• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.6
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• pp.949-954
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• 2013

We measured and analyzed the brain waves to observe the characteristics of human drowsiness. The basic method is to analyze the EEG(Electroencephalography) signals from subjects according to the frequency bands. It has been reported that alpha waves are related to a wakefulness state, an eye closure state and a state that begins to sleep. In this study, therefore, we restricted the frequency band for analyzing to between 8 and 13Hz called brain's alpha waves. We observed which components had a stronger influence on human drowsiness among the restricted frequency band and represented the experimental results to analyze using the power spectrum method.

• Journal of Biomedical Engineering Research
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• v.30 no.4
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• pp.327-332
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• 2009

In this paper, new index was developed to estimate the depth of anesthesia during general anesthesia using EEG. Analysis of the power spectral density(PSD) of EEG was used to develop new parameters because EEG signal tends to have slow wave during anesthesia. Classifier for index creator was developed by using SEF, BDR and BTR parameters, which are calculated by power spectral density. EEG data were obtained from 7 patients (ASA I, II) during general anesthesia with Sevoflurane. The anesthetic depth evaluation indexes ranged from 0 to 100. The average were $86.05{\pm}10.1$, $36.98{\pm}20.2$, $15.33{\pm}13.6$, $50.87{\pm}16.5$ and $87.72{\pm}11.7$ for the states of pre-operation, induction of anesthesia, operation, awaked and post-operation, respectively. The results show that while the depth of anesthesia was evaluated, more accurate information can be provided for anesthetician.

• 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 KIISE:Computing Practices and Letters
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• v.14 no.5
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• pp.497-501
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• 2008

In this paper we present a method for continuous EEG classification, where we employ nonnegative tensor factorization (NTF) to determine discriminative spectral features and use the Viterbi algorithm to continuously classily multiple mental tasks. This is an extension of our previous work on the use of nonnegative matrix factorization (NMF) for EEG classification. Numerical experiments with two data sets in BCI competition, confirm the useful behavior of the method for continuous EEG classification.

• Korean Journal of Acupuncture
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• v.20 no.3
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• pp.49-60
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• 2003

The aim of this study was to examine the effects of electroacupuncture(EA) at the PC6 (Naegwan) on normal humans using KarhunenLoeve decomposition method. Electroencephalogram(EEG) is a multi-scaled signal consisting of several components of time series with different dominant frequency ranges and different origins. EEG KarhunenLoeve decomposition method exibit site-specific and state-related differences in specific frequency bands. In this study, KarhunenLoeve decomposition method was used as a measure(D2) of complexity. 30 channel EEG study was carried out in 10 subjects (10 males; $age=21.4{\pm}0.5$ years). Results : We found that the average values and standard deviations of D2 at FP1, FP2, FTC1, FTC2, TT1, TT2, T4, TCP1, P3, P4, T6, OZ channel (p<0.05) were higher than during the acupuncture treatment, and the average values and standard deviations of D2 at F3, F8 channels(p<0.05) were lowered than during the acupuncture treatment. However, the comparison with that before and after the treatment shows no significant differences in all channels.

• 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.

• Journal of Acupuncture Research
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• v.18 no.2
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• pp.67-78
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• 2001

The aim of this study was to examine the effects of low frequency electroacupuncture(EA) at the $B_{62}$ (Shinmaek) $K_6$(Chohae) on vascular dementia in humans using nonlinear dynamics. Electroencephalogram(EEG) is a multi-scaled signal consisting of several components of time series with different dominant frequency ranges and different origins. Nonlinear measures of the EEG like the correlation dimension ($D_2$) and the first positive Lyapunov exponent ($L_1$) reflect the complexity of the EEG. In this study, $D_2$ was used as a measure of complexity. Sixteen channel EEG study was carried out in six subjects (5 females and 1 males; $age=83.83{\pm}7.19years$). We found that the baseline $D_2$ values of the EEG at F4 and F8 channels (P<0.01) were lowered than during the acupuncture treatment, indicating decreased complexity of the EEG. However, the comparison with that before and after the treatment shows no significant differences in all channels.

• Journal of Acupuncture Research
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• v.27 no.1
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• pp.137-148
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• 2010

Objectives : To research the changes of electroencephalogram(EEG) signals for acupuncture stimulation and to establish the hereafter direction for the study on EEG. Methods : We reviewed the domestic papers searched by search engine of Korean Acupuncture & Moxibustion Society and Korea Institute of Oriental Medicine. Results : We have searched 31 articles in 10 journals. The 13 articles were concerned with acupuncture. 1. All articles were published after 2001. In 2007 there were 10 articles. 2. The studies dealing with the changes of EEG signals were 24, the studies dealing with correlation of EEG signals were 5, and the studies analyzing EEG with Korean medicine were 2. 3. In the studies dealing with the changes of EEG signals, the case-control studies were 9, the non case-control studies were 14, and the case study was 1. 10 studies used electro-acupuncture, 1 study used herbal acupuncture, and 2 studies used manual acupuncture. Conclusions : We need more various kinds of studies. 1. Excited condition by acupuncture stimulation may reduce $\alpha$ wave. 2. There may be the acupuncture point-specific variation of EEG signal patterns. 3. The number of responding channels for acupuncture stimulation may correlate with the quantity or variety of acupuncture effect.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.6
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• pp.1333-1342
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• 2018

Until now, Electroencephalography(: EEG) has been the most important and convenient method for the diagnosis and treatment of epilepsy. However, it is difficult to identify the wave characteristics of an epileptic EEG signals because it is very weak, non-stationary and has strong background noise. In this paper, we analyse the effect of dimensionality reduction methods on Epileptic EEG feature selection and classification. Three dimensionality reduction methods: Pincipal Component Analysis(: PCA), Kernel Principal Component Analysis(: KPCA) and Linear Discriminant Analysis(: LDA） were investigated. The performance of each method was evaluated by using Support Vector Machine SVM, Logistic Regression(: LR), K-Nearestneighbor(: K-NN), Decision Tree(: DR) and Random Forest(: RF). From the experimental result, PCA recorded 75% of highest accuracy in SVM, LR and K-NN. KPCA recorded 85% of best performance in SVM and K-KNN while LDA achieved 100% accuracy in K-NN. Thus, LDA dimensionality reduction is found to provide the best classification result for epileptic EEG signal.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.257-260
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• 1993

A new method of spindle waveform detection is presented for the automated analysis of sleep EEG. The method is based on the combined application of signal conditioning in the time-domain and local spectrum analyzing in the frequency-domain. The overall detection system is implemented and, tested in real-time with a total of 24 hour data obtained from four subjects. The result shows an average agreement of 86.7% with the visually inspected result.