• 센서학회지
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• 제25권4호
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• pp.257-263
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• 2016

Among several technologies related to human brain, Brain Computer Interface (BCI) system is one of the most notable technologies recently. Conventional BCI for direct communication between human brain and machine are discomfort because normally electroencephalograghy(EEG) signal is measured by using multichannel EEG sensor. In this study, we propose 2-channel EEG sensor-based application control system which is more convenience and low complexity to wear to get EEG signal. EEG sensor module and system algorithm used in this study are developed and designed and one of the BCI methods, Motor Imagery (MI) is implemented in the system. Experiments are consisted of accuracy measurement of MI classification and driving control test. The results show that our simple wearable system has comparable performance with studies using multi-channel EEG sensor-based system, even better performance than other studies.

• 한국전자통신학회논문지
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• 제8권5호
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• pp.791-796
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• 2013

뇌파 건강관리 시스템의 태동은 산업과 연구분야에서 요즘 중요한 쟁점으로 여겨지고 있다. 실시간으로 간질병이나 뇌경색의 환자들의 의료응급서비스를 지원하기 위해서는 EEG신호 감지가 필수적이다. 이러한 시스템을 위하여 효과적인 네트워크를 지원하는 것이 필수적이기 때문에 센서노드 기반의 무선통신 토폴로지를 제안하며 시뮬레이트한다. 마지막으로 이러한 네트워크의 효과적인 토폴로지를 위하여 옵넷 시뮬레이터의 결과를 평가한다.

• 한국전자통신학회논문지
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• 제11권9호
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• pp.923-928
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• 2016

오늘날 사람들의 건강을 실시간으로 점검하고 응급상황에 대처하기 위해 다양한 기술들이 개발되어지고 있다. 지금까지는 대부분 맥박과 같은 생체신호를 측정하여 활용하고 있지만 최근에는 뇌파(EEG)를 활용하는 연구가 늘어나고 있다. 그러나 길에서 걸어 다니는 모든 사람들의 EEG 신호를 실시간으로 검출하여 해당 서버에 전송하는 것은 여러 가지 문제점을 야기시킨다. 특히, 2차원 공간에서 실시간으로 EEG 신호를 수집 및 전송하는 제약이 존재하기 때문에 본 논문에서는 이러한 제약을 피할 수 있는 3차원 공간의 드론을 활용하는 효율적인 네트워크 모델을 제안하고자 한다. 이러한 모델을 Opnet 시뮬레이터를 활용하여 네트워크 구조를 설계하고 시뮬레이션하고 평가하였다.

• Journal of Information Processing Systems
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• 제14권4호
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• pp.1049-1061
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• 2018

As mobile devices such as smartphones and tablet PCs become more popular, users are becoming accustomed to consuming a massive amount of multimedia content every day without time or space limitations. From the industry, the need for user satisfaction investigation has consequently emerged. Conventional methods to investigate user satisfaction usually employ user feedback surveys or interviews, which are considered manual, subjective, and inefficient. Therefore, the authors focus on a more objective method of investigating users' brainwaves to measure how much they enjoy their content. Particularly for multimedia content, it is natural that users will be immersed in the played content if they are satisfied with it. In this paper, the authors propose a method of using a portable and dry electroencephalogram (EEG) sensor device to overcome the limitations of the existing conventional methods and to further advance existing EEG-based studies. The proposed method uses a portable EEG sensor device that has a small, dry (i.e., not wet or adhesive), and simple sensor using a single channel, because the authors assume mobile device environments where users consider the features of portability and usability to be important. This paper presents how to measure attention, gauge and compute a score of user's content immersion level after addressing some technical details related to adopting the portable EEG sensor device. Lastly, via an experiment, the authors verified a meaningful correlation between the computed scores and the actual user satisfaction scores.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2016년도 춘계학술대회
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• pp.513-514
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• 2016

최근 뇌파에 대한 연구가 아주 활발하게 진행되고 있다. 이러한 뇌파를 분석하기 위해서는 뇌파센서를 이용하여 뇌파를 수집하고 뇌파의 주파수 분석 등을 이용하여 뇌파를 분석할 수 있다. 이러한 분석을 위하여 본 논문에서는 NeuroSky 사의 mindwave mobile 뇌파 센서를 이용하여 뇌파를 수집하고 수집된 뇌파의 주파수 분석을 이용하여 delta, theta, alpha, SMR, beta 파를 분석하고 이러한 주파수별 뇌파가 집중도와 어떤 상관관계를 갖고 있는지 분석하고자 한다. 이러한 분석을 통하여 일반적으로 알려진 집중도와 beta파의 밀접한 관계뿐만 아니라 다른 주파수 성분과의 상관관계도 분석할 수 있을 것이다. 이러한 기초연구를 통하여 보다 발전된 형태의 뇌파를 연구할 수 있을 것이다.

• 센서학회지
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• 제13권3호
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• pp.199-206
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• 2004

Epileptic seizures result from a temporary electrical disturbance of the brain. In this paper, a method of discriminating EEG for diagnoses of temporal lobe epilepsy is proposed. The proposed method for classification of epilepsy and sleep EEG is based on the wavelet transform and the fuzzy c-means. The magnitude and mean of wavelet coefficients for each EEG band are applied to the cluster of the FCM classifier. The proposed system show a little more accurate diagnosis for EEG by analysis of frequency for Wavelet and the success rate of 95% classification using FCM. From the simulation results by the implemented system, we demonstrated this research can be reduce doctor's labors and realize quantitative diagnosis of EEG.

• 센서학회지
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• 제13권3호
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• pp.236-243
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• 2004

In this paper, we proposed a brain-computer interface system using EEG signals. It can generate 4 direction command signal from EEG signals captured during imagination of subjects. Bandpass filter used for preprocessing to detect the brain signal, and the power spectrum at a specific frequency domain of the EEG signals for concentration status and non-concentration one is used for feature. In order to generate an adequate signal for controlling the 4 direction movement, we propose a new interface system implemented by using a support vector machine and a time-multiplexing method. Moreover, bio-feed back process and on-line adaptive pattern recognition mechanism are also considered in the proposed system. Computer experimental results show that the proposed method is effective to recognize the non-stational brain wave signal.

• 한국컴퓨터정보학회논문지
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• 제15권10호
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• pp.105-112
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• 2010

의료 분야의 감성 및 심리 치료를 확장하여 이와 관련된 기술을 일반 생활에 접목하고, 또한 생체신호를 이용하여 보다 쾌적한 삶의 환경을 구축하려는 연구가 활발하게 진행되고 있다. 본 논문에서는 뇌전도(EEG : electroencephalogram)와 심전도(ECG : electrocardiogram)의 심박변이도(HRV : Heart Rate Variability)의 패턴을 분석하여 평온, 집중, 긴장, 우울의 네 가지 감성을 분류하고 추론하기 위한 감성추론시스템을 설계하고 구현하였다. 많은 감성 인식 연구가 얼굴이나 음성의 인식에 의하여 이루어지고 있으며, 생체신호를 이용한 추론 연구의 경우에도, 뇌전도나 심전도 등의 단일 생체신호의 분석에 의하여 이루어지고 있다. 본 논문에서는 단일 생체신호가 아닌 뇌전도와 심전도신호를 조합하여 복합적으로 분석함으로서 단일 생체신호의 분석 연구보다 추론의 정확도를 높였으며, 감성 추론을 위한 엔진으로지도 학습과 비지도학습의 RBFN(Radial Basis Function Network) 신경망을 적용하여 오류역전파 알고리즘의 지역 최소점과 수렴속도가 느린 단점을 보완하였다.

• 국제학술발표논문집
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• The 8th International Conference on Construction Engineering and Project Management
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• pp.237-242
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• 2020

Construction noise is among the most critical stressors that adversely affect the quality of life of the people residing near construction sites. Many countries strictly regulate construction noise based on sound pressure levels, as well as timeslots and type of construction equipment. However, individuals react differently to noise, and their tolerance to noise levels varies, which should be considered when regulating construction noise. Although studies have attempted to analyze individuals' stress responses to construction noise, the lack of quantitative methods to measure stress has limited our understanding of individuals' stress responses to noise. Therefore, the authors proposed a quantitative stress measurement framework with a wearable electroencephalogram (EEG) sensor to decipher human brain wave patterns caused by diverse construction stressors (e.g., worksite hazards). This present study extends this framework to investigate the feasibility of using the wearable EEG sensor to measure individuals' emotional stress responses to construction noise in a laboratory setting. EEG data were collected from three subjects exposed to different construction noises (e.g., tonal vs. impulsive noises, different sound pressure levels) recorded at real construction sites. Simultaneously, the subjects' perceived stress levels against these noises were measured. The results indicate that the wearable EEG sensor can help understand diverse individuals' stress responses to nearby construction noises. This research provides a more quantitative means for measuring the impact of the noise generated at a construction site on neighboring communities, which can help frame more reasonable construction noise regulations that consider various types of residents in urban areas.

• 한국정보기술학회 영문논문지
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• 제8권2호
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• pp.27-39
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• 2018

In this paper, we describe the algorithms, EEG classification methods, and position data analysis methods using EEG and ADS1299 sensors. In addition, it is necessary to manage the amount of real-time data of location data and EEG data and to extract data efficiently. To do this, we explain the process of extracting important information from a vast amount of data through a cloud server. The electrical signals extracted from the brain are measured to determine the psychological state and health status, and the measured positions can be collected using the position sensor and triangulation method.