• Journal of KIISE
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• 제44권9호
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• pp.887-892
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• 2017

Brain-computer interface (BCI) is a technology that controls computer and transmits intention by measuring and analyzing electroencephalogram (EEG) signals generated in multi-channel during mental work. At this time, optimal EEG channel selection is necessary not only for convenience and speed of BCI but also for improvement in accuracy. The optimal channel is obtained by removing duplicate(redundant) channels or noisy channels. This paper propose a dual filter-based channel selection method to select the optimal EEG channel. The proposed method first removes duplicate channels using Spearman's rank correlation to eliminate redundancy between channels. Then, using F score, the relevance between channels and class labels is obtained, and only the top m channels are then selected. The proposed method can provide good classification accuracy by using features obtained from channels that are associated with class labels and have no duplicates. The proposed channel selection method greatly reduces the number of channels required while improving the average classification accuracy.

• Korean Journal of Cognitive Science
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• 제21권2호
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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.

• Journal of KIISE:Software and Applications
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• 제35권3호
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• pp.169-178
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• 2008

Cancer patients have been suffered from the instability of mind/body and unbalanced homeostasis because of cancer progression and medical treatment such as chemotherapy, It is very important that appropriated actions can be promptly taken by monitoring cancer patients' mental conditions. For this reason, it is crucial to develop a monitoring method which is convenient and not harmful to their body. Brain-computer-interface(BCI) system is introduced for the purpose in this paper. Prefrontal brain waves of cancer patients and control groups have been measured by a portable neurofeedback(NF) system based on self-regulation of the human electroencephalogram(EEG). The NF system consists of the portable EEG amplifier and a headband with dry electrodes placed on Fp1 and Fp2 sites. Patterns of the prefrontal brain waves taken by computer are correlated to brain quotients by EEG-analysis program. Basic rhythm quotient, attention quotient, emotional quotient, anti-stress quotient and correlation quotient of control group have shown high significant level compared with the cancer patients group. On the other hand, the EEG patterns analysis is shown its possibility to be an important methodology of monitoring cancer patients' condition.

• Communications of the Korean Institute of Information Scientists and Engineers
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• 제29권4호
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• pp.10-18
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• 2011

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 한국정보통신학회 2015년도 추계학술대회
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• pp.924-926
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• 2015

In this paper, we use the interface, which many countries concentrates research of Brain - Computer Interface with the device and MyndPlay based on the IoT intelligent Arduino. Finally we will make the Brain - Computer Connection environment, the purpose of Brain - Computer Interface. Recognizes the EEG of a person who wearing the equipment, analyze, classify, and we did a research to design an intelligent thing to suit user's condition. In addition, we use the XBee, and Bluetooth to communicate to other devices, such as smart phone. In conclusion, this paper check users current status via brain waves, and it allows to control the power and other objects by using the EEG(Electroencephalography).

• Journal of Korea Entertainment Industry Association
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• 제13권8호
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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 IKEEE
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• 제18권3호
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• pp.368-375
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• 2014

Brain computer interface(BCI) technology has been continuously developed due to the continuous development of interface technology and the promotion of brain wave research. In this paper, we propose a driving habit management system by adopting BCI to transportation. The proposed system consists of the electroencephalogram(EEG) measuring unit, the EEG analysis unit, the memory section for storing the state information of drivers, the speed controller unit and the alarming section for generating warnings. Our proposed system can reduce the drowsy driving, improve the driving habits of users and help to prevent traffic accidents.

• Electronics and Telecommunications Trends
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• 제24권5호
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• pp.1-9
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• 2009

정부는 2017년까지 뇌 연구분야 세계 7위 진입을 목표로 연구개발 인프라 인력양성 등의 분야에 2009년에 총 610억 원을 투자한다. 인간의 뇌 정보를 활용할 수 있는 인지 메커니즘에 대한 분석이 이루어진다면, 인간과 같이 스스로 생각하고 느끼는 수준의 인공두뇌 및 인간형 로봇 개발이 가능하게 되고, 생각만으로 제어하는 뇌-컴퓨터 뇌-로봇 뇌-기계 인터페이스 기술로 사용자의 뇌파 혹은 신경 근육신호 등에 따라 알아서 움직이는 휠체어나 인공수족, 자동차, 비행기 등이 가까운 시일 내에 실현될 것으로 기대된다. 이에 인지과학의 한 분야인 증강인지(augmented cognitive)와 HCI 기술의 교집합 영역에서의 응용연구 이슈를 탐색하기 위해 인지과학의 개요, 증강인지의 출현 증강인지 시스템 모델, 프로젝트 사례 등에 대한 자료 수집 및 분석 내용을 본 고에 서술코자 한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 한국퍼지및지능시스템학회 2002년도 추계학술대회 및 정기총회
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• pp.469-472
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• 2002

사람의 뇌 속에 있는 신경 세포들은 여러 정보 처리 활동을 하면서 전기적인 신호를 발생시키는데 이를 두피 표면에서 측정한 것이 뇌파이다. 이러한 뇌파는 임상에서 주로 이용되어 왔으나 근래에는 이러한 뇌파를 이용하여 컴퓨터와 통신하거나 기기를 제어할 수 있는 이른바 BCI(Brain-Computer Interface)에 대한 연구가 대두되고 있다 BCI 연구의 궁극적 목표는 다양한 정신상태에 따른 뇌파의 특성을 파악하여 컴퓨터나 기기 등을 제어하는 것이다. 이를 위하여 본 연구에서는 좀 더 정확하고 신뢰성 있는 기기 제어를 위해 피험자의 의지대로 발생시킨 잡파를 이용하여 방향 제어 시스템을 구현하였다. 뇌파에 포함된 잡파 중 구별될 수 있는 특징을 나타내는 잡파를 선택하고 이들의 패턴을 인식하고 분류한 후 이를 제어 신호로 변환하여 방향을 제어하는 시스템을 구현하였다.

• Communications of the Korean Institute of Information Scientists and Engineers
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• 제31권7호
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• pp.26-34
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• 2013