• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2000.04a
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• pp.91-96
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• 2000

본 연구는 이러한 뇌파 바이오피드백 원리를 적용하여 뇌파를 유도하고 유도된 상태에 대한 피드백을 제공함으로써 사용자 스스로 자신의 뇌파 상태를 조절하도록 하는 장치로 개발된 Q0jump 시스템의 효과성을 밝히고자 하였다. 연구 방법에 있어서 뇌파 측정 장비와 Q-jump를 동시에 측정함으로써 Q-jump 시스템의 타당성을 검증하였으며, 또한 뇌파유도 프로그램을 사용하여 그 효과성을 입증하려 했다. 그 결과 Q-jump 시스템과 뇌파 측정장비에서 얻어진 결과가 유의한 상관을 가짐을 보았다. 상대적 출현량에 있어서 뇌파 유도프로그램을 사용했을 때, 전두엽 부분에서 slow alpha 는 유의한 증가를, fast beta는 유의한 감소를 나타냈다. 후속 연구에 있어서는 통제집단을 이용한 보다 면밀한 검증 연구가 필요하다.

• Review of KIISC
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• v.27 no.1
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• pp.39-46
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• 2017

본 논문에서는 생체인식 인증의 한 가지 방법인 뇌파 기반 사용자 인증기술의 최신 기술동향에 대해 고찰하고 해당기술의 실용화를 위해 해결해야 할 기술적 문제점과 요구사항에 대해 분석한다. 뇌파 기반 사용자 인증기술은 최근에 스마트폰, 금융 등 다양한 분야에서 사용되고 있는 기존의 생체인식 인증기술과 비교해볼 때 가변성, 유출 저항성 등의 장점이 있지만, 사용자들로부터 뇌파를 수집하기 위해 필요한 장비의 경제성, 뇌파 수집 행위의 사용자 편의성, 현재까지 발표된 뇌파 기반 사용자 식별 기법들의 안정성 등이 개선되어야 하는 것으로 파악된다. 이와 관련하여 뇌파 측정 장비들의 발전 동향을 살펴보고 해당 장비들의 간소화와 인증정확도 간 트레이드오프(trade-off)와 최신 기계학습 및 인공지능 기술들을 활용한 뇌파 기반 사용자 식별 기법들의 안정성을 위해 해결되어야 할 뇌파의 시간차 문제 및 이에 따른 인증정확도 저하 문제를 규명하고 분석한다.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1990_1991
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• 2009

본 논문은 뇌파와 같이 측정을 위해서 많은 수의 채널이 필요한 계측 장치에서 채널에 따른 증폭률의 차이를 보정하기 위해 동일한 입력을 가한 후 측정된 시간 영역의 신호를 주파수 영역으로 변환하고 주파수 영역에서의 신호를 분석하여 각 채널의 증폭률의 차이를 유도하고 유도된 증폭률의 차이를 보정하는 알고리즘을 소개한다. 본 논문은 다채널 시스템에서 측정된 신호를 주파수 스펙트럼으로 변환하는 단계와 스펙트럼에서 각 채널의 이득률을 분석하는 단계를 포함하는 다채널 시스템에서 채널간 이득률을 보정하는 방법을 제안한다.

• Journal of the HCI Society of Korea
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• v.12 no.4
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• pp.75-80
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• 2017

With the technological development, benefits of Virtual Reality (VR) has become a key of medium in communication research. In addition, explaining human minds with physiological data has become more popular since more accurate and detailed data can be expressed. However, reading brain signals in a virtual environment setting with psychophysiological measures (e.g. EEG and fNIRS) has remained a difficulty for researchers due to a technical constraint. Since a combination of cables for brain measures attached to a head cap obstruct wearing a Head-Mounted Display (HMD) over the cap, measuring brain activities with multiple channels on several areas of the brain is inappropriate in the VR setting. Therefore, we have developed a new brain measurement cap that includes probe connectors and brackets enabling a direct connection to the HMD. We highly expect this method would contribute to cognitive psychology research measuring brain signals with new technology.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.2
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• pp.383-395
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• 2017

Brainwave-based user authentication technology has advantages such as changeability, shoulder-surfing resistance, and etc. comparing with conventional biometric authentications, fingerprint recognition for instance which are widely used for smart phone and finance user authentication. Despite these advantages, brainwave-based authentication technology has not been used in practice because of the price for EEG (electroencephalography) collecting devices and inconvenience to use those devices. However, according to the development of simple and convenient EEG collecting devices which are portable and communicative by the recent advances in hardware technology, relevant researches have been actively performed. However, according to the experiment based on EEG samples collected by using a single-channel EEG measurement device which is the most simplified one, the authentication accuracy decreases as the number of channels to measure and collect EEG decreases. Therefore, in this paper, we analyze technical problems that need to be solved for practical use of brainwave-based use authentication and propose an incremental elimination method of collected EEG samples for each user to consist a set of EEG samples which are effective to authentication users.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.240-243
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• 2013

본 논문에서는 로봇 제어 목적의 응용을 위해 SVM 알고리즘과 HMM 알고리즘을 근간으로 하는 효과적인 뇌파 데이터 자동 분류 방법을 제안한다. Emotive Epoc 헤드셋 뇌파 측정 장비를 이용하여 뇌파 데이터를 수집하고, 수집된 뇌파 데이터로부터 FFT알고리즘을 이용하여 특징 추출을 수행한다. 그리고 SVM 알고리즘을 이용한 1단계 분류 방법과 SVM 알고리즘의 분류 결과를 다시 입력 시퀀스로 삼아 시계열 학습 알고리즘인 HMM에 적용하는 2단계 분류 방법의 실험 결과를 소개한다.

• The Journal of the Korea Contents Association
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• v.7 no.8
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• pp.41-49
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• 2007

BCI system integrates control or telecommunication system with generating electric signals in scalp itself after signal acquisition. This system detect a movement of EEG at real time, can control an electron equipment using a generated signal through EEG movement or software-based processor. In this paper, we deal with removing and separating artifacts induceced from measurement when brain-computer interface system that analyzes recognizes EEG signals occurred from various mental states. In this paper, we proposed a method of EEG classification and an artifact interval detection using bisection mathematical modeling in the EEG classification process for BCI system implementation.

• Journal of Science Education
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• v.34 no.1
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• pp.113-123
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• 2010

The purpose of this study is to survey its characteristics through analyzing brain-wave activity in the scientifically-gifted and general children in the problem-solving process. The subjects of this study were 6 elementary school students, who are attending the institute of education for the gifted belonging to the regional office of education and 6 general children in the same region. The analysis was performed targeting total 12 people. As the task for measuring brain wave is Hanio tower, it is the effective task of researching into the problem-solving process. As the equipment of measuring brain wave is EEG System, it used equipment that was developed in Australia. The analysis of data was minimized noise. As a result of research, the gifted children are excellent in stable level compared to general people in a stable situation with opening the eyes, thereby being able to be known to be high in preparatory level for learning. This can be seen to be indicated as a result that the effect of learning is excellent due to being high in preparatory level for solving problem. Also, even in the process of performing task, the brain-activity level in the gifted children is high, thereby having been able to know that ${\alpha}-wave$ is formed that is significantly high in the regions of frontal lobe and occipital lobe. Accordingly, given developing task that is high in brain activity level of the gifted children, the higher educational effect will be able to be expected.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.947-950
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• 2015

In the cause of car accidents in Korea, drowsy driving has shown that it is larger fctors than drunk driving. Therefore, in order to prevent drowsy driving accidents, drowsiness detection and warning system for drivers has recently become a very important issue. Furthermore, Many researches have been published that measuring alpha wave of EEG signals is the effective way in order to be aware of drowsiness of drivers. In this study, we have developed EEG measuring device that applies a signal processing algorithm using the LabView program for detecting drowsiness. According to results of drowsiness inducement experiments for small test subjects, it was able to detect the pattern of EEG, which means drowsy state based on the changing of power spectrum, counterpart of alpha wave. After all, Comparing to the results of drowsiness pattern between commercial equipments and developed device, we could confirm acquiring similar pattern to drowsiness pattern. With this results, the driver's drowsiness prevention system expect that it will be able to contribute to lowering the death rate caused by drowsy driving accidents.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.3D
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• pp.231-240
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• 2010

As majority of the traffic accidents in intersections is caused by human factor, a close examination is required on its contributing factors through measuring the psychological and physiological response according to the driving characteristics of the drivers and the road conditions. In this study, for the safety evaluation of individual intersection considering human factors of the drivers, electroencephalography reaction was measured utilizing cutting-edge measuring equipment and survey on drivers' cognitive characteristics in ordinary times and while driving test was conducted. The relationship between the electroencephalography response when approaching the intersection and cognitive evaluation survey data in driving test was clarified, and individual intersection safety evaluation model was built considering cognitive evaluation factor and the reaction of a bio-response electroencephalography data. As a result, I could find out that cognitive evaluation was made through the reaction of a bio-response (Electroencephalography) process because electroencephalography reaction of a bio-response showed differently by the physical characteristics of the intersection and cognitive evaluation had a difference.