• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.847-850
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• 2016

뇌-컴퓨터 인터페이스(Brain-Computer Interface: BCI) 기술의 중요성 및 활용도가 증대됨으로써 EEG(electroencephalogram: EEG)기반의 사용자 인터페이스에 대한 개발 및 연구가 활발히 진행되고 있다. 그러나 뇌파 발생 훈련이 되어 있지 않은 사용자는 EEG 기반의 사용자 인터페이스를 사용하기가 어렵다. 따라서 본 논문에서는 향후 뇌파 훈련을 위한 시뮬레이터를 개발하고자, 그 전단계로 사용자에게서 공통적으로 정확도가 높게 측정되는 채널 및 특징점을 비교, 분석 하였다. 피험자 3명의 왼손 동작 상상과 오른손 동작 상상으로 발생된 EEG 생체신호로부터 ERD/ERS를 확인하고, 8개의 특징점을 추출하여 SVM 분류 알고리즘을 기반으로 정확도를 측정하였으며, ${\mu}$대역 채널 AF4, F4에서의 특징 MAV에서 가장 우수한 성능을 보였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1396-1398
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• 2015

BCI (Brain Computer Interface)는 사람의 두뇌와 컴퓨터를 연결하는 '뇌-컴퓨터 인터페이스'를 나타내는 것이며 EEG(Electroencephalogram)을 주로 분석하여 인간의 행동이나 의도를 파악한다. 본 논문에서는 EEG를 이용한 행동인식의 하나로 졸음을 판단하는 방법을 제안한다. 제안방법에서는 MindWave를 이용하여 얻은 실험 데이터를 FFT를 이용하여 1초 단위로 스펙트럼을 분석하여 High-Alpha 영역의 시간에 따른 데이터 변화 패턴을 분석하여 졸음을 판단한다. 실험 결과, 100%의 최고 성능을 얻을 수 있었다.

• Journal of information and communication convergence engineering
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• v.18 no.4
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• pp.245-253
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• 2020

The human body and nervous system transmit information through electric charges. After the electric charge transmits information to the brain, we can feel pain, numbness, comfort, and other feelings. Electric therapy is currently used widely in clinical practice because the field of examination is more representative of electrocardiogram, and in the field of treatment is more representative of electrotherapy. In this study, we design a system for neurophysiological therapy and conduct parameter calculation and model selection for the components of the system. The system is based on a discontinuous conduction mode (DCM) boost circuit, and controlled and regulated by a single-chip microcomputer. The system does not only have a low cost but also fully considers the safety of use, convenience of the human-computer interface, adjustment sensitivity, and waveform diversity in the design. In future, it will have strong implications in the field of electrotherapy.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.357-358
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• 2023

본 연구는 실제 행동과 운동 심상으로 팔과 다리 동작 인식을 위한 BCI 패러다임을 제안하고 유도성 분석을 한다. 이 페러다임은 각 팔과 양다리의 특정 움직임을 인식하기 위해 ERP를 기반 페러다임을 구성한다. BCI 페러다임은 왼팔, 오른팔, 양다리를 움직이는 영상 자극을 주며 이를 기반으로 왼팔, 오른팔, 양다리 움직임에 대한 인식을 한다. 거울뉴런은 실제 행동과 실제 행동을 보았을때와 운동심상을 통한 자극을 받았을 때 같은 뉴런이 활성화된다는 성질을 가지고 있다. 이러한 성질을 이용하여 운동심상만과 실제 행동을 동시에 학습할 경우를 유도성 분석을 진행한다. 또한 유도성 특징 분석을 통해 나타난 결과를 바탕으로 BCI 패러다임을 제안한다.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.5
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• pp.497-501
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• 2008

In this paper we present a method for continuous EEG classification, where we employ nonnegative tensor factorization (NTF) to determine discriminative spectral features and use the Viterbi algorithm to continuously classily multiple mental tasks. This is an extension of our previous work on the use of nonnegative matrix factorization (NMF) for EEG classification. Numerical experiments with two data sets in BCI competition, confirm the useful behavior of the method for continuous EEG classification.

• 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 KIISE:Software and Applications
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• v.29 no.11
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• pp.837-850
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• 2002

In this paper, we propose an EEG-based response time prediction method during a yes/no cognitive decision task. In the experimental task, a subject goes through responding of visual stimulus, understanding the given problem, controlling hand motions, and hitting a key. Considering the subject's varying brain activities, we model subjects' mental states with defining CT (cut time), ST (selection time), and RP (repeated period). Based on the assumption between ST and RT in the mental model, we predict subjects' response time by detection of selection time. To recognize the subjects' selection time ST, we extract 3 types of feature from the filtered brain waves at frequency bands of $\alpha$, $\beta$, ${\gamma}$ waves in 4 electrode pairs combined by spatial relationships. From the extracted features, we construct specific rules for each subject and meta rules including common factors in all subjects. Applying the ST detection rules to 8 subjects gives 83% success rates and also shows that the subjects will hit a key in 0.73 seconds after ST detected. To validate the detection rules and parameters, we test the rules for 2 subjects among 8 and discuss about the experimental results. We expect that the proposed detection method can be a basic technology for brain-computer-interface by combining with left/right hand movement or yes/no discrimination methods.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.5
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• pp.197-206
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• 2014

Motor imagery based Brain-computer Interface(BCI), which has recently attracted attention, is the technique for decoding the user's voluntary motor intention using Electroencephalography(EEG). For classifying the motor imagery, event-related desynchronization(ERD), which is the phenomenon of EEG voltage drop at sensorimotor area in ${\mu}$-band(8-13Hz), has been generally used but this method are not free from the performance degradation of the BCI system because EEG has low spatial resolution and shows different ERD-appearing band according to users. Common spatial pattern(CSP) was proposed to solve the low spatial resolution problem but it has a disadvantage of being very sensitive to frequency-band selection. Discriminative filter bank common spatial pattern(DFBCSP) tried to solve the frequency-band selection problem by using the Fisher ratio of the averaged EEG signal power and establishing discriminative filter bank(DFB) which only includes the feature frequency-band. However, we found that DFB might not include the proper filters showing the spatial pattern of ERD. To solve this problem, we apply a band-selection process using CSP feature vectors and linear discriminant analysis to DFBCSP instead of the averaged EEG signal power. The filter selection results and the classification accuracies of the existing and the proposed methods show that the CSP feature is more effective than signal power feature.

• Investigative Magnetic Resonance Imaging
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• v.4 no.2
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• pp.128-135
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• 2000

Purpose : The aim of this study is to develope the Magnetic Resonance Spectroscopy(MRS) data processing S/W which plays an important role as a diagnostic tool in clinical field. Materials and methods : Post-processing software of MRS based on graphical user interface(GUI) under windows operating system of personal computer(PC) was developed using MATLAB(Mathwork, U.S.A.). This tool contains many functions to increase the quality of spectrum data such as DC correction, zero filling, line broadening, Gauss-Lorentzian filtering, phase correction, etc. And we obtained the normal human brain $^1H$ MRS data from parietal white matter, basal ganglia and occipital grey matter region using 1.5T Gyroscan ACS-NT R6 (philips, Amsterdam, Netherland) MRS package. The analysis of the MRS peaks were performed by obtaining the ratio of peak area. Results : The peak ratios of NAA/Cr, Cho/Cr, MI/Cr for the different MRS machines have a little different values. But these peak ratios were not significantly different between different echo time MRS peak ratios in the same machine (p<0.05). Conclusion : MRS post-processing S/W based on GUI using PC was developed and applied to the analysis of normal human brain $^1H$ MRS. This independent MRS processing job increases the performance and throughput of patient scan of main console. Finally, we suggest that the database for normal in-yivo human MRS data should be obtained before clinical applications.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.3
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• pp.227-232
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• 2013

BCI (Brain Computer Interface) is technology to control external devices by measuring the brain activity, such as electroencephalogram (EEG), so that handicapped people communicate with environment physically using the technology. Among them, EEG is widely used in various fields, especially robot agent control by using several signal response characteristics, such as P300, SSVEP (Steady-State Visually Evoked Potential) and motor imagery. However, in order to control the robot agent without any constraint and precisely, it should take advantage of not only a signal response characteristic, but also combination. In this paper, we try to use the fusion of motor imagery and P300 from EEG for practical use of robot control in BCI environment. The results of experiments are confirmed that the recognition rate decreases compared with the case of using one kind of features, whereas it is able to classify each both characteristics and the practical use technology based on mobile robot and wireless BCI measurement system is implemented.