• 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 Digital Convergence
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• v.13 no.6
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• pp.177-184
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• 2015

Brain-computer interface is (BCI) is a communication device that the brain activity is directly input to the computer without input devices, such as a mouse or keyboard. As the brain wave interface hardware technology evolves, expensive and large EEG equipment has been downsized cheaply. So it will be applied to various multimedia applications. Among BCI studies, we suggest the domestic and foreign research trend about how the BCI is applied about the game almost people use. Next, look at the problems of the game with the BCI, we would like to propose the future direction of domestic BMI research and development.

• 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.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.1005-1010
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• 2006

인체에서 발생하는 생체신호 중에서 뇌파는 신호가 복잡하고 재현이 어려움에도 불구하고 BCI(Brain Computer Interface) 분야에서는 선진국 선두 그룹을 중심으로 획기적인 기술을 개발하고 있다. 또한 BCI 에 대한 개발의 필요성도 손발을 사용하지 못하는 중증 장애인을 중심으로 확대되고 있다. BCI2000 시스템은 이러한 노력으로 탄생하였으며 BCI 선두 그룹을 중심으로 개발 발전되고 있다. 이 시스템 내부에서는 순수 상상에 의한 방향 인식과 가상키보드 등의 작업이 가능하도록 수정 보완 작업이 계속되고 있으며 정기적인 모임을 통해 그 기술을 공유하고 있다. BCI 에서의 선진그룹과 국내 연구 결과에는 많은 기술적 차이가 있지만 본 연구에서는 BCI 에서의 기술 발전에 자극되어 좌우 방향의 이벤트에 대한 뇌파 신호 분석과 이를 통하여 모니터 상의 방향을 제어하는 실험을 실시하였고 그 방법과 결과를 논의한다.

• Korean Journal of Materials Research
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• v.10 no.3
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• pp.179-183
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• 2000

The reactive ion etching process of GaN using $BCI_3/H_2/Ar$ high density inductively coupled plasma was investigated. Results showed that both of the etch rate and the sidewall verticality significantly increased as the ICP power, bias voltage, and the $BCI_3$ ratio were increased whereas effects of the other variables were minimal. The maximum etch rate of about 175nm/min was obtained at the condition of ICP power 900W, bias voltage 400V, 4mTorr, and 60% $BCI_3$, which resulted in reasonably smooth etched surface. Etch residues were observed in the case of samples etched at the low bias conditions, which were proposed to be as the $GaCI_x$ compounds.

• Journal of the Institute of Convergence Signal Processing
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• v.10 no.1
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• pp.40-48
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• 2009

Leading of the computer, IT technology has make great strides. As a information-industry-community was highly developed, user's needs to convenience about intelligence and humanization of interface is being increase today. Nowadays, researches with are related to BCI are progress put the application-technology development first in importance eliminating research about fountainhead technology with DB construction. These problems are due to a BCI-related research studies have not overcome the initial level, and not toward a systematic study. Brain wave are collected from subjects is a signal that the signal is appropriate and necessary in the experiment is difficult to distinguish. In addition, brain wave that it's not necessary to collect the experiment, serious eyes flicker, facial and body movements of an EMG and electrodes attached to the state, noise, vibration, etc. It is hard to collect accurate brain wave was caused by mixing disturbance wave in experiment on the environment. This movement, and the experiment of subject impact on the environment due to the mixing disturbance wave can cause that lowering cognitive and decline of efficiency when embodied BCI system. Therefore, in this paper, we propose an accurate and efficient brain-wave DB building system that more exactness and cognitive basis studies when embodied BCI system with brain-wave. For the minimize about brain wave DB with mixing disturbance, we propose a DB building method using an automatic control and prevent unnecessary action, put to use the subjects face tracking.

• Journal of the Institute of Convergence Signal Processing
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• v.19 no.1
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• pp.7-13
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• 2018

In the current study, we proposed an optimized brain-computer interface (BCI) which employed blind source separation (BBS) approach to remove noises. Thus motor imagery (MI) signal and steady state visual evoked potential (SSVEP) signal were easily to be detected due to enhancement in signal-to-noise ratio (SNR). Moreover, a combination between MI and SSVEP which is typically can increase the number of commands being generated in the current BCI. To reduce the computational time as well as to bring the BCI closer to real-world applications, the current system utilizes a single-channel EEG signal. In addition, a convolutional neural network (CNN) was used as the multi-class classification model. We evaluated the performance in term of accuracy between a non-BBS+BCI and BBS+BCI. Results show that the accuracy of the BBS+BCI is achieved $16.15{\pm}5.12%$ higher than that in the non-BBS+BCI by using BBS than non-used on. Overall, the proposed BCI system demonstrate a feasibility to be applied for multi-dimensional control applications with a comparable accuracy.

• 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 the Institute of Convergence Signal Processing
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• v.25 no.1
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• pp.7-14
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• 2024

This study conducted a comparative experiment on the continuity of time series data and the classification performance of artificial intelligence models. In BCI research using EEG signals, the performance of behavior and thought classification improved as the continuity of the data decreased. In particular, LSTM achieved a high performance of 0.8728 on data with low continuity, and DNN showed a performance of 0.9178 when continuity was not considered. This suggests that data without continuity may perform better. Additionally, data without continuity showed better performance in task classification. These results suggest that BCI research based on EEG signals can perform better by showing various data characteristics through shuffling rather than considering data continuity.

• Journal of Korea Game Society
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• v.9 no.6
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• pp.31-44
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• 2009

BCI (Brain-Computer Interface) is a way to control computer by using the human brain waves. As the hardware using brain wave technologies has developed, former expensive and big sized brain wave measuring devices have recently become much smaller and cheaper in their prices, making it possible for the individuals to buy them. This predicts them to be applied in various fields of multimedia industry. This thesis is to give an insight into whether the BCI device can be used as a new gaming device approaching it in a game designing point of view. At first, we proposed game play elements that can effectively utilizing the BCI devices, systematize, and produced a game prototype adopting the BCI device based on such game play elements. Next, we verified it with statistical data analysis to show that the prototype adopting the BCI device gives more clear and efficient controls in its game play than a game of only adopting keyboard & mouse devices and analysis verified that BCI-based game play elements provide users with a more intuitive and interesting experience than traditional non-BcI-based game play elements. The results will give a guideline for effective game design methodology for making BCI based games.