• Journal of Korea Game Society
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• v.13 no.6
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• pp.15-26
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• 2013

In recent years, studies in BCI game with popular BCI devices are progressing actively by the development of BCI(Brain Computer Interface) techniques. Most of BCI games have developed as experimental contents for researching. On the game control paradigm, it is insufficient to conduct a study about induced methods of proper barinwave to control the BCI game. In this study, we suggest a rhythm game using BCI which has a new play element that visualizes the rhythm of music and represents the notes of music in sound and a generation method of visual-auditory feedback through the synchronization of the tempo of music with brainwave. Experimental Results make certain that our suggestion is possible for the improvement of game score through the induction of brainwave that is necessary to control the game.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.515-520
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• 2012

In brain computer interface (BCI) system, the most important part is classification of human thoughts in order to translate into commands. The more accuracy result in classification the system gets, the more effective BCI system is. To increase the quality of BCI system, we proposed to reduce noise and artifact from the recording data to analyzing data. We used auditory stimuli instead of visual ones to eliminate the eye movement, unwanted visual activation, gaze control. We applied independent component analysis (ICA) algorithm to purify the sources which constructed the raw signals. One of the most famous spatial filter in BCI context is common spatial patterns (CSP), which maximize one class while minimize the other by using covariance matrix. ICA and CSP also do the filter job, as a raw filter and refinement, which increase the classification result of linear discriminant analysis (LDA).

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.2083-2085
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• 2009

Brain Computer Interface (BCI) is a communication pathway between devices (computers) and human brain. It treats brain signals in real-time basis and discriminates some information of what human brain is doing. In this work, we develop a EEG BCI system using a feature extraction such as common spatial pattern (CSP) and a classifier using Fisher linear discriminant analysis (FLDA). Two-class EEG motor imagery movement datasets with both cued and uncued are tested to verify its feasibility.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.9
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• pp.838-842
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• 2007

A brain-computer interface(BCI) system is a communication channel which transforms a subject's thought process into command signals to control various devices. These systems use electroencephalographic signals or the neuronal activity of many single neurons. The presented study deals with an efficient analysis method of neuronal signals from a BCI System using an independent component analysis(ICA) algorithm. The BCI system was implemented to generate event signals coding movement information of the subject. To apply the ICA algorithm, we obtained the perievent histograms of neuronal signals recorded from prefrontal cortex(PFC) region during target-to-goal(TG) task trials in the BCI system. The neuronal signals were then smoothed over 5ms intervals by low-pass filtering. The matrix of smoothed signals was then rearranged such that each signal was represented as a column and each bin as a row. Each column was also normalized to have a unit variance. As a result, we verified that different patterns of the neuronal signals are dependent on the target position and predefined event signals.

• Journal of the Korean Society of Physical Medicine
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• v.13 no.4
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• pp.59-65
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• 2018

PURPOSE: The purpose of the present study is to investigate the changes in the cognitive function of elderly dementia patients residing in a residential care facility, following six weeks of brain-computer interface (BCI)-based cognitive training and to determine whether BCI-based cognitive training effectively improves their cognitive functions. METHODS: Thirty subjects diagnosed with dementia were randomly assigned to either the experimental or control group. Pre- and post-test cognitive function assessments were conducted using the mini mental state examination-Korean (MMSE-K) and Korean-dementia rating scale (K-DRS). The experimental group received BCI-based cognitive training, which consisted of games such as flying a ball and exploding a bomb, while the control group participated in music listening activities and National Health Gymnastics. Both groups engaged in a total of 18 sessions (3 times per week for 6 weeks, for 40 minutes per session). RESULTS: After 6 weeks of intervention, the experimental group had significantly increased MMSE-K scores ($19.53{\pm}1.30$ to $22.20{\pm}1.15$; p<.0011) and total K-DRS scores ($87.20{\pm}4.16$ to $99.33{\pm}1.15$; p<.0011). In addition, the experimental group showed greater cognitive improvements than the control group. CONCLUSION: The results of this study suggest that BCI-based cognitive training is a positive intervention tool for improving the cognitive function of dementia patients.

• 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.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.11
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• pp.265-272
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• 2020

In the research of brain computer interface (BCI) technology, one of the big problems encountered is how to deal with some people as called the BCI-illiteracy group who could not control the BCI system. To approach this problem efficiently, we investigated a kind of spectral EEG characteristics in the prior resting state in association with BCI performance in the following BCI tasks. First, spectral powers of EEG signals in the resting state with both eyes-open and eyes-closed conditions were respectively extracted. Second, a convolution neural network (CNN) based binary classifier discriminated the binary motor imagery intention in the BCI task. Both the linear correlation and binary prediction methods confirmed that the spectral EEG characteristics in the prior resting state were highly related to the BCI performance in the following BCI task. Linear regression analysis demonstrated that the relative ratio of the 13 Hz below and above the spectral power in the resting state with only eyes-open, not eyes-closed condition, were significantly correlated with the quantified metrics of the BCI performance (r=0.544). A binary classifier based on the linear regression with L1 regularization method was able to discriminate the high-performance group and low-performance group in the following BCI task by using the spectral-based EEG features in the precedent resting state (AUC=0.817). These results strongly support that the spectral EEG characteristics in the frontal regions during the resting state with eyes-open condition should be used as a good predictor of the following BCI task performance.

• 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.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.4
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• pp.227-236
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• 2007

Ubiquitous systems have grown explosively over the few years. Nowadays users' needs for high qualify service lead a various type of user terminals. One of various type of user interface, various types of effective human computer interface methods have been developed. In many researches, researchers have focused on using brain-wave interface, that is to say, BCI. Nowadays, researches which are related to BCI are under way to find out effective methods. But, most researches which are related to BCI are not centralized and not systematic. These problems brought about ineffective results of researches. In most researches related in HCI, that is to say - pattern recognition, the most important foundation of the research is to build correct and sufficient DB. But there is no effective and reliable standard research conditions when researchers are gathering brain-wave in BCI. Subjects as well as researchers do not know effective methods for gathering DB. Researchers do not know how to instruct subjects and subjects also do not know how to follow researchers' instruction. To solve these kinds of problems, we propose effective brain-wave DB building system using the five senses stimulation. Researcher instructs the subject to use the five senses. Subjects imagine the instructed senses. It is also possible for researchers to distinguish whether brain-wave is right or not. In real time, researches verify gathered brain-wane data using spectrogram. To verify effectiveness of our proposed system, we analyze the spectrogram of gathered brain-wave DB and pattern. On the basis of spectrogram and pattern analysis, we propose an effective brain-wave DB building method using the five senses stimulation.

• Journal of Korea Multimedia Society
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• v.14 no.9
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• pp.1202-1209
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• 2011

Neuro feedback training using ElectroEncephalo Grams (EEGs) is commonly utilized in the treatment of Alzheimer's disease, and Attention Deficit Hyperactivity Disorder (ADHD). Recently, BCI (Brain-computer Interface) contents have developed, not for the purpose of treatment, but for concentration improvement or brain relaxation training. However, as each user has different wave forms, it is hard to develop contents controlled by such different wave. Therefore, an EEG process that allows the ability to transform the variety of wave forms into one standard signal and use it without taking a user's characteristic of EEG into account, is required. In this paper, a framework that can reduce users' characteristics by normalizing and converting measured EEGs is proposed for contents. This framework also contains the process that controls different brainwave measuring devices. In experiment a handling process applying the proposed framework to the developed BCI contents is introduced.