• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2001.05a
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• pp.271-275
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• 2001

두뇌-컴퓨터 인터페이스로 활용하기 위한 시도로서, 인간의 가장 간단한 의사 표시인 긍/부정 의사와 관련한 뇌파를 측정하고 시간-주파수 분석을 수행하였다. 선행 연구 결과와 뇌파 측정 실험 조건에 대해 살펴 보고, 시간-주파수 분석 결과로부터 긍/부정 반응 동작에 따른 뇌파 변화에 대해 토론하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.468-470
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• 2024

고령으로 거동이 불편하거나 목 아래 전신 마비 또는 와병 환자를 위하여 저가형 단 채널 뇌파(EEG) 측정기를 사용하여 환자의 흥분이나 불안정 상태를 인식할 수 있는 순환신경망(RNN) 기반 뇌파 인식기 모델을 제안하였다. 제한된 환경에서 GRU(Gate Recurrent Unit) 신경망을 사용한 뇌파 인식기는 <정상/안정>은 인식률 100%, <흥분/불안정>은 90%의 인식률을 보였다. 또한, 자체 개발한 생체신호 기반 호출 시스템과 연동하여 구현함으로써 적용 가능성을 검증하였다.

• Science of Emotion and Sensibility
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• v.3 no.1
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• pp.17-24
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• 2000

본 연구는 일반 컴퓨터 게임과 뇌파를 이용한 바이오피드백 게임의 수행에 따른 뇌파의 변화를 비교하고자 하였다. 뇌파는 게임을 수행하는 동안 변화된 slow alpha파와 fast beta파의 relative power change를 실시간으로 분석하였다. slow alpha파와 fast beta파만을 분석한 이유는 두 뇌파 유형이 시시각각으로 변하는 게임 상황에서 수행자의 정신활동을 잘 반영하는 것으로 고려되었기 때문이다. 두 가지 게임을 수행 전반기와 후반기로 나누어 안정기와 비교하여 변화된 뇌파를 비교, 분석한 결과, 전반적으로 뇌파 게임은 수행 전반기에 비해 후반기에 slow alpha파가 증가하였고, 이와는 반대로 컴퓨터 게임은 전반기에 비해 후반기로 갈수록 fast beta파가 증가하였다. 특히 이러한 변화는 F3, Fp2, 그리고 T4 부위에서 두드러지게 관찰되었다. 이는 뇌파게임이 일반 컴퓨터 게임에 비해서 안정된 정서를 발달시킬 수 있다는 가능성을 시사한다.

• Korean Journal of Clinical Laboratory Science
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• v.49 no.3
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• pp.294-299
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• 2017

The aim of this study was to detect the status of epilepticus and seizure based on the initial patterns observed in the first 30 minutes of continuous electroencephalogram (cEEG) monitoring. An cEEG was recorded digitally using electrodes applied according to the International 10~20 System. The EEG data were reviewed from January 2014 to December 2015. The baselines of the EEG patterns were characterized by lateralized periodic discharges, generalized periodic discharges, burst suppression, focal epileptiform, asymmetric background, generalized slowing, and generalized periodic discharges with a triphagic wave. The etiology was classified into five categories. The subjects of this study were 128 patients (age: $56.9{\pm}17.5years$, male:female, 74:54). The mean cEEG monitoring duration was $5.5{\pm}5.1$ (min:max, 1:33) days. The EEG pattern categories included lateralized periodic discharges (N=7), generalized periodic discharges (N=10), burst suppression (N=6), focal epileptiform (N=19), asymmetric background (N=24), generalized slowing (N=51), and generalized periodic discharges with a triphagic wave (N=11). The etiological classifications of the patients with status epilepticus were remote symptomatic (N=4), remote symptomatic with acute precipitant (N=9), acute symptomatic (N=6), progressive encephalopathy (N=2), and febrile seizure (N=1). cEEG monitoring was found to be useful for the diagnosis of non-convulsive epileptic seizures or status epilepticus. The seizure was confirmed by the EEG pattern.

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.1
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• pp.7-12
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• 2020

Various parameters are used to measure anesthetic depth during surgery using brain waves, and in actual clinical use, the linear analysis SEF is widely used. However, with recent studies showing that biological signals including EEG, contain nonlinear properties interest in nonlinear analysis of brain signals is increasing and parameters based on these are being developed. In this study, we are going to develop a parameter that can measure EEG using the nonlinear analysis method and extract noise that can be mixed with external electronic equipment and EEG instrumentation by comparing it with the data from the bispectrum analysis of static waves.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.3 no.4
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• pp.23-27
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• 2010

This paper performs emotion classification test to find out the best parameter of electroencyphalogram(EEG) signal. Linear predictor coefficients, band cross-correlation coefficients of fast Fourier transform(FFT) and autoregressive model spectra are used as the parameters of 10-channel EEG signal. A multi-layer neural network is used as the pattern classifier. Four emotions for relaxation, joy, sadness, irritation are induced by four university students of an acting circle. Electrode positions are Fp1, Fp2, F3, F4, T3, T4, P3, P4, O1, O2. As a result, the Linear predictor coefficients showed the best performance.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.3
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• pp.607-612
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• 2019

In this paper, we measured and analyzed the EEG signals related to the relativity between musical stimuli and human concentration for memorization. In our experiments, the subjects carried out the tasks related to human memorization exposing to musical stimuli and the tasks are to memorize the english words. We used two kinds of musical stimuli, one is a sedative tendency music and the other is a stimulative tendency music. We presented the results that are analyzed as the EEG signals by frequency bands, respectively.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.1252-1254
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• 2004

BMI(brain machine interface) has been recently applied to give a disabled person mobility. This study is to determine the effective EEG parameters for predicting the movement moment of body limbs thought analysis of moving averaged ERD. The results showed that the proposed method for classifying EEG for predicting the movement seemed to be better than the classical method of determining ERD.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.147-150
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• 2008

사람과 컴퓨터의 인터페이스를 위한 방법에는 여러 가지가 있으나 보다 편리하고 몸이 불편한 사람들도 이용할 수 있도록 하기 위하여 최근에는 사람의 생체신호를 이용하여 Interface하기위한 연구가 활발히 진행되고 있다. 생체신호에는 뇌파, 근전도, 심전도, 등 여러 가지가 있지만 이를 위해 사용자의 가장 많은 정보를 내포하고 있는 뇌파에 대한 연구는 필수적이다. 따라서 세계 여러 나라에서 뇌파에 대한 연구가 진행되고 있지만 아직까지는 뇌파에 대한 정확한 분석이 이루어지지 못하고 있는 실정이다. 이를 위해 본 논문에서는 정확한 뇌파분석을 위한 뇌파 유발 자극 방법 및 측정법을 제안하고 사람이 몸을 움직이고자 하는 상상을 할 때 ERS(Event-Related Synchronization), ERD(Event-Related Desynchronization)를 분석함으로써 사람의 의도를 뇌파를 통해 분석하고자 한다.

• 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.