• Title/Summary/Keyword: 뇌파신호

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A New Design Method of Machine Control Interface by Using Bio-signals (생체신호를 이용한 새로운 형태의 기계 제어 인터페이스 구현방법)

  • Jin Kyung-Soo;Park Byoung-Woo;Byeon Jong-Gil
    • The Journal of the Korea Contents Association
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    • v.5 no.1
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    • pp.19-26
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    • 2005
  • This paper introduces a new design method of realizing the machine control interface by using bio-signals(EEG/EOG). This method can be further expanded to be applied to the computer system responding to EEG or EOG signals and the general bio-feedback system. For this reason, we made the remotely controlled toy system controlled by the EEG spectrums, their combination indexes, and EOG parameters. And the headset that has bio-signal processing modules built-in offers convenience for users, and this make much more advanced system than any other existing BCI and BMI system.

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The efficiency Analysis of study using brainwave measurement device (Biopac 뇌파측정 장치를 이용한 학습의 효율성 분석)

  • An, Young-Jun;Lee, Chung-Heon;Park, Mun-Kyu;Ji, Hoon;Lee, Dong-Hoon
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2015.05a
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    • pp.951-953
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    • 2015
  • Learning for thinking says the behavior of the organism changes as a result of practice or experience. It is very difficult to identify focusing ability objectively when students study. But, brain of the body is not so. EEG signal means continuously electric records of brain potential variation between two points on the scalp when brain activities take place. In types of EEG, there are delta(0~4Hz), theta(4~8Hz), alpha(8~13Hz), beta(13~30Hz) and gamma waves(30~50Hz). SMR waves and Mid-beta waves appear when focused for studying. Part for the most influence on concentrating reported that Mid-beta waves. In relation to brain activities, EEG has been actively researched for evaluating brain focus index system during learning and study. So, By using Biopac system for this study, measured brain wave was converted into FFT for extracting Mid-beta domain signals that are related to learning after giving focus invoked subjects to a small number of people. When concentrating, we measured the change in the power of the Mid-beta frequency domain and presented a correlation. Based on these results, we analyzed whether students are concentrated objectively on learning or not. and hope to offer more efficient learning method.

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Differences of central and autonomic responses between olfactory stimuli with Lavenar and Jasmin in human (Lavendar와 Jasmin으로 유발된 후각 강성에 대한 중추 및 자율신경계 반응)

  • 백은주;이윤영;하태환;임재중;이배환
    • Proceedings of the Korean Society for Emotion and Sensibility Conference
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    • 1998.11a
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    • pp.158-162
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    • 1998
  • 향에 의해 유발되는 감성에 대한 중추신경계 및 자율신경계의 반응의 변화를 측정하기 위해 안정시키는 향과 각성시키는 향을 사용하여 주관적 평가와 동시에 시행하였다. 안정시키는 향으로 1% Lavendar 향을 사용하였고, 각성시키는 향으로 0.8% Jasmin 향을 사용하였으며, 안정상태를 향자극 전후에 측정하여 대조군으로 사용하였다. 중추신경계의 지표로 뇌파측정을 하였고 뇌파의 전극은 international 10-20 system에서 4 채널을 사용하였으며, 자율신경계의 지표로는 심전도, heart rate, 피부저항, 피부온도를 기록하였다. 뇌파의 분석은 Fast Fourier Transform analysis의 power spectra로 하였고, 그 frequency bands는 theta(4-8Hz), alpha(8-l3Hz), beta(14-30Hz)로 하였다 또한 심전도를 이용하여 심전도 상의 연속적인 R-R peak간 시간간격을 시계열 데이터로 재구성한 Heart rate variability 분석도 하였다. HRV 분석을 보다 정확히 할 수 있도록 호흡이 심전도에 미치는 영향을 제거하기 위하여 호흡을 분당 20회로 일정하게 하였다 생체신호 측정과 동시에 실시한 주관적 검사에서 lavendar 향은 친숙하게, jasmin 향은 활기차고 상쾌하고 유쾌하게 평가되었다. 뇌파 분석에서 lavendar 향을 주었을 때 theta의 증가 양상을 보였으며, Jasmin 향을 주었을 때는 모든 채널에서 beta 파의 증가 양상을 보였다. 또한 HRV 분석 결과 부교삼신경의 활동성이 부각되는 HF/LF의 값이 lavendar에서는 대조 자극보다 높게 나타났으며, jasmin에서는 대조자극보다 낮은 값이 나타나는 경향을 보였다. 결론적으로 안정과 각성의 후각 자극으로 인한 감성의 변화를 뇌파와 자율신경계 등의 생체지표로 관찰할 수 있었다.정하는 감성요인의 차이를 알 수 있었으며 또한 essential oil에서는 성별 차이가 없는데 반해 페르몬 향의 경우 성별의 차이를 나타내었다.. 방법을 타액과 혈청내 testosterone 농도 측정에 응용하여 RIA의 결과와 비교하여 본 바 상관관계가 타액에서 r=0.969, 혈청에서 r=0.990으로 두 결과가 잘 일치하였다. 본 실험에서 측정된 한국인 여성의 타액내 testosterone농도는 107.7$\pm$12.0 pmol/l이었고, 남성의 타액내 농도는 274.2$\pm$22.1 pmol/l이었다. 이상의 결과로 보아 본 연구에서 정립된 EIA 방법은 RIA를 대신하여 소규모의 실험실에서도 활용할 수 있을 것으로 사려된다.또한 상실기 이후 배아에서 합성되며, 발생시기에 따라 그 영향이 다르고 팽창과 부화에 관여하는 것으로 사료된다. 더욱이, 조선의 ${\ulcorner}$구성교육${\lrcorner}$이 조선총독부의 관리하에서 실행되었다는 것을, 당시의 사범학교를 중심으로 한 교육조직을 기술한 문헌에 의해 규명시켰다.nd of letter design which represents -natural objects and was popular at the time of Yukjo Dynasty, and there are some documents of that period left both in Japan and Korea. "Hyojedo" in Korea is supposed to have been influenced by the letter design. Asite- is also considered to have been "Japanese Letter Jobcheso." Therefore, the purpose of this study is to loo

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A Dual Filter-based Channel Selection for Classification of Motor Imagery EEG (동작 상상 EEG 분류를 위한 이중 filter-기반의 채널 선택)

  • Lee, David;Lee, Hee Jae;Park, Snag-Hoon;Lee, Sang-Goog
    • Journal of KIISE
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    • v.44 no.9
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    • pp.887-892
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    • 2017
  • Brain-computer interface (BCI) is a technology that controls computer and transmits intention by measuring and analyzing electroencephalogram (EEG) signals generated in multi-channel during mental work. At this time, optimal EEG channel selection is necessary not only for convenience and speed of BCI but also for improvement in accuracy. The optimal channel is obtained by removing duplicate(redundant) channels or noisy channels. This paper propose a dual filter-based channel selection method to select the optimal EEG channel. The proposed method first removes duplicate channels using Spearman's rank correlation to eliminate redundancy between channels. Then, using F score, the relevance between channels and class labels is obtained, and only the top m channels are then selected. The proposed method can provide good classification accuracy by using features obtained from channels that are associated with class labels and have no duplicates. The proposed channel selection method greatly reduces the number of channels required while improving the average classification accuracy.

Correlation between Stories and Emotional Responses for American Movies (영화 스토리와 관객 감성반응과의 상관성에 대한 연구)

  • Woo, Jeong-Gueon
    • The Journal of the Korea Contents Association
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    • v.21 no.7
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    • pp.13-19
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    • 2021
  • While watching the movie, the audience shows various emotional reactions. Emotional reactions such as sadness and anger, joy and anger appear depending on the storyline of the film. This aspect can be seen through the audience's brain wave response. This study is to examine the relationship between the movie story development and the movie story development through brain wave measurement of the emotional reaction of the audience in situations and events occurring in the movie development. Four American films, which represent each genre and are well known to many people, were selected for the study. These are of the adventure genre, of the animation genre, of the action genre, and of the drama genre. In order to measure the emotional response of these movies, four cases were set centered on the PPG of EEG and analyzed as a time series graph pattern. It can be seen that the emotional response on the graph has a certain relationship with the story development. It is expected that this study will help in selecting a genre when making a movie in the future, especially when deciding how to compose and develop a story, and it will help to induce the emotions of the audience.

Auto Thresholding for Efficient Neurofeedback Trainning (효과적인 뉴로피드백 훈련을 위한 임계값 설정 기법)

  • Shin, Min-Chul;Hwang, Hae-Do;Yoon, Seung-Hyun;Lee, Jieun
    • Journal of the Korea Computer Graphics Society
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    • v.25 no.2
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    • pp.19-29
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    • 2019
  • We develop a complete system that includes data collection, signal processing, and real-time interaction for effective neurofeedback training. Our system supports a sophisticated technique to find threshold values which are quite important for effective neurofeedback system. A therapist specifies a target success rate of positive feedback, allowable error and time. The system computes a current success rate and compare it with the target one. If the difference between two rates exceeds the allowable error for allowable time, we find an optimum threshold value to obtain the target success rate by using numerical optimization technique. We conduct several experiments by varying input parameters: target success rate, allowable error and time, and demonstrate the effectiveness of our technique by showing the desired target success rate is stably obtained and systematically controlled by input parameters.

Effect of Neurofeedback based Robotic Invention Education on Attention Ability of ADHD Children (뉴로피드백을 이용한 로봇 발명 교육이 ADHD 아동의 주의집중력 변화에 미치는 영향)

  • Nam, Hyun-wook
    • Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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    • v.6 no.6
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    • pp.273-283
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    • 2016
  • In this paper, the effect of neurofeedback training program on attention ability of ADHD children is studied. The main concept of a neurofeedback training program is a robot control with brain wave. To do this, Mindset(neurosky, ltd) was used as a brain wave measurement and lego NXT was used to a robot kit. The developed brain wave training program has a 12 chapter. Students meet a problem situation and they invent and make a problem solving robot with NXT kits. After that, they control the their own robot by their brain wave. Developed program was applied to 8 student who live in chunan area. To monitor a change of attention ability, attention behavior checklist, K-CBCL, CTRS-R, ADS were used. These checklist were recorded with before and after the program. The result shows that student attention ability is increase after the program in the most of the checklist.

Development of Brain-machine Interface for MindPong using Internet of Things (마인드 퐁 제어를 위한 사물인터넷을 이용하는 뇌-기계 인터페이스 개발)

  • Hoon-Hee Kim
    • Journal of Internet of Things and Convergence
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    • v.9 no.6
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    • pp.17-22
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    • 2023
  • Brain-Machine Interfaces(BMI) are interfaces that control machines by decoding brainwaves, which are electrical signals generated from neural activities. Although BMIs can be applied in various fields, their widespread usage is hindered by the low portability of the hardware required for brainwave measurement and decoding. To address this issue, previous research proposed a brain-machine interface system based on the Internet of Things (IoT) using cloud computing. In this study, we developed and tested an application that uses brainwaves to control the Pong game, demonstrating the real-time usability of the system. The results showed that users of the proposed BMI achieved scores comparable to optimal control artificial intelligence in real-time Pong game matches. Thus, this research suggests that IoT-based brain-machine interfaces can be utilized in a variety of real-time applications in everyday life.

Human Emotion Recognition using Power Spectrum of EEG Signals : Application of Bayesian Networks and Relative Power Values (EEG 신호의 Power Spectrum을 이용한 사람의 감정인식 방법 : Bayesian Networks와 상대 Power values 응용)

  • Yeom, Hong-Gi;Han, Cheol-Hun;Kim, Ho-Duck;Sim, Kwee-Bo
    • Journal of the Korean Institute of Intelligent Systems
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    • v.18 no.2
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    • pp.251-256
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    • 2008
  • Many researchers are studying about human Brain-Computer Interface(BCI) that it based on electroencephalogram(EEG) signals of multichannel. The researches of EEG signals are used for detection of a seizure or a epilepsy and as a lie detector. The researches about an interface between Brain and Computer have been studied robots control and game of using human brain as engineering recently. Especially, a field of brain studies used EEG signals is put emphasis on EEG artifacts elimination for correct signals. In this paper, we measure EEG signals as human emotions and divide it into five frequence parts. They are calculated related the percentage of selecting range to total range. the calculating values are compared standard values by Bayesian Network. lastly, we show the human face avatar as human Emotion.

Electroencephalogram-Based Driver Drowsiness Detection System Using Errors-In-Variables(EIV) and Multilayer Perceptron(MLP) (EIV와 MLP를 이용한 뇌파 기반 운전자의 졸음 감지 시스템)

  • Han, Hyungseob;Song, Kyoung-Young
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.39C no.10
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    • pp.887-895
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    • 2014
  • Drowsy driving is a large proportion of the total car accidents. For this reason, drowsiness detection and warning system for drivers has recently become a very important issue. Monitoring physiological signals provides the possibility of detecting features of drowsiness and fatigue of drivers. Many researches have been published that to measure electroencephalogram(EEG) signals is the effective way in order to be aware of fatigue and drowsiness of drivers. The aim of this study is to extract drowsiness-related features from a set of EEG signals and to classify the features into three states: alertness, transition, and drowsiness. This paper proposes a drowsiness detection system using errors-in-variables(EIV) for extraction of feature vectors and multilayer perceptron (MLP) for classification. The proposed method evaluates robustness for noise and compares to the previous one using linear predictive coding (LPC) combined with MLP. From evaluation results, we conclude that the proposed scheme outperforms the previous one in the low signal-to-noise ratio regime.