• Journal of Internet of Things and Convergence
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• v.9 no.1
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• pp.25-31
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• 2023

The brain-machine interface(BMI) is a next-generation interface that controls the device by decoding brain waves(also called Electroencephalogram, EEG), EEG is a electrical signal of nerve cell generated when the BMI user thinks of a command. The brain-machine interface can be applied to various smart devices, but complex computational process is required to decode the brain wave signal. Therefore, it is difficult to implement a brain-machine interface in an embedded system implemented in the form of an edge device. In this study, we proposed a new type of brain-machine interface system using IoT technology that only measures EEG at the edge device and stores and analyzes EEG data in the cloud computing. This system successfully performed quantitative EEG analysis for the brain-machine interface, and the whole data transmission time also showed a capable level of real-time processing.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.1252-1254
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• 2004

BMI(brain machine interface) has been recently applied to give a disabled person mobility. This study is to determine the effective EEG parameters for predicting the movement moment of body limbs thought analysis of moving averaged ERD. The results showed that the proposed method for classifying EEG for predicting the movement seemed to be better than the classical method of determining ERD.

• 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.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1993-1994
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• 2008

뇌-기계 인터페이스(BMI : Brain Machine Interface)는 사람의 뇌에서 추출된 데이터를 이용하여 신체동작 없이 기계나 컴퓨터를 동작시키는 새로운 인터페이스 기술이다. 이러한 뇌-기계 인터페이스 기술은 자발전위 뇌파와 유발전위 뇌파를 이용한다. 자발전위 뇌파는 원하는 파형의 파워 값을 조절하여 새로운 인터페이스를 만드는 것이고, 유발전위 뇌파는 자극을 받았을 때 발생하는 값을 이용하여 새로운 인터페이스를 구현하는 것을 말한다. 이 중 자발전위는 사람이 스스로 뇌파의 방출량을 조절할 수 있어 집중력 향상과 같은 효과를 얻을 수 있다는 장점이 있다. 따라서 본 연구에서는 자발전위를 이용하여 뇌-기계 인터페이스 기술을 구현하였다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.11
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• pp.7508-7512
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• 2015

We propose a brain-machine interface(BMI) based human-robot interaction(HRI) platform which operates machines by interfacing intentions by capturing brain waves. Platform consists of capture, processing/mapping, and action parts. A noninvasive brain wave sensor, PC, and robot-avatar/LED/motor are selected as capture, processing/mapping, and action part(s), respectively. Various investigations to ensure the relations between intentions and brainwave sensing have been explored. Case studies-an interactive game, on-off controls of LED(s), and motor control(s) are presented to show the design and implementation process of new BMI based HRI platform.

• Journal of Digital Convergence
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• v.13 no.6
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• pp.177-184
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• 2015

Brain-computer interface is (BCI) is a communication device that the brain activity is directly input to the computer without input devices, such as a mouse or keyboard. As the brain wave interface hardware technology evolves, expensive and large EEG equipment has been downsized cheaply. So it will be applied to various multimedia applications. Among BCI studies, we suggest the domestic and foreign research trend about how the BCI is applied about the game almost people use. Next, look at the problems of the game with the BCI, we would like to propose the future direction of domestic BMI research and development.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.5
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• pp.18-23
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• 2010

In this paper, we propose a computationally efficient method detecting the P300 wave for brain-machine interface. Electrophysiological researches have shown that the P300 wave's potential is decreased when human intention matches visual stimulation. Motivated by this fact, we can infer human intention for brain-machine interface by detecting the P300 wave's potential decrease. The P300 wave is recorded from EEG(electroencephalogram) electrodes attached on human brain skull after giving alphabetical stimulation. To detect the potential decrease in P300, firstly we statistically model the P300 wave's negative potential. Then we infer human intention based on maximum likelihood estimation. The proposed method was evaluated on the data recorded from three healthy human subjects. The method achieved an averaging accuracy of 98% from subject k, 90% from subject j and 79.8% from subject h.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1922-1923
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• 2007

현대 사회는 급속한 기술의 발전으로 인하여 공상과학영화에서나 볼 수 있었던 첨단 기술들이 실생활에서 구현되어지고 있다. 이러한 첨단기술 중 하나였던 뇌를 이용하여 각종 인터페이스를 제어하는 기술인 BCI 및 BMI 기술이 각광을 받고 있다. 이러한 기술들은 EEG 신호의 취득 및 분석 기술이 발전하면서 많은 발전을 이루었고 앞으로도 그 발전 가능성은 무궁무진하다. 따라서 본 연구에서는 이러한 기술의 실현을 위해 획득된 뇌파 신호를 분석하여 기계장치를 제어 할 수 있도록 데이터의 처리 방법을 제안하였다. 이러한 데이터 처리 방법으로는 Fir(Finite impulse response)필터링과 ICA알고리즘의 구현, FFT 분석을 통한 주파수별 전력분포 계산의 과정이 있다. 이러한 과정 등을 통해 피검자가 원하는 EEG 데이터를 얻을 수 있게 된다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.12
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• pp.346-351
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• 2017

This research examines 240 patients who visited a center a specific city, between July-September 2017. Subjects underwent body composition analysis and brainwave measurements, and were subsequently divided into groups according to BMI and body fat percentage. These patients were measured by timeseries linear analysis for their brain function and observed via brainwave activities. Results of the research are as follows: there is a difference in stress-resistance between obese and those in the healthy weight range. This implies there is a causal relationship between stress and obesity. In addition, the methodology used in this study, which is a scientific and objective physiological indicator of a scientific and objective physiological index, suggests that the results of the study are reliable. Results support that managing stress moderates obesity-related problems.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.7
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• pp.2949-2954
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• 2012

This study was to examine the correlation between the brain function of adult suffering from obesity and the level of their obesity based on brainwave measurements. Based on the result of Body Composition Analysis (BCA) examination, population of 651 overweight pupils was chosen from June 2011 to December of 2012 in S city, I hospital. These patients were measured by timeseries linear analysis for their brain function and observed via brainwave activities. The results have been thus far; first, as their BMI (Body Mass Index) and level of obesity (body fat percentage) were higher, degree of mental stress and resistant to stress marked lower. These results prove that by managing the stress resistant ability and attention ability, self-controlling ability, one can expect a positive effect on finding a methods to ease the obese-related problems.