• Journal of Computing Science and Engineering
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• v.7 no.2
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• pp.132-138
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• 2013

Multi-modal techniques have received increasing interest in the neuroscientific and brain computer interface (BCI) communities in recent times. Two aspects of multi-modal imaging for BCI will be reviewed. First, the use of recordings of multiple subjects to help find subject-independent BCI classifiers is considered. Then, multi-modal neuroimaging methods involving combined electroencephalogram and near-infrared spectroscopy measurements are discussed, which can help achieve enhanced and robust BCI performance.

• Journal of KIISE:Software and Applications
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• v.27 no.1
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• pp.24-32
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• 2000

BCI(Brain-Computer Interface) is studied to control the machines with brain. In this study, an EEG(Electroencephalography) signal classification model is proposed. The model gets EEG pattern from each subject's brain and extracts characteristic features. The model discriminates the EEG patterns by using those extracted characteristic features of each subject. The proposed method classifies each pair of the given tasks and combines the results to give the final result. Four tasks such as rest, movement, mental-arithmetic calculation and point-fixing were used in the experiment. Over 90% of the trials, the model yielded successful results. The model exploits characteristic features of the subjects and the weight table that was produced after training. The analysis results of the model such as its high success rates and short processing time show that it can be used in a real-time brain-computer interface system.

• Journal of the Korea Society of Computer and Information
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• v.8 no.4
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• pp.57-62
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• 2003

At the beginning stage of design process, the concept design is required to equip the creative idea thinking and exerts critical effect on the success of production. To support the idea thinking process at the stage of concept design, web-based Creative Group Thinking System(CGTS) was developed. In this vein, the purpose of this study is to investigate the significance of HCI(Human Computer Interface) and UI(User Interface) and to find the way to increase the applicability of the UI of CGTS.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.3
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• pp.695-701
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• 2008

This study has been made to research on the brain wave characteristics of baduk expert by BCI(Brain Computer Interface). The test was based on the researches from 1th September, 2005 to 30th December, 2005, compared with the ones of the standardized general public. The number of the general public are 695 (elementary school students 423, middle and high school students 161, adults 111) and the number of the baduk players are 57 (researchstudents 15, Korean baduk club students 16, professional baduk players 26). The research data show that the baduk players have the higher indexes than the general public in Self Regulation quotient p=.002, Attention Quotient(left) p=.002, Emotion Quotient p=.027, Stress Quotient(left) p=.002 and Brain Quotient p=.006. There are some differences in brain functions between baduk players and the ordinary people. Difference in functions of the brain among baduk experts is also analyzed. That result shows that there is no different brain function between professional baduk player.

• Journal of Internet of Things and Convergence
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• v.9 no.1
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• pp.25-31
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• 2023

The brain-machine interface(BMI) is a next-generation interface that controls the device by decoding brain waves(also called Electroencephalogram, EEG), EEG is a electrical signal of nerve cell generated when the BMI user thinks of a command. The brain-machine interface can be applied to various smart devices, but complex computational process is required to decode the brain wave signal. Therefore, it is difficult to implement a brain-machine interface in an embedded system implemented in the form of an edge device. In this study, we proposed a new type of brain-machine interface system using IoT technology that only measures EEG at the edge device and stores and analyzes EEG data in the cloud computing. This system successfully performed quantitative EEG analysis for the brain-machine interface, and the whole data transmission time also showed a capable level of real-time processing.

• Proceedings of the Korea Society of Design Studies Conference
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• 2003.05a
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• pp.120-121
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• 2003

• Journal of Digital Convergence
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• v.13 no.8
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• pp.289-294
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• 2015

This paper proposes an autonomous machine learning method applicable to the BCI(Brain-Computer Interface) is based on the self-organizing Kohonen method, one of the exemplary method of unsupervised learning. In addition we propose control method of learning region and self machine learning rule using an interactive function. The learning region control and machine learning was used to control the side effects caused by interaction function that is based on the self-organizing Kohonen method. After determining the winner neuron, we decided to adjust the connection weights based on the learning rules, and learning region is gradually decreased as the number of learning is increased by the learning. So we proposed the autonomous machine learning to reach to the network equilibrium state by reducing the flow toward the input to weights of output layer neurons.

• Journal of Computing Science and Engineering
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• v.7 no.2
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• pp.139-146
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• 2013

Recent advances in computer science enabled people with severe motor disabilities to use brain-computer interfaces (BCI) for communication, control, and even to restore their motor disabilities. This paper reviews the most recent works of BCI in stroke rehabilitation with a focus on methodology that reported on data collected from stroke patients and clinical studies that reported on the motor improvements of stroke patients. Both types of studies are important as the former advances the technology of BCI for stroke, and the latter demonstrates the clinical efficacy of BCI in stroke. Finally some challenges are discussed.

• 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.

• Electronics and Telecommunications Trends
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• v.26 no.5
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• pp.123-133
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• 2011

뇌-컴퓨터 인터페이스(BCI: Brain Computer Interface)는 차세대 인터페이스의 유력한 대안으로 등장하고 있다. 특히 뇌파 연구의 증진과 뇌-컴퓨터 인터페이스 기술의 활용 확대에 힘입어 발전을 거듭하고 있다. 최근에는 Neurosky, Emotive, OCZ 등의 기업에서 헤드셋 형태의 가볍고 착용이 간편한 기기를 저렴한 가격에 발매함으로써 게임, 집중력 향상 연습 등 다양한 용도로 활용되고 있다. 본 고에서는 뇌-컴퓨터 인터페이스의 개념과 특성, 국내외 개발동향 및 적용전망을 살펴보고, 시사점 및 대응방향을 도출해보고자 한다.