels (p<0.05) during the GB37-acupoint treatment were significantly changed. And in many channels were decreased. In the ${\beta}$ (beta) band, the power values at Cz, C4, T4, Tcp1, T6, Po1, O1, Oz, O2 channels (p<0.05) during the GB37-acupoint treatment were significantly changed. And in many channels were decreased. In the ${\delta}$(delta) band, the power values at Fp1, TT2 channels (p<0.05) during the GB37-acupoint treatment were significantly changed. And in many channels were decreased. In the $\theta$ (theta) band, the power values at Fp1, F8, FTC2, Pz channels (p<0.05) during the GB37-acupoint treatment were significantly changed. And in many channels were decreased. Conclusions : This results suggest that the acupuncture treatment at the GB37 significantly mostly change the power spectrum value on the alpha (18 channels), beta (9 channels) bands.

• 한국임상수의학회지
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• 제17권2호
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• pp.359-367
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• 2000

The aim of this study was to evaluate the effects of propofol on cortical electroencephalogram (EEG) in seven dogs. Propofol infusion was accomplished from low concentration to high concentration in series, and each concentration was infused for 20 minutes (M0: 0, M0.5: 0.5, M1.0:1.0, and M1.5: 1.5 mg/kg/min of infusion rate). EEG was recorded via needle electrode placed at Cz, which was applied to International 10-20 system. Arterial blood pressure. blood gas analysis and ECG were also measured. Hoemodynamics, Pa$CO_2$, PaO$_2$, heart rate and respiratory rate were variable, but were net significant(p>0.05). The power spectra of EEG in every concentration was compared wish those of control (MO). The powers at a1l frequencies at M1.0 and Ml.5 were decreased. Especially, the powers of the frequencies over 20 Hz were significantly decreased (p<0.O5). Powers at frequencies between 8 and 15Hz at MO.S were significantly increased (p<0.05) in response to the painful stimuli. It was inferred that they may reflect activity of the brain which is consciously processing the external Stimuli. Like the Power spectra, al1 the band powers of He EEG ($\delta$ 1-4, $\theta$4-8, $\alpha$ 8-13, $\beta$L13-21. $\beta$H 21-30, \ulcorner 30-50, and total 1-5OHz) were decreased in proportion to the increase of infusion rate at M1 .0 and M1.5. Especially, decrease of $\beta$H and ${\gamma}$ were significant(p<0.01). At M0.5, $\alpha$ band was significantly increased(p<0.05) among all the bands. Seizure activities which were concide with occurrence of spike wave were shown in all dogs at Ml .0 and M1.5.

• 인간식물환경학회지
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• 제21권6호
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• pp.575-589
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• 2018

This study analyzed the changes in Electroencephalogram(EEG) and Electrocardiogram(ECG) depending on the healing environment in order to find a way to improve the forest healing program based on the healing environment in response to the demand for qualitative improvement of the program since the program is a charged service. This study selected eight sites running forest healing programs at four national healing forests (i.e., Saneum, Cheongtaesan, Daegwanryeng, and Jangseong) - two routes per national healing forest - considering forest environments. This study chose NUMBER standard sampling plots ($20{\times}20m$) and measured three atmospheric environment items, seven physical environment items, two soil environment items, and eight vegetation environment items including forest sound and anion at each plot to evaluate physiological changes in it. EEG and ECG, which have been widely used in forest healing evaluation, were utilized as criteria. Seventy three subjects were selected with taking the age, drug, caffeine, smoking, and the time of last meal into consideration. As a result, EEG changes were correlated with three atmospheric environment items, six physical environment items, one soil environment item, and two vegetation environment items. ECG changes were significantly correlated with two atmospheric environment items, six physical environment items, two soil environment items, and two vegetation environment items (p<.05). It is expected that 11 environmental factors such as temperature, density, and altitude affecting EEG (e.g., alpha balance and gamma balance) and ECG (e.g., HRV mean) could be used as effective tools in developing more differentiated programs for improving healing effects.

• 대한의용생체공학회:의공학회지
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• 제43권4호
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• pp.185-192
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• 2022

Early detection of mild cognitive impairment can help prevent the progression of dementia. The purpose of this study was to design and validate a machine learning model that automatically differential diagnosed patients with mild cognitive impairment and identified cognitive decline characteristics compared to a control group with normal cognition using resting-state quantitative electroencephalogram (qEEG) with eyes closed. In the first step, a rectified signal was obtained through a preprocessing process that receives a quantitative EEG signal as an input and removes noise through a filter and independent component analysis (ICA). Frequency analysis and non-linear features were extracted from the rectified signal, and the 3067 extracted features were used as input of a linear support vector machine (SVM), a representative algorithm among machine learning algorithms, and classified into mild cognitive impairment patients and normal cognitive adults. As a result of classification analysis of 58 normal cognitive group and 80 patients in mild cognitive impairment, the accuracy of SVM was 86.2%. In patients with mild cognitive impairment, alpha band power was decreased in the frontal lobe, and high beta band power was increased in the frontal lobe compared to the normal cognitive group. Also, the gamma band power of the occipital-parietal lobe was decreased in mild cognitive impairment. These results represented that quantitative EEG can be used as a meaningful biomarker to discriminate cognitive decline.

• 국제학술발표논문집
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• The 8th International Conference on Construction Engineering and Project Management
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• pp.237-242
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• 2020

Construction noise is among the most critical stressors that adversely affect the quality of life of the people residing near construction sites. Many countries strictly regulate construction noise based on sound pressure levels, as well as timeslots and type of construction equipment. However, individuals react differently to noise, and their tolerance to noise levels varies, which should be considered when regulating construction noise. Although studies have attempted to analyze individuals' stress responses to construction noise, the lack of quantitative methods to measure stress has limited our understanding of individuals' stress responses to noise. Therefore, the authors proposed a quantitative stress measurement framework with a wearable electroencephalogram (EEG) sensor to decipher human brain wave patterns caused by diverse construction stressors (e.g., worksite hazards). This present study extends this framework to investigate the feasibility of using the wearable EEG sensor to measure individuals' emotional stress responses to construction noise in a laboratory setting. EEG data were collected from three subjects exposed to different construction noises (e.g., tonal vs. impulsive noises, different sound pressure levels) recorded at real construction sites. Simultaneously, the subjects' perceived stress levels against these noises were measured. The results indicate that the wearable EEG sensor can help understand diverse individuals' stress responses to nearby construction noises. This research provides a more quantitative means for measuring the impact of the noise generated at a construction site on neighboring communities, which can help frame more reasonable construction noise regulations that consider various types of residents in urban areas.

• 대한의용생체공학회:의공학회지
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• 제44권6호
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• pp.392-403
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• 2023

This study presents a new hybrid fNIRS-EEG system to meet the demand for a lightweight and low-cost sleep pattern monitoring device. For multiple-channel configuration, a six-channel electroencephalogram (EEG) and a functional near-infrared spectroscopy (fNIRS) system with eight photodiodes (PD) and four dual-wavelength LEDs are designed. To enhance the convenience of signal measurement, the device is miniaturized into a patch-like form, enabling simultaneous measurement on the forehead. Due to its fully integrated functionality, the developed system is advantageous for performing sleep stage classification with high-temporal and spatial resolution data. This can be realized by utilizing a two-dimensional (2D) brain activation map based on the concentration changes in oxyhemoglobin and deoxyhemoglobin during sleep stage transitions. For the system verification, the phantom model with known optical properties was tested at first, and then the sleep experiment for a human subject was conducted. The experimental results show that the developed system qualifies as a portable hybrid fNIRS-EEG sleep pattern monitoring device.

• 대한불안의학회지
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• 제20권1호
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• pp.8-16
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• 2024

Objective : The purpose of this study is to compare the signal obtained from the frontal 2-electrodes EEG with that obtained from the temporal, central, and parietal 2 electrodes. Methods : EEGs were recorded in a total of 67 patients with major depressive disorder (MDD), 104 patients with schizophrenia (SCZ), and 29 patients with Alzheimer's disease (AD). For each disease group, there were healthy controls (HC) that were paired accordingly (HC1=69, HC2=104, HC3=27). The following measurements were compared across electrodes: band power, alpha peak frequency (APF), APF power, alpha asymmetry (AA), and Kolmogorov complexity (KC). Results : Statistically significant differences were found in band power measured from frontal electrodes compared to electrodes placed in other locations. Specifically, the power of theta waves was measured higher in the temporal electorodes, alpha 1 and alpha 2 waves in the parietal, beta 1 and beta 2 in the central, and gamma waves in the temporal electrodes. Both SCZ and AD patients showed increased theta power in all electrodes. In SCZ patients, APF decreased in the central and temporal electrodes, but the APF power analysis showed no difference between the patients and controls. Additionally, AD patients exhibited increased AA in the central EEG, while SCZ patients showed decreased KC in the parietal and temporal electrodes. Conclusion : Depending on the electrode location, sensitive EEG frequencies differed. Compared with signals from other electrodes, frontal EEG in MDD patients revealed generally constant signal values, though the temporo-parieto-central electrodes appeared to be more reliable in SCZ and AD patients.

• 전기전자학회논문지
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• 제23권2호
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• pp.727-734
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• 2019

본 논문은 인간의 의지가 뇌로부터 전달되는 과정에서 근육의 움직임이나 동작이 뇌의 특정 부위에서 유의미한 특징을 나타내는 신호를 찾아낼 수 있는지를 확인한다. 일반적으로 뇌파의 발생은 특정한 동작을 유발하고 유발된 동작으로부터 신호를 받아 변화를 보인다. 이러한 신호는 불확실성이 높으며 육안으로 판별하기엔 그 차이를 파악하기 어렵다. 따라서 분류에 앞서 어떤 신호를 분석할 것인지 정의하는 과정이 필요하다. 뇌파 혹은 뇌전도의 형태는 주파수 대역별로 분류하였을 경우, 알파, 베타, 델타, 쎄타, 감마의 영역으로 나눌 수가 있다. 뇌파의 측정 부위에 따라 활성화되는 주파수의 대역이나 에너지의 차이가 다르기 때문에 이들 신호의 특정한 크기가 정확한 동작이나 의지를 표현한다고 할 수는 없지만, 특정한 영역에서 다른 동작을 했을 경우의 뇌파 활성도를 기준으로 동작을 분류하거나, 동작에 영향을 미치는 뇌파의 경향성을 판단할 수 있다. 따라서 본 논문에서는 1차적으로 근육의 좌우 이두근의 근전도가 활성화 되는 시점을 기준으로 뇌파의 발현형태를 관찰하고, 이후 좌완과 우완의 근육 활성화에 따른 뇌파의 유의미한 차이를 뇌파를 통해 유추할 수 있는지를 검증한다. 근전도의 좌우활성화에 따른 뇌파의 분류기준을 찾을 수 있다면, 뇌로부터 발현된 신호가 각각의 근육에 전달되는 과정에서 전이된 신호의 형태를 파악하는데 도움을 줄 수 있으며, 향후 더욱 복잡한 뇌신호의 발생 유형을 통해 알려지지 않은 많은 뇌파의 정보를 활용할 수 있을 것으로 판단한다.