• Proceedings of the Korea Information Processing Society Conference
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• 2001.04a
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• pp.297-300
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• 2001

뇌파(Electroencephalogram, EEG)는 뇌의 자발적 전기활동을 두피에서 측정한 것이다. 그 동안 뇌질환과 관련된 임상에서 주로 사용되어져 왔으며, 비선형 동역학 연구를 통해 결정론적인 동역학 신호임이 밝혀짐에 따라 뇌 기능연구 분야에서 그 응용범위가 넓어지고 있다. 우리는 뇌파 신호에 대하여 독립성분분석(Independent Component Analysis, ICA)을 통하여 그 결과를 알아보았다. 즉, 뇌파의 독립성분 분석 적용 타당성을 알아본 다음 이를 적용하여 독립 소스들을 분리해 내었다. 또한 Topological Mapping을 이용하여 각각의 독립 소스들이 뇌의 어느 위치에서 발생하는지도 알아보았다. 이를 통하여 EEG에 독립성분분석을 적용함으로써 뇌 활동의 시간적, 공간적 분석이 가능하고 유용함을 나타내었다.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1396-1398
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• 2015

BCI (Brain Computer Interface)는 사람의 두뇌와 컴퓨터를 연결하는 '뇌-컴퓨터 인터페이스'를 나타내는 것이며 EEG(Electroencephalogram)을 주로 분석하여 인간의 행동이나 의도를 파악한다. 본 논문에서는 EEG를 이용한 행동인식의 하나로 졸음을 판단하는 방법을 제안한다. 제안방법에서는 MindWave를 이용하여 얻은 실험 데이터를 FFT를 이용하여 1초 단위로 스펙트럼을 분석하여 High-Alpha 영역의 시간에 따른 데이터 변화 패턴을 분석하여 졸음을 판단한다. 실험 결과, 100%의 최고 성능을 얻을 수 있었다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.1
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• pp.12-18
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• 2013

Motor imagery classification in electroencephalography (EEG)-based brain-computer interface (BCI) systems is an important research area. To simplify the complexity of the classification, selected power bands and electrode channels have been widely used to extract and select features from raw EEG signals, but there is still a loss in classification accuracy in the state-of- the-art approaches. To solve this problem, we propose a discriminative feature extraction algorithm based on power bands with principle component analysis (PCA). First, the raw EEG signals from the motor cortex area were filtered using a bandpass filter with ${\mu}$ and ${\beta}$ bands. This research considered the power bands within a 0.4 second epoch to select the optimal feature space region. Next, the total feature dimensions were reduced by PCA and transformed into a final feature vector set. The selected features were classified by applying a support vector machine (SVM). The proposed method was compared with a state-of-art power band feature and shown to improve classification accuracy.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.2
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• pp.178-183
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• 2013

In this study, we proposed a method for electroencephalogram (EEG) classification using invariant CSP at special channels for improving the accuracy of classification. Based on the naive EEG signals from left and right hand movement experiment, the noises of contaminated data set should be eliminate and the proposed method can deal with the de-noising of data set. The considering data set are collected from the special channels for right and left hand movements around the motor cortex area. The proposed method is based on the fit of the adjusted parameter to decline the affect of invariant parts in raw signals and can increase the classification accuracy. We have run the simulation for hundreds time for each parameter and get averaged value to get the last result for comparison. The experimental results show the accuracy is improved more than the original method, the highest result reach to 89.74%.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.21 no.7
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• pp.629-636
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• 2011

In this study, survey experiment and EEG test was carried out to know the effect of visual images on EEG for evaluating words of soundscape sound source with 18 subjects. Analysis on the EEG were executed to know the difference according to with and without visual images. Followings are results of this study. 1) There is no big difference with visual images in soundscape sound evaluating adjectives such as "Full", "Clear", "Enjoyable" whereas there is a big difference in soundscape sound evaluating adjectives such as "Pleasant", "Comfortable", "Gentle", "Sonorous". 2) There is a tendency that soundscape sound source which is consist of single sound source shows + 1 above increase in survey test when visual image is suggested whereas soundscape sound source which is consist of one more sound source shows - 1 below decrease in survey test. 3) Statistical analysis was used to know considerable probability. ${\alpha}$-wave has a considerable probability and Maximum level difference occurring brain spots were number 1 and 2.

• Journal of the Korean Society of Clothing and Textiles
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• v.45 no.2
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• pp.305-316
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• 2021

In this study, the effect of wearing functional compression pants is verified using a lower-limb wearable robot through a bio-signal analysis and subjective fit evaluation. First, the compression area to be applied to the functional compression pants is derived using the quad method for nine men in their 20s. Subsequently, functional compression pants are prepared, and changes in Electroencephalogram (EEG) and Electrocardiogram (ECG) signals when wearing the functional compression and normal regular pants inside a wearable robot are measured. The EEG and ECG signals are measured with eyes closed and open. Results indicate that the Relative alpha (RA) and Relative gamma wave (RG) of the EEG signal differ significantly, resulting in increased stability and reduced anxiety and stress when wearing the functional compression pants. Furthermore, the ECG analysis results indicate statistically significant differences in the Low frequency (LF)/High frequency (HF) index, which reflect the overall balance of the autonomic nervous system and can be interpreted as feeling comfortable and balanced when wearing the functional compression pants. Moreover, subjective sense is discovered to be effective in assessing wear fit, ease of movement, skin friction, and wear comfort when wearing the functional compression pants.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.12
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• pp.1421-1426
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• 2014

Many people complain their big or little stress due to the complicated city life in modern times, so they are exposed of the mental illness. Especially, not only students and office workers but also most people suffer from degradation of efficiency at work and keeping the high quality of life because of the insufficiency of concentration ability. To improve the concentration ability, the meditation is a substitution. The influence of meditation for the concentration ability is experimented with EEG brainwave. Some experienced meditators are participated for the experiments, and the left and right portion of prefrontal lobe, AF3 and AF4, are measured and analyzed. As a result, the changes of rhythmic activity of a unique pattern and power spectra are observed.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.7
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• pp.717-722
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• 2016

The purpose of this study is to observe the changes of EEG signals in the process for solving the problems in concentration. In the experiments, subjects were given two tasks. The first task is to memorize the words after they used their own smart phone for ordinary commercial games and the second task is to memorize the words after they read a page of a p-book. In this paper, we present SMR waves and mid-beta waves to analyze from the EEG signals of the subjects because the waves are the EEG components related to concentration of human.

• Journal of Haehwa Medicine
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• v.10 no.1
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• pp.365-374
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• 2001

The brain is one of the most complex systems in nature. Brain waves, or the "EEG", are electrical signals that can be recorded from the brain, either directly or through the scalp. The kind of brain wave recorded depends on the behavior of the animal, and is the visible evidence of the kind of neuronal (brain cell) processing necessary for that behavior. But, EEG had been considered as a virtually infinite-dimensional random signal. However, nonlinear dynamics light on dynamical aspects of the human EEG. The methods of nonlinear dynamics provide excellent tolls for the study of multi-variable, complex system such as EEG. In this study, 20 persons seperated in 2 groups were examined with EEG, one group stimulated on specific area of the sole of the foot with footbed inside the shoes. This experiment resulted in at the group stimulated on specific area of the sole of the foot correlation dimension of P4 and O1 channels increased significantly. Therefore. we obserbed that stimulation on specific area of the body had a constant effections on the specific channels.

• The Korean Journal of Physiology and Pharmacology
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• v.3 no.3
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• pp.245-250
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• 1999

The effects of an antipsychotic, chlorpromazine, on the electroencephalogram (EEG) were observed while rats were awake but immobile. The time course and the dose-dependency of the EEG changes were examined. The method of the power spectrum analysis was used to examine the EEG changes by the drug. The bands were divided into delta $(1{\sim}3.5\;Hz),$ theta $(3.5{\sim}8\;Hz),$ alpha $(8{\sim}13\;Hz),$ beta1 $(13{\sim}21\;Hz),$ beta2 $(21{\sim}30\;Hz)$ and gamma $(30{\sim}50\;Hz).$ In rats, the low dose of chlropromazine (1 mg/kg, i.p.) produced a significant increase in the power of the beta1 band. The higher doses (5, 10 mg/kg, i.p.) produced a significant increase in the power of the delta, theta, alpha and beta1 bands, and the decrease in the power of the gamma band. The powers of the bands changed dose-dependently. Then, the authors discussed whether the EEG effects produced by a drug are associated with the accompanying behavioral changes specifically.