• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.845-848
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• 2003

There are many researches on using human neuro-biological signals for various problems such as controlling a mechanical object and/or interfacing human with the computer. It is one of very interesting topics that human can use various instruments without learning specific knowledge if the instruments can be controlled as human intends. In this paper, we proposed an intelligent motion planner for a redundant manipulator, which is controlled by humans neuro-biological signals, especially, EOG (Electrooculogram). We found the optimal motion planner for the redundant manipulator that can move to the desired point. We used neural networks to find the inverse kinematics solution of the manipulator. We also showed the performance of the proposed motion planner with several simulations.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.9
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• pp.755-762
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• 2004

In this research, EOG(Electrooculogram) signal was analyzed to predict the subject's intention using a fuzzy classifier. The fuzzy classifier is built automatically using the EOG data and evolutionary algorithms. An assistant robot manipulator in redundant configuration has been developed, which operates according to the EOG signal classification results. For automatic fuzzy model construction without any experts' knowledge, an evolutionary algorithm with the new representation scheme, design of adequate fitness function and evolutionary operators, is proposed. The proposed evolutionary algorithm can optimize the number of fuzzy rules, the number of fuzzy membership functions, parameter values for the each membership functions, and parameter values for the consequent parts. It is shown that the fuzzy classifier built by the proposed algorithm can classify the EOG data efficiently. Intelligent motion planner that consists of several neural networks are used for control of robot manipulator based upon EOG classification results.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.408-410
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• 2012

기존의 비디오 검색 방법들은 관련 주석이나 영상 정보에 기반하며 사용자의 반응과 관련하여서는 많은 정보를 활용하고 있지 않다. 비디오 시청시 사용자의 뇌신호나 시선추적 정보 등의 인지적 반응을 이용하여 연속적인 비디오 스트림의 각 부분에 대하여 사용자들이 나타내는 관심이나 감성 정보를 추출한다면 보다 인터랙티브한 비디오 데이터 검색 및 추천이 가능하다. 본 논문에서는 비디오를 시청하는 사용자의 안구전도(electrooculogram)를 기록한 후, 장면 전환이 발생한 부분에서의 사건관련전위 분석을 통해 해당 부분에서 나타나는 특징적 반응을 찾고 이에 대한 인지적 해석을 도출했다. 실험 결과 장면 전환 이후200~700ms 부분에서 P300 성분과 유사한 피크가 발생하는 것을 확인하였으며, 이러한 결과는 장면 전환에 따른 피험자의 비디오 내용 인지에 대한 의도 불일치 및 주의력 증가로 해석된다.

• Journal of Korea Entertainment Industry Association
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• v.13 no.8
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• pp.525-530
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• 2019

Electroencephalography(EEG) studies that have been in clinical research since the discovery of brainwave have recently been developed into brain-computer interface studies. Currently, research is underway to manipulate robot arms and drones by analyzing brainwave. However, resolution and reliability of EEG information is still limited. Therefore, it is required to develop various technologies necessary for measuring and interpreting brainwave more accurately. Pioneering new applications with these technologies is also important. In this paper, we propose development of a personal authentication system fit for lifecaretainment based on EEG. The proposed system guarantees the resolution and reliability of EEG information by using the Electrooculogram and Electromyogram(EMG) together with EEG.

• Journal of the Korea Society of Computer and Information
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• v.11 no.6 s.44
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• pp.133-141
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• 2006

Recently many wearable computers have been developed. But they still have many user interface problems from both an input and output perspective. This paper presents a wearable user interface based on EOG(electrooculogram) sensing circuit and marker recognition. In the proposed user interface, the EOG sensor circuit which tracks the movement of eyes by sensing the potential difference across the eye is used as a pointing device. Objects to manipulate are represented human readable markers. And the marker recognition system detects and recognize markers from the camera input image. When a marker is recognized, the corresponding property window and method window are displayed to the head mounted display. Users manipulate the object by selecting a property or a method item from the window. By using the EOG sensor circuit and the marker recognition system, we can manipulate an object with only eye movement in the wearable computing environment.

• Journal of the Korean Academy of Clinical Electrophysiology
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• v.8 no.1
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• pp.43-50
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• 2010

Purpose : The objective of this study is to effect of an aquatic rotation control and obstacle avoidance when conducted underwater on hemiplegia patient's balance ability and vestibular function. Methods : Twelve hemiplegia patients participated and were randomly assigned to a control group(I) with standard physical therapy and an aquatic group(II) with an aquatic rotation control, obstacle avoidance and standard physical therapy as well. The aquatic group trained using a Halliwick rotation control and obstacle avoidance through 3 times per week over 6 weeks. For all subjects, vestibular function, their balance, the change of electrooculogram (EOG), the change of accelerometer axis and torsiometer according to visual sense, vestibular sense with galvanic vestibular stimulation (GVS) or not during leg close stance were measured. Results : The EOG in the vertical and horizontal (p<0.05) were both significantly lowered. The change was significantly lower in the trajectory range of motion of trunk and spine with torsiometer when leg close stand (p<0.01) and leg close stand with GVS (p<0.01). The centre of gravity accelerated, there were reduced significantly difference X and Y axis of accelerometer during the closing of the leg without vision (p<0.05). There were reduced significantly difference X and Z axis of accelerometer during the closing of the leg with GVS (p<0.05). There were reduced significantly difference X and Z axis of accelerometer during the closing of the leg and close eyes with GVS (p<0.05). Conclusion : The balance ability, vestibular system and postural control is improved.

• 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.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.768-773
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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 drowsiness detection system using Linear Predictive Coding (LPC) coefficients and Support Vector Machine (SVM). Samples of EEG data from each predefined state were used to train the SVM program by using the proposed feature extraction algorithms. The trained SVM 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.

• Journal of the Korea Convergence Society
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• v.9 no.11
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• pp.53-60
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• 2018

This paper describes the experiments of gaze tracking utilizing a low-cost electrooculogram measuring device. The goal of the experiments is to verify whether the low-cost device can be used for a complicated human-computer interaction tool, such as the eye-writing. Two experiments are conducted for this goal: a simple gaze tracking of four directional eye-movements, and eye-writing-which is to draw letters or shapes in a virtual space. Eye-written alphabets were obtained by two PSL-iEOGs and an Arduino Uno; they were classified by dynamic positional warping after preprocessed by a wavelet function. The results show that the expected recognition accuracy of the four-directional recognition is close to 90% when noises are controlled, and the similar median accuracy (90.00%) was achieved for the eye-writing when the number of writing patterns are limited to five. In future works, additional algorithms for stabilizing the signal need to be developed.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1981-1982
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• 2008

뇌파신호 측정 시에는 환자의 움직임 등으로 artifact가 발생하게 된다. 따라서 정확한 진단에는 이와 같은 artifact를 제거하는 것이 중요하다. 본 논문에서는 뇌파신호에서 발생할 수 있는 artifact 중 EOG(Electrooculogram: 안전위도)를 검출하고 제거하기 위한 방법으로 EOG 필터링(EOG filtering)을 제안하며, 나머지 근전도를 제거하기 위해 신경망(neural network)를 사용한다. 이때 입력신호의 특징이 신경망에 보다 잘 적용될 수 있도록 비선형 양자화기를 적응적으로 동작시키는 방법을 제안한다. 제안하는 방법을 통해 뇌파신호의 artifact를 효과적으로 제거할 수 있다.