• 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.

• International Journal of Control, Automation, and Systems
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• 제4권5호
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• pp.592-600
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• 2006

The pattern recognition of the complicated grasping operation based on electroencephalography (simply named as EEG) is very helpful on realtime control of the robotic hand. In the paper, a new spectral feature analysis method based on Band Pass Filter (simply named as BPF) and Power Spectral Analysis (simply named as PSA) is presented for discriminating the complicated grasping operations. By analyzing the spectral features of grasping operations with the use of the two-channel EEG measurement system and the pattern recognition of the BP neural network, the degree of recognition by the traditional spectral feature method based on FFT and the new spectral features method based on BPF and PSA could be compared. The results show that the proposed method provides highly improved performance than the traditional one because the new method has two obvious advantages such as high recognition capability and the fast learning speed.

• 산업경영시스템학회지
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• 제19권39호
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• pp.129-142
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• 1996

The early diagnosis has an very important role in curing dementia. But there was not the effective method to diagnose it until now. In this paper we analyzed the EEG of Alzheimer's disease patients and normal groups by nonlinear methods. In the analysis we calculated the correlation dimensions $D_2$ and the largest Lyapunov exponent $L_1$. We found that patients with Alzheimer's disease have significantly lower $D_2$ and TEX>$L_1$ than normal groups. It means that brains injured by Alzheimer's disease have electrophysiological inactive elements and have decreased chaotic behaviour. We propose the nonlinear analysis of the EEG as a useful tool for the early diagnosis of Alzheimer's disease.

• International Journal of Contents
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• 제9권4호
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• pp.1-10
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• 2013

The electroencephalogram (EEG) time series is a measure of electrical activity received from multiple electrodes placed on the scalp of a human brain. It provides a direct measurement for characterizing the dynamic aspects of brain activities. These EEG signals are formed from a series of spatial and temporal data with multiple dimensions. Missing data could occur due to fault electrodes. These missing data can cause distortion, repudiation, and further, reduce the effectiveness of analyzing algorithms. Current methodologies for EEG analysis require a complete set of EEG data matrix as input. Therefore, an accurate and reliable imputation approach for missing values is necessary to avoid incomplete data sets for analyses and further improve the usage of performance techniques. This research proposes a new method to automatically recover random consecutive missing data from real world EEG data based on Linear Dynamical System. The proposed method aims to capture the optimal patterns based on two main characteristics in the coevolving EEG time series: namely, (i) dynamics via discovering temporal evolving behaviors, and (ii) correlations by identifying the relationships between multiple brain signals. From these exploits, the proposed method successfully identifies a few hidden variables and discovers their dynamics to impute missing values. The proposed method offers a robust and scalable approach with linear computation time over the size of sequences. A comparative study has been performed to assess the effectiveness of the proposed method against interpolation and missing values via Singular Value Decomposition (MSVD). The experimental simulations demonstrate that the proposed method provides better reconstruction performance up to 49% and 67% improvements over MSVD and interpolation approaches, respectively.

• 수면정신생리
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• 제17권2호
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• pp.75-84
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• 2010

The cyclic alternating pattern (CAP) is a periodic EEG activity in NREM sleep, characterized by sequences of transient electrocortical events that are distinct from background EEG activities. A CAP cycle consists of two periodic EEG features, phase A and subsequent phase B whose durations are 2-60 s. At least two consecutive CAP cycles are required to define a CAP sequence. The CAP phase A is a phasic EEG event, such as delta bursts, vertex sharp transients, K-complex sequences, polyphasic bursts, K-alpha, intermittent alpha, and arousals. Phase B is repetitive periods of background EEG activity. The absence of CAP more than 60 seconds or an isolated phase A is classified as non-CAP. Phase A activities can be classified into three subtypes (A1, A2, and A3), based on the amounts of high-voltage slow waves (EEG synchrony) and low-amplitude fast rhythms (EEG desynchrony). CAP rate, the percentage of CAP durations in NREM sleep is considered to be a physiologic marker of the NREM sleep instability. In insomnia, the frequent discrepancy between self-reports and polysomnographic findings could be attributed to subtle abnormalities in the sleep tracing, which are overlooked by the conventional scoring methods. The conventional scoring scheme has superiority in analysis of macrostructure of sleep but shows limited power in finding arousals and transient EEG events that are major component of microstructure of sleep. But, it has recently been found that a significant correlation exists between CAP rate and the subjective estimates of the sleep quality in insomniacs and sleep-improving treatments often reduce the amount of CAP. Thus, the extension of conventional sleep measures with the new CAP variables, which appear to be the more sensitive to sleep disturbance, may improve our knowledge on the diagnosis and management of insomnia.

• 한국지능시스템학회논문지
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• 제20권3호
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• pp.388-393
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• 2010

문화콘텐츠기술(CT, Culture Technology)은 문화 산업 발전을 위한 기술로 문화콘텐츠 기획과 상품화, 미디어탑재, 전달의 가치사슬 과정 등 문화상품의 부가가치를 높이기 위해 소용되는 모든 형태의 유무형의 기술이다. 문화콘텐츠 기술(CT)분야에서 음악의 특징을 분석하여 다양한 어플리케이션을 개발하는 연구가 활발히 진행되고 있다. 이와 관련된 연구 중 EEG를 측정하고 그 결과에서 음악적 자극에 대한 반응을 검출하여 활용하는 연구가 주목받고 있다. 본 논문에서는 EEG에서 음악적 자극에 대응되는 반응신호들을 증폭시켜 평균화 하는 방법인 ERP(Event-Related Potentials) 실험을 기반으로 음색을 추출하는 과정에서 노이즈를 제거하기 위한 방법으로 ICA 알고리즘을 적용하여 음색 추출 및 노이즈 제거 결과에 따른 EEG의 특성을 분석하여 적용한다.

• 사물인터넷융복합논문지
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• 제9권1호
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• pp.25-31
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• 2023

뇌-기계 인터페이스는 차세대 인터페이스로서 기기 이용자가 명령을 생각할 때 발생하는 신경세포의 전기적 신호인 뇌파를 해석하여 기기를 조종하는 인터페이스다. 뇌-기계 인터페이스는 다양한 스마트기기 등에 응용될 수 있지만 뇌파 신호를 해석하는 데는 상당량의 계산 프로세스가 필요하다. 따라서 에지(Edge) 형태로 구현된 임베디드 시스템에서는 뇌-기계 인터페이스를 구현하기가 어렵다. 본 연구에서는 사물인터넷 기술을 이용하여 에지에서는 뇌파 측정만을 진행하고 뇌파 데이터의 저장 및 분석은 클라우드 컴퓨팅에서 수행하는 새로운 형태의 뇌-기계 인터페이스 시스템을 제안하였다. 본 시스템은 뇌-기계 인터페이스를 위한 정량 뇌파 분석을 성공적으로 수행하였으며 데이터 송수신 시간 또한 실시간 처리가 가능한 수준을 보였다.

• Korean Journal of Acupuncture
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• 제31권3호
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• pp.136-145
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• 2014

목적 : 본 무작위배정 플라시보 대조군 교차연구는 신문혈의 침 치료가 뇌 활성도와 자율신경계에 어떠한 영향을 미치는지 뇌파(EEG)와 심박변이도(HRV)를 통해 알아보고자 한 연구이다. 방법 : 18명의 건강인 피험자가 참여하여 1-3일의 간격을 두고 무작위배정 순서에 따라 신문혈에 진짜침과 가짜침으로 시술받았고, 치료 전, 후에 각각 뇌파와 심박변이도를 측정하였다. 피험자의 스트레스 정도에 따른 반응의 차이를 살펴보기 위하여 스트레스설문지를 이용하여 서브그룹분석을 시행하였다. 결과 : 그 결과, 침 치시술은 뇌파 중 ${\alpha}$밴드 값의 증가를 나타냈으며, 진짜침 그룹의 경우 심박변이도의 결과값 중 HF와 RMS-SD의 증가가 대조군에 비해 유의하게 높았다. 스트레스 정도에 따른 서브그룹 분석에서는 진짜침 그룹 중 스트레스 정도가 높은 피험자는 ${\alpha}$밴드 값이 증가한 반면, 스트레스 정도가 낮은 피험자는 감소하거나 증가의 폭이 낮은 것을 알 수 있었으며, 거짓침 그룹에서는 비교적 적은 변화를 나타내었다. 결론 : 본 연구 결과를 통해 침이 뇌 활성도 및 자율신경계에 영향을 미치며, 이는 부교감신경계통과 관련이 있음을 알 수 있었다. 또한 이러한 결과는 환자의 스트레스 정도에 따라 다른 반응을 나타냄을 보여주었다.

• 한국의류학회지
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• 제33권10호
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• pp.1665-1672
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• 2009

This study examines the comfort of outdoorwear by electrocardiogram (ECG) and electroencephalogram (EEG) analyses. An experiment that consisted of rest (30 min), exercise (30 min), and recovery (20 min) periods was administered in a climate chamber with 10 healthy male participants. Two kinds of outdoorwear made of 100% cotton fabrics ('Control') and specially engineered fabrics having the feature of quick sweat absorbency and high speed drying fabric ('Functional') are evaluated in the experiment. ECG and EEG signals were obtained during the rest and recovery periods for the two outdoorwear conditions. The ECG analysis identified a smaller decrement of high frequency (HF) power for the 'Functional' when compared with the 'Control' during the recovery period. Next, the EEG analysis showed that the relative band powers of slow $\alpha$ and mid $\alpha$ increased for the 'Functional' while they decreased for the 'Control' and that the ratio of $\alpha$ power to high $\beta$ power was higher for the 'Functional'. The evaluation results indicate that the participants could remain relaxed more with less stress while wearing the functional outdoorwear that demonstrated the positive effects on autonomic nervous system (ANS) activities. The present study is significant in regard that use of ECG and EEG for the assessment of wear comfort is the first in the field of clothing and textile.

• 한국콘텐츠학회논문지
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• 제11권4호
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• pp.42-50
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• 2011

생체신호인 뇌파를 이용하여 이러닝 학습자의 학습태도를 파악하고 그에 따른 적절한 피드백을 제공하여 학습자의 학습효율을 극대화하려는 연구의 일환으로 여대생을 대상으로 학습자의 학습태도와 뇌파를 분석하여 이들의 상관관계를 밝혀보고자 한다. 학습자가 학습에 집중하는 태도와 그렇지 않은 태도에 대해 뇌파의 파워 스펙트럼을 추출하여 학습자의 뇌파가 어떻게 반응하는지에 중점을 두어 연구하였다. 학습에 집중하는 태도의 대조군으로 산만한 태도와 눈감은 태도를 설정하여 실험을 진행하였다. 학습에 집중하는 태도에서는 집중도가 산만한 태도에 비하여 높게 나타나고 이완지표는 낮게 나타나며, 클릭과 눈굴림과 같은 산만한 태도에서는 주의지표와 잡파 비율이 높게 나왔다. 특히, 눈을 감았을 때는 알파 세타 비율이 1이하로 나타나 눈을 뜬 다른 상태와 뚜렷이 구분되었다.