• Journal of the Korea Society of Computer and Information
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• v.11 no.5 s.43
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• pp.95-103
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• 2006

In this paper, the input pattern vectors are extracted and the learning algorithms is designed to recognize EEG pattern vectors. The frequency and amplitude of alpha rhythms and beta rhythms are used to compose the input pattern vectors. And the algorithm for EEG pattern recognition is used SOM to learn initial reference vectors and out-star learning algorithm to determine the class of the output neurons of the subclass layer. The weights of the proposed algorithm which is between the input layer and the subclass layer can be learned to determine initial reference vectors by using SOM algorithm and to learn reference vectors by using LVQ algorithm, and pattern vectors is classified into subclasses by neurons which is being in the subclass layer, and the weights between subclass layer and output layer is learned to classify the classified subclass, which is enclosed a class. To classify the pattern vectors of EEG, the proposed algorithm is simulated with ones of the conventional LVQ, and it was a confirmation that the proposed learning method is more successful classification than the conventional LVQ.

• Journal of Naturopathy
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• v.11 no.2
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• pp.79-84
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• 2022

Background: Exercise influences the generation of brain cells through learning and experience in the process of acquiring motor skills and helps improve brain function. It is necessary to scientifically verify how brain wave activity, a method of analyzing brain function, affects movement. Purposes: We scientifically identify the positive effects on EEG activity when applying complex functional linear ladder movements in an appropriate environment. Methods: After recruiting 30 male university students, we divided them into a linear ladder exercise group, a treadmill exercise group, and a control group, and exercise was applied and measured repeatedly for ten weeks. Results: There was a statistically significant change between groups in the left prefrontal lobe of alpha waves when exercise was applied (p < .05). Conclusions: Although exercise has a positive effect on EEG, line ladder exercise, which applies a complex pattern and produces more leg movement, appears to have a better impact on brain function than traditional aerobic exercise.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2001.05a
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• pp.26-31
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• 2001

긍/부정 선택 실험에서 나타나는 뇌파 변화를 연구하였다. 서로 다른 위치에서 측정된 뇌파의 시공간적 상호관계를 정량화하는 변수로, 시간영역에서 계산하기 용이한 동기율(synchronization rate), 편향성(synchronization rate), 편향성(polarity), 상호상관(cross-correlation) 등의 변수를 도입하여, 긍/부정 선택 순간의 뇌파 변화를 살펴보았다. 좌우 전전두엽(Fp1, Fp2)에서 특정된 뇌파를 사용하여 계산한 동기율, 편향성의 평균과 요동폭, 상호상관 등은, 선택 순간 근처에서, 평상시에 뇌파와 통계적으로 유의미한 차이를 보였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.66-68
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• 2012

본 논문에서는 자체 개발한 Java Platform 기반의 뇌파 분석 도구와 Android 기반의 Mobile 기기를 연계하여 중증 장애인의 상태 및 상황 등을 모니터링 할 수 있는 시스템을 제시한다. 제안된 시스템은 뇌파 측정기, 뇌파 분석 툴(PC Client) 그리고 Mobile 기기(Android)로 크게 3부분으로 구성된다. 뇌파 측정기로부터 수집된 원 주파수에서 저주파 대역의 잡음을 제거하기 위해 고주파 필터를 적용한 후, 적용된 데이터를 주파수 영역에서 분석하기 위해 FFT를 적용한다. FFT를 적용한 데이터를 Power Spectrum 분석 기법을 이용하여 Theta, Delta, Alpha, SMR, Beta 파형의 값을 추출하고, 14 채널의 뇌파 측정 위치에 따른 상관관계 분석기법을 통해 중증 장애인의 상태를 표현한다. 본 논문에서 제안한 방법으로 실험한 결과, 중증 장애인 모니터링 시스템에 효율적으로 적용되는 것을 확인하였다.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2001.11a
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• pp.255-259
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• 2001

본 연구에서는 긍/부정 판단 과제 시 연령간 뇌파 반응의 차이를 밝히고자 한다. 실험 대상의 연령은 10명(20대 5명, 60대 5명)이었으며, 모두 오른손잡이였다. 실험과제는 의미과제와 일화과제로 구분되며 각각 12문항으로 구성된다. 의미기억과제 덧셈문제를, 일화기억 과제는 도형을 이용하였으며, 마우스 버튼을 눌러 긍/부정 판단 반응을 하도록 하였다. 뇌파는 PE1, PF2, F3, F4, O1, O2에서 단극유도법으로 측정되었으며, EOG를 측정하여 뇌파분석 시에 눈 깜박임으로 인하 noise를 제거하도록 하였다. 뇌파 분석은 원자료를 FFT(Fast Fourier Transformation)를 수행하여 각 대역의 상대적인 power를 구하는 방법으로 이루어졌다. 분석 결과, 반응 시간은 긍/부정판단간의 차이는 없었으나, 두 과제 모두에서 연령별로 유의미한 차이가 있었다. 긍/부정판단간의 따른 뇌파 반응은 명확한 차이가 나타나지 않았다. 연령에 따른 뇌파반응은 theta파, slow beta, fast beta에서 유의한 차이가 나타났다.

• Journal of Biomedical Engineering Research
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• v.20 no.1
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• pp.81-90
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• 1999

This paper describes an EEG(electroencephalogram) software for two-channel cerebral function monitoring system to detect the cerebral ischemia. In the software, two-channel bipolar analog EEG signals are digitized and from the signals various EEG parameters are extracted and displayed on a monitor in real-time. Digitized EEG signal is transformed by FFT(Fast Fourier transform) and represented as CSA(compressed spectral array) and DSA(density spectral array). Additional 5 parameters, such as alpha ratio, percent delta, spectral edge frequency, total power, and difference in total power, are estimated using the FFT spectra. All of these are effectively merged in a monitor and displayed in real-time. Through animal experiments and clinical trials on men, the software is modified and enhanced. Since the software provides raw EEG, CSA, DSA, simultaneously with additional 5 parameters in a monitor, it is possible to observe patients multilaterally. For easy comparison of patient's status, reference patterns of CSA, DSA can be captured and displayed on top of the monitor. And user can mark events of surgical operation and patient's conditions on the software, this allow him jump to the points of events directly, when reviewing the recorded EEG file afterwards. Other functions, such as forward/backward jump, gain control, file management are equipped and these are operated by simple mouse click. Clinical tests in a university hospital show that the software responds accurately according to the conditions of patients and medical doctors can use the software easily.

• Journal of Digital Contents Society
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• v.19 no.4
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• pp.821-827
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• 2018

Affective computing has gained increasing interest in the recent years with the development of potential applications in Human computer interaction (HCI) and healthcare. Although momentous research has been done on human emotion recognition, however, in comparison to speech and facial expression less attention has been paid to physiological signals. In this paper, Electroencephalogram (EEG) signals from different brain regions were investigated using modified wavelet energy features. For minimization of redundancy and maximization of relevancy among features, mRMR algorithm was deployed significantly. EEG recordings of a publically available "DEAP" database have been used to classify four classes of emotions with Multi class Support Vector Machine. The proposed approach shows significant performance compared to existing algorithms.

• Journal of the Korean Society for Library and Information Science
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• v.49 no.1
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• pp.443-464
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• 2015

This study examined the feasibility of using EEG signals and ERP P3b for extracting video key-frames based on users' cognitive responses. Twenty participants were used to collect EEG signals. This research found that the average amplitude of right parietal lobe is higher than that of left parietal lobe when relevant images were shown to participants; there is a significant difference between the average amplitudes of both parietal lobes. On the other hand, the average amplitude of left parietal lobe in the case of non-relevant images is lower than that in the case of relevant images. Moreover, there is no significant difference between the average amplitudes of both parietal lobes in the case of non-relevant images. Additionally, the latency of MGFP1 and channel coherence can be also used as criteria to extract key-frames.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.3
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• pp.136-141
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• 2012

One of the main reasons for serious road accidents is driving while drowsy. For this reason, drowsiness detection and warning system for drivers has recently become a very important issue. Monitoring physiological signals provides the possibility of detecting features of drowsiness and fatigue of drivers. One of the effective signals is to measure electroencephalogram (EEG) signals and electrooculogram (EOG) signals. The aim of this study is to extract drowsiness-related features from a set of EEG signals and to classify the features into three states: alertness, drowsiness, sleepiness. This paper proposes a neural-network-based drowsiness detection system using Linear Predictive Coding (LPC) coefficients as feature vectors and Multi-Layer Perceptron (MLP) as a classifier. Samples of EEG data from each predefined state were used to train the MLP program by using the proposed feature extraction algorithms. The trained MLP program was tested on unclassified EEG data and subsequently reviewed according to manual classification. The classification rate of the proposed system is over 96.5% for only very small number of samples (250ms, 64 samples). Therefore, it can be applied to real driving incident situation that can occur for a split second.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.7
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• pp.623-629
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• 2013

This paper presents the implementation result of the EEG(electroencephalogram) signal transmission protocol and its test platform. EEG measured by a dry-type electrode is directly converted into digital signal by ADC(analog-to-digital converter). Thereafter it is transferred DSP(digital signal processor) platform by $I^2C$(inter-integrated circuit) protocol. DSP conducts the pre-processing of EEG and extracts feature vectors of EEG. In this work, we implement the $I^2C$ protocol with 16 channels by using 10 or 12-bit ADC. In the implementation results, the overhead ratio for the 4 bytes data burst transmission measures 2.16 and the total data rates are 345.6 kbps and 414.72 kbps with 10-bit and 12-bit 1 ksps ADC, respectively. Therefore, in order to support a high speed mode of $I^2C$ for 400 kbps, it is required to use 16:1 and $(8:1){\times}2$ ratios for slave:master in 10-bit ADC and 12-bit ADC, respectively.