• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.829-833
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• 1992

This paper presents the design of an automated EEG analyzing system. The design considerations including processing speed, A/D conversion, filtering, and waveforms detection, are overviewed with the description of the associated EEG characteristics. The architecture of the currently implemented system consists of a p-controller based front-end signal processing unit and a host computer system. The data acquisition procedures are described along with a couple of illustrations of the acquired EEG/EOG signal.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2000년도 추계학술대회 논문집
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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.

• 융합신호처리학회논문지
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• 제8권3호
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• pp.178-184
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• 2007

일반적으로 EEG 신호는 Alpha파, Beta파, Theta파, Delta파로 구분할 수 있다. Alpha파는 사람에게 있어서 가장 우세한 파형으로써 정신적으로 안정 시 잘 나타나는 뇌파이며, Beta파는 흥분 시 우세하게 나타난다. 본 연구에서는 EEG의 안정 상태를 정량적으로 나타내기 위해 웨이브렛 변환과 파워 스펙트럼 분석을 이용하였다. EEG신호를 웨이브렛 변환을 통해 Alpha파와 Beta파만 검출하여 고속 푸리에 변환을 이용 Alpha파와 Beta파의 파워 스펙트럼을 구하였다. 이후 Beta파의 파워 스펙트럼에 대한 Alpha파의 파워 스펙트럼 비율로 정의되는 상대적 안정상태비(Stable State Ratio)를 계산하였다. 그 결과 피험자가 정상적인 활동 상태에서 정신적으로 편안한 안정 상태에 이르기까지 5분 이내가 16%, $5{\sim}10$분 사이가 9%, 그리고 최소 10분 이상의 시간이 소요되는 피험자집단이 총 69%로 우세하게 나타났다.

• 전기학회논문지
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• 제60권6호
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• pp.1190-1195
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• 2011

This paper presents a waist pressure evaluation method in human sensibility using a electroencephalogram(EEG) signal. For this objective, a size-controllable waist-belt is used. First of all, EEG signals for relaxed state are acquired. Then, the waist-belt of the subject is tightened about 90% of normal state. After a few minutes, the belt of the subject is released. Some necessary preprocessing is performed on the acquired signals, Linear Prediction (LP) coefficients are utilized as the feature parameters extracting the characteristics of EEG signal, and a multi-layer neural network is used for indicating the state of body pressure. The results of the method showed 77.2% of coincidence with body pressure states. This may be compromising results for ssubject-independent sensibility evaluation using EEG signal.

• 센서학회지
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• 제13권3호
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• pp.236-243
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• 2004

In this paper, we proposed a brain-computer interface system using EEG signals. It can generate 4 direction command signal from EEG signals captured during imagination of subjects. Bandpass filter used for preprocessing to detect the brain signal, and the power spectrum at a specific frequency domain of the EEG signals for concentration status and non-concentration one is used for feature. In order to generate an adequate signal for controlling the 4 direction movement, we propose a new interface system implemented by using a support vector machine and a time-multiplexing method. Moreover, bio-feed back process and on-line adaptive pattern recognition mechanism are also considered in the proposed system. Computer experimental results show that the proposed method is effective to recognize the non-stational brain wave signal.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1990년도 추계학술대회
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• pp.114-117
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• 1990

EEG signal is analyzed by two methods, analysis by visual inspection of EEG recording sheets and analysis by quantative method. Generally visual inspection method is used in the clinical field. But this method has its limitation because EEG signal is random signal. Therefore it is necessary to analyze EEG signals quantatively to obtain more precise and objective information of neural and brain. In this paper, power spectrum of EEG signal was estimated by AR(AutoRegressive) model in the frequency domain. This process is useful as a preprocessing stage for tomographic brain mapping (TBM) at each frequency, band. As a method for estimating power spectral density of EEG signals, periodogram method, autocorrelation method. covariance method, modified covariance method, and Burg method are tested in this paper.

• 대한임베디드공학회논문지
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• 제18권2호
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• pp.81-87
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• 2023

With the development of BCI devices, it is now possible to use EEG control technology to move the robot's arms or legs to help with daily life. In this paper, we propose a customized vehicle control model based on BCI. This is a model that collects BCI-based driver EEG signals, determines information according to EEG signal analysis, and then controls the direction of the vehicle based on the determinated information through EEG signal analysis. In this case, in the process of analyzing noisy EEG signals, controlling direction is supplemented by using a camera-based eye tracking method to increase the accuracy of recognized direction . By synthesizing the EEG signal that recognized the direction to be controlled and the result of eye tracking, the vehicle was controlled in five directions: left turn, right turn, forward, backward, and stop. In experimental result, the accuracy of direction recognition of our proposed model is about 75% or higher.

• 대한전자공학회논문지SP
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• 제41권3호
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• pp.221-229
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• 2004

The Continuous Wavelets Transform project signal f(t) to "Time-scale"plan utilizing the time varied function which called "wavelets". This Transformation permit to analyze scale time dependence of signal f(t) thus the local or global scale properties can be extracted. Moreover, the signal f(t) can be reconstructed stably by utilizing the Inverse Continuous Wavelets Transform. In this paper, the EEG signal is analyzed by wavelets coherence method and the De-noising procedure is represented.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1994년도 추계학술대회
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• pp.81-84
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• 1994

A clinically oriented EEG and EP mapping system was developed with user-friendly interface and easy interactive operations. The system was based on the graphical user interface developed with C/C++ and Software Development Kit (SDK) operated under Microsoft Windows 3.1. Continuous acquisition for the EEG signal and burst mode acquisition for EEG signal syncronized to the external stimuli arc implemented with real time display. A neural network based automatic artifact discrimation is developed and implemented with which examination time can be reduced by a factor of 3 or more. Several bands of spectral maps and spectrums arc displayed for EEG diagnosis. Amplitude maps of EP signal at specified times by operator are displayed together with cine mode of EP maps for dynamic study. Source localization and other statistical signal processing are also included.

• 센서학회지
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• 제25권4호
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• pp.257-263
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• 2016

Among several technologies related to human brain, Brain Computer Interface (BCI) system is one of the most notable technologies recently. Conventional BCI for direct communication between human brain and machine are discomfort because normally electroencephalograghy(EEG) signal is measured by using multichannel EEG sensor. In this study, we propose 2-channel EEG sensor-based application control system which is more convenience and low complexity to wear to get EEG signal. EEG sensor module and system algorithm used in this study are developed and designed and one of the BCI methods, Motor Imagery (MI) is implemented in the system. Experiments are consisted of accuracy measurement of MI classification and driving control test. The results show that our simple wearable system has comparable performance with studies using multi-channel EEG sensor-based system, even better performance than other studies.