• Science of Emotion and Sensibility
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• v.6 no.4
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• pp.15-23
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• 2003

Recently, there have been many attempts to develop BCI (brain computer interface) based on EEG (electroencephalogram). Measurement and analysis of EEG evoked by particular stimulation is important for the design of brain wave pattern and interface of BCI. The purpose of this study is to develop a general-purpose system that measures subject's reaction time after audio-visual stimulation which can work together with any other biosignal measurement systems. The entire system is divided into four modules, which are stimulation signal generation, reaction time measurement, evoked potential measurement and synchronization. Stimulation signal generation module was implemented by means of Flash. Measurement of the reaction time (the period between the answer request and the subject reaction) was achieved by self-made microcontroller system. EEG measurement was performed using the ready-made hardware and software without any modification. Synchronization of all modules was achieved by, first, the black-and-white signals on the stimulation screen synchronized with the problem presentation and the answer request, second, the photodetectors sensing the signals. The proposed method offers easy design of purpose-specific system only by adding simple modules (reaction time measurement, synchronization) to the ready-made stimulation and EEG system, and therefore, it is expected to accelerate the researches requiring the measurement of evoked response and reaction time.

• Korean Journal of Cognitive Science
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• v.21 no.2
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• pp.309-338
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• 2010

In this paper, we studied the brain-computer interface (BCI). BCIs help severely disabled people to control external devices by analyzing their brain signals evoked from motor imageries. The findings in the field of neurophysiology revealed that the power of $\beta$(14-26 Hz) and $\mu$(8-12 Hz) rhythms decreases or increases in synchrony of the underlying neuronal populations in the sensorymotor cortex when people imagine the movement of their body parts. These are called Event-Related Desynchronization / Synchronization (ERD/ERS), respectively. We implemented a BCI-based mouse interface system which enabled subjects to control a computer mouse cursor into four different directions (e.g., up, down, left, and right) by analyzing brain signal patterns online. Tongue, foot, left-hand, and right-hand motor imageries were utilized to stimulate a human brain. We used a non-invasive EEG which records brain's spontaneous electrical activity over a short period of time by placing electrodes on the scalp. Because of the nature of the EEG signals, i.e., low amplitude and vulnerability to artifacts and noise, it is hard to analyze and classify brain signals measured by EEG directly. In order to overcome these obstacles, we applied statistical machine-learning techniques. We could achieve high performance in the classification of four motor imageries by employing Common Spatial Pattern (CSP) and Linear Discriminant Analysis (LDA) which transformed input EEG signals into a new coordinate system making the variances among different motor imagery signals maximized for easy classification. From the inspection of the topographies of the results, we could also confirm ERD/ERS appeared at different brain areas for different motor imageries showing the correspondence with the anatomical and neurophysiological knowledge.

• Proceedings of the KAIS Fall Conference
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• 2009.05a
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• pp.162-165
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• 2009

본 논문에서는 뇌-컴퓨터 인터페이스(Brain-Computer Interface) 기술을 중 움직임과 관련된 EEG(Electroencephalograph)신호를 이용하여 한국어를 생성하기 위한 시스템 설계 방법을 제안한다. 뇌-컴퓨터 인터페이스의 정보변환율(Information Transfer Rate)향상을 위하여 바이오피드백 방법과 기계학습 방법을 동시에 적용시킬 수 있는 방법과 움직임 관련 SMR(Sensorimotor Rhythm)과 한국어 음절, 어절 예측을 기술을 사용하여 ALS환자 혹은 운동능력이 없는 사람들을 위한 한국어 생성을 위한 설계 방법에 대해서 연구하였다.

• Journal of Information Technology Applications and Management
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• v.9 no.4
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• pp.165-177
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• 2002

For measuring EEG with the international 10-20 electrode system on 16 channels, and to analyze the interrelationship between the original signals and the changed signals after the stimulation, we use the scent of lavender which stimulates the olfactory sense. Moreover, the effect of the scent stimulation to the brain is analyzed. The purpose of this analysis is to apply these results to the computerized mapping of the brain signals and to find possible ways of specifying the source of the brain signals through various medical applications.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.6
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• pp.832-837
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• 2007

Many researchers are studying brain activity to using functional Magnetic Resonance Imaging (fMRI), Time Resolved Spectroscopy(TRS), Electroencephalography(EEG), and etc. They are used detection of seizures or epilepsy and deception detection in the main. In this paper, we focus on emotion recognition by recording brain waves. We specially use fMRI, TRS, and EEG for measuring brain activity Researchers are experimenting brain waves to get only a measuring apparatus or to use both fMRI and EEG. This paper is measured that we take images of fMRI and TRS about brain activity as human emotions and then we take data of EEG signals. Especially, we focus on EEG signals analysis. We analyze not only original features in brain waves but also transferred features to classify into five sections as frequency. And we eliminate low frequency from 0.2 to 4Hz for EEG artifacts elimination.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.311-313
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• 1992

Automated analysis of EEG invariably requires the inclusion of a signal conditioning filter. This paper investigates the EEG waveform distortions caused by the transient responses of the various types of signal conditioning filters. This study explicitly simulates the filter responses to the typical EEG waveform models, and compares the distortions.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2000.11a
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• pp.409-412
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• 2000

A high performance EEG signal measurement system is fabricated. It consists of high precision pre-amplifier and auto identification bandwidth unit. High precision pre-amplifier is composed of signal generator, signal amplifier with a impedance converter, body driver and isolation amplifier. The pre-amplifier is designed for low noise characteristics, high CMRR, high input impedance, high IMRR and safety, Auto identification bandwidth unit is composed of AD-converter and PIC micro-controller for real time processing EEG signal. The performance of EEG signal measurement system has been shown the classified bandwidth through the clinical demonstrations.

• Proceedings of the KOSOMBE Conference
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• v.1994 no.05
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• pp.69-71
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• 1994

1Hz에서 20Hz까지의 단속 주파수를 지닌 청각자극을 가해 얻은 EEG신호에서 자극에 따른 신호의 정성적이고 정량적인 특성을 카오스 분석방법을 통해 밝혔다. 먼저, 뇌전위 신호에 전반적으로 나타나는 일반적인 카오스 특징(fractal mechanism, 1/f frequency spectrum, positive lyapunov exponent등등)이 확인되어졌다. 유발전위에 대해서는 자극의 주파수에 따른 주기 배증을 경유한 카오스로 가는 길(route to chaos)과 2차원 pseudo-phase portrait의 뿌앙까레 단면에서의 기하학적 모양(topological property)의 변화가 관찰되어졌고, 자발전위가 포함된 유발전위에 대해서는 적절한 bases를 지닌 3차원 phase space에서 기이한 끌개(chaotic attractor)가, 유발전위의 정보를 지닌채 보여졌다. 끝으로 자극 주파수(단속 주파수와 반송 주파수) 변화와 측정이 이루어진 머리표면에서의 공간적 위치에 따른, lyapunov exponent값 변화가 의미있게 해석되어졌다. 이 결과는 무질서하게 보이는 뇌전위신호에서 주어진 청각자극에 대한 정보를 얻는 새로운 방법을 제시하게 된다.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.343-344
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• 2007

본 논문은 의사표현이 어려운 전신마비환자의 뇌파(EEG)를 이용하여 긍정과 부정을 표현할 수 있는 방법에 대해서 소개한다. 더 나아가 인간의 감정에 따라 긍정과 부정을 민감하게 반응하는 뇌 영역을 분석하였다. 해당영역의 뇌파(EEG)변화를 측정하기 위해 컴퓨터 시스템과 접목시키는 목적도 포함하고 있다. 이를 위해서 미약한 뇌파를 증폭 시키는 전치 증폭기를 구현하였고 인공산물과 뇌파 주파수영역만을 통과시키는 아날로그 전자회로를 구현하였다. 또한 인간의 두뇌피질로부터 측정된 신호는 컴퓨터 시스템에 전송된다. 수신된 신호를 실시간 Fast Fourier Transform(FFT) 신호처리과정을 거쳐 뇌파의 주파수 영역을 분류하게 된다. 이때 분류된 뇌파를 바탕으로 인간의 긍정과 부정을 표현할 수 있는 방법을 제시한다.

• The Journal of the Korea Contents Association
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• v.7 no.8
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• pp.41-49
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• 2007

BCI system integrates control or telecommunication system with generating electric signals in scalp itself after signal acquisition. This system detect a movement of EEG at real time, can control an electron equipment using a generated signal through EEG movement or software-based processor. In this paper, we deal with removing and separating artifacts induceced from measurement when brain-computer interface system that analyzes recognizes EEG signals occurred from various mental states. In this paper, we proposed a method of EEG classification and an artifact interval detection using bisection mathematical modeling in the EEG classification process for BCI system implementation.