• 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.

• 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.

• 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.

• Korean Journal of Biological Psychiatry
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• v.22 no.2
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• pp.87-94
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• 2015

Objectives The power spectral analysis of electroencephalogram has been widely used to reveal the pathophysiology of the alcoholic brain. However, the results were not consistent and the three dimensional study can be hardly found. The purpose of this study was to investigate characteristics of the three dimensional electroencephalographic (EEG) activity of alcohol dependent patients using standardized low resolution electromagnetic tomography (sLORETA). Methods The participants consisted of 30 alcohol dependent patients and 30 normal healthy controls. All the participants were males who had refrained from alcohol at least one month and were not taking any medications. Thirty two channel EEG data was collected in the resting state with eyes-closed condition during 30 seconds. The three dimensional data was compared between two groups using sLORETA for delta, theta, alpha, beta1, beta2, and beta3 frequency bands. Results sLORETA revealed significantly increased brain cortical activity in alpha, beta1, beta2, and beta3 bands each in alcohol dependent patients compared to normal controls. The voxels showing the maximum significance were in the left transverse temporal gyrus, left superior temporal gyrus, left anterior cingulate, and left fusiform gyrus in alpha, beta1, beta2, and beta3 bands respectively. Conclusions These results suggest that chronic alcohol intake may cause neurophysiological changes in cerebral activity. Therefore, the measuring of EEG can be helpful in understanding the pathophysiology of cognitive impairements in alcohol dependence.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.1
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• pp.122-127
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• 2009

In this paper, we measured EEG signals on frontal and Broca's area when subjects imagine to speak A or B or C or D. These signals were analyzed by Event-Related Spectral Perturbation (ERSP), Inter-Trial Coherence (ITC) and Event Related Potential (ERP) methods. As a result, high coherences were showed at 1$\sim$13Hz during 0$\sim$300ms after the stimuli of each character and P300 was seen clearly and there are several differences between the ERP results. However, unlike the motivation of this study to classify the characters, it is impossible that we can classify each intention or each character cause these differences. Nevertheless, this paper suggest an application system using this results so BCI can provide various services.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.7
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• pp.2491-2496
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• 2010

The DFA(detrended fluctuation analysis) which is included the correlation property of the EEG is used to analysis the depth of anesthesia. We studied ASA I or II adult patients supported by the society of anesthesiologists. Patients with history of dementia and neurological disorder are excluded. Average age is $48.9{\pm}10.9$ old, average weight is $57.1{\pm}8.2$ kg and average hight is $158{\pm}6.6$cm of the patients under the operation. Anesthesia medicine is Sevoflurane and the stages of anesthesia are 6 stages, that is pre-operation, induction, right after induction, stop the medicine and post-operation. Among the scaling exponent ${\alpha}1$, ${\alpha}2$, ${\alpha}3$ we know that ${\alpha}1$, ${\alpha}3$, were well appeared to discriminate pre-operation, induction, right after induction, stop the medicine and post-operation. So we confirmed that the parameters is useful to the depth of anesthesia.

• Journal of KIISE:Software and Applications
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• v.27 no.1
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• pp.24-32
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• 2000

BCI(Brain-Computer Interface) is studied to control the machines with brain. In this study, an EEG(Electroencephalography) signal classification model is proposed. The model gets EEG pattern from each subject's brain and extracts characteristic features. The model discriminates the EEG patterns by using those extracted characteristic features of each subject. The proposed method classifies each pair of the given tasks and combines the results to give the final result. Four tasks such as rest, movement, mental-arithmetic calculation and point-fixing were used in the experiment. Over 90% of the trials, the model yielded successful results. The model exploits characteristic features of the subjects and the weight table that was produced after training. The analysis results of the model such as its high success rates and short processing time show that it can be used in a real-time brain-computer interface system.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.17-18
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• 2011

본 논문에서는 일반적으로 뇌파, 호흡운동, 표정변화, 심전도, 산소포화도, 인체의 땀 등을 이용하여 거짓말 탐지를 하는 것을 근간으로 하여 일상생활에서 쉽게 거짓말 탐지를 할 수 있는 시스템을 제작하여 제시한다. 이 거짓말 탐지기 시스템 제작을 통해 생체신호를 스마트폰과 연동함으로써 건강기기의 또 다른 방향을 제시할 것으로 기대된다.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.2049-2050
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• 2011

신체 움직임이 자유롭지 못한 중증 장애인의 경우 환경제어장치를 사용하면 일상생활 보조가 가능해지므로 활용 효과가 매우 크다. 그러나 현재 국내에서 개발되는 제품은 정상인을 위한 홈오토메이션이 대부분이고, 장애인을 위한 환경제어장치의 경우에도 입력 매체에 따라 대상 사용자가 제한되는 문제점이 있었다. 따라서 본 논문에서는 기존의 입력 장치 사용에 제한이 있었던 중증 장애인들도 사용가능하도록 1-채널 생체신호(뇌파 및 얼굴 근전도) 계측 시스템 및 환경제어장치를 개발하였다. 향후 개발된 시스템은 중증 장애인의 일상생활 체험관에 구축하고 장애인의 사용성 평가를 통해 그 효과를 입증하고자 한다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2011.07a
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• pp.490-493
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• 2011

최근 사용자와 컴퓨터간의 상호작용이 가능한 사용자 인터페이스(UI, User Interface)에 대한 연구가 활발히 진행되고 있다. 이중 키보드나 마우스, 리모컨과 같은 별도의 입력장치가 없이 뇌의 활동으로부터 발생하는 생체신호를 이용하여 사용자의 생각만으로 컴퓨터와 커뮤니케이션을 할 수 있는 뇌만으로 컴퓨터와 커(BCI, Brain-Computer Interface) 시스템이 각광을 받고 있다. 본 연구에서는 뇌의 생체신호로는 뇌전도도(EEG, Electroencephalogram)를 사용하였으며, 이를 통하여 P300 speller 실험을 수행하였다. P300 speller 실험을 통하여 발생된 뇌전도도를 취합하여 P300(사건 관련 전위(ERP, Event-related potential)에서 자극 제시 약 300msec 후에 정점에 달하는 정파)을 분석하였다.