• 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.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.5
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• pp.605-610
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• 2008

There are many methods for Human-Computer Interface. Recently, many researchers are studying about Brain-Signal this is because not only the disabled can use a computer by their thought without their limbs but also it is convenient to general people. But, studies about it are early stages. This paper proposes an EEG signals measurement and analysis methods for Brain-Computer Interface. Our purpose of this research is recognition of subject's intention when they imagine moving their arms. EEG signals are recorded during imaginary movement of subject's arms at electrode positions Fp1, Fp2, C3, C4. We made an analysis ERS(Event-Related Synchronization) and ERD(Event-Related Desynchronization) which are detected when people move their limbs in the ${\mu}$ waves and ${\beta}$ waves. Results of this research showed that ${\mu}$ waves are decreased and ${\beta}$ waves are increased at left brain during the imaginary movement of right hand. In contrast, ${\mu}$ waves are decreased and ${\beta}$ waves are increased at right brain during the imaginary movement of left hand.

• Proceedings of the ESK Conference
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• 2004.10a
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• pp.59-59
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• 2004

• Journal of the Ergonomics Society of Korea
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• v.23 no.4
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• pp.57-66
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• 2004

EEG(Electroencephalogram) provides a possibility of communicating between a human and a computer, called BCI(brain computer interface). EEG evoked by a movement has been often used as a control command of a computer. This study is to predict human movements by EEG parameters showed significant consistency. Three undergraduate students were asked to move both hands and foots thirty times respectively. Each movement consisted of single and three consecutive movements. Their EEG signals were analyzed to obtained ERD(Event Related Desynchronization) and ERS(Event Related Synchronization). The results showed that ERD and ERS could be used as a significant classifier identifying either single movement or repetitive movement of human limbs. The number of repetition of movement could be used to various control commands of a computer.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.373-376
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• 2004

최근에 뇌파를 이용하여 컴퓨터와 통신하거나 기기를 제어할 수 있는 이른바 뇌-컴퓨터 인터페이스BCI(Brain-Computer Interface)에 대한 연구가 대두되고 있다. 이러한 BCI 연구의 궁극적 목표는 다양한 정신상태에 따른 뇌파의 특성을 파악하여 컴퓨터나 기기 등을 제어하는 것이다. 본 논문에서는 움직임과 관련 있는 10～12Hz의 mu파 영역에서의 ERD/ERS를 계산하였고, 그 결과 왼쪽과 오른쪽 손의 움직임을 상상할 때에 운동과 관련된 기능이 집중되어 있는 일차운동영역(primary motor area)의 mu파에서 ERD/ERS의 차이가 나타남을 발견하였다 또한, RLS방법을 사용한 Adaptive Autoregressive Model 계수의 특징을 추출을 하였으며, 이를 신경망으로 학습시켜 인식률을 비교하였다.

• 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.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06a
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• pp.469-472
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• 2011

신체 부위를 움직이는 상상을 할 때, 일반적으로 뇌의 감각 및 운동 피질 영역에서 특정 주파수 대역의 EEG(Electroencephalography) 신호의 세기가 감소하거나 증가하는 ERD(Event-Related Desynchronization)/ERS(Event-Related Synchronization) 현상이 발생한다. 하지만 ERD/ERS는 현상은 피험자에 의존적이고 매시도마다 큰 차이를 보인다. 이러한 문제를 해결하기 위해, 본 논문에서 각 시간-주파수 공간에 대하여 서로 다른 공간 필터를 구성하는 비 동질(non-homogeneous) 공간 필터 최적화 방법을 제안한다. EEG 신호는 시간에 대하여 비정상적(non-stationary) 특징을 가지기 때문에 제안하는 방법과 같이 시간에 따라 변화하는 ERD/ERS 특징을 반영하여 공간적 특징을 추출하는 방법은 시간에 대한 변화를 고려하지 않은 기존의 방법보다 우수한 성능을 보인다. 본 논문에서는 International BCI Competition IV에서 제공하는 4가지 동작 상상(왼손, 오른손, 발, 혀)에 대한 EEG 신호 데이터를 사용하여 동작 상상 분류 실험을 하고 이 결과를 기존의 타 방법들과 비교 분석하였다. 실험 결과, 피험자에 따라 서로 다른 시간-주파수 특징이 추출됨을 확인하였고, 최적화된 공간 필터들이 시간에 따라 변화하는 것을 확인하였다. 또한 이러한 특징을 이용하여 분류를 수행하였을 때, 더욱 우수한 분류 결과를 보임을 확인하였다.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.5
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• pp.197-206
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• 2014

Motor imagery based Brain-computer Interface(BCI), which has recently attracted attention, is the technique for decoding the user's voluntary motor intention using Electroencephalography(EEG). For classifying the motor imagery, event-related desynchronization(ERD), which is the phenomenon of EEG voltage drop at sensorimotor area in ${\mu}$-band(8-13Hz), has been generally used but this method are not free from the performance degradation of the BCI system because EEG has low spatial resolution and shows different ERD-appearing band according to users. Common spatial pattern(CSP) was proposed to solve the low spatial resolution problem but it has a disadvantage of being very sensitive to frequency-band selection. Discriminative filter bank common spatial pattern(DFBCSP) tried to solve the frequency-band selection problem by using the Fisher ratio of the averaged EEG signal power and establishing discriminative filter bank(DFB) which only includes the feature frequency-band. However, we found that DFB might not include the proper filters showing the spatial pattern of ERD. To solve this problem, we apply a band-selection process using CSP feature vectors and linear discriminant analysis to DFBCSP instead of the averaged EEG signal power. The filter selection results and the classification accuracies of the existing and the proposed methods show that the CSP feature is more effective than signal power feature.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.9
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• pp.853-859
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• 2013

The purpose of this study was to identify the correlation between electroencephalography (EEG) and strength, using grip strength. 64-channel EEG data were recorded from five healthy subjects in tasks requiring handgrip contractions of nine levels of MVC (Maximal Voluntary Contraction). We found the ERS (Event-Related Synchronization)/ERD (Event-Related Desynchronization) at the measured EEG data using STFT (Short-Time Furier Transform) and spectral power in the EEG of each frequency range displayed in the graph. In this paper, we identified that the stronger we contracted, the greater the spectral power was increased in the ${\beta}$, ${\gamma}$ wave.

• 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.