• 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 Korea Contents Association Conference
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• 2017.05a
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• pp.477-478
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• 2017

4차 산업혁명 시대가 도래해 인간 뇌와 기계 간 인터페이스 기술 개발이 한창이다. BMI(Brain-Machine Interface)는 뇌의 신경계로부터 신호를 측정하고 분석해 기계와 같은 외부 기기에 연결해 제어함으로써 사용자의 의사나 의도대로 기기를 움직이는 인터페이스를 만드는 것이다. 뇌-기계 인터페이스 기술은 뇌질환 치료, 장애인을 위한 로봇 팔과 로봇다리 같은 인체 결합기술, 인간과 기계와의 직접적인 정신 교류의 개발을 위한 필적인 기술이다. 본 논문에서는 4차 산업혁명의 핵심기술 중 하나인 뇌 기계 인터페이스에 대한 3P 정보분석을 수행함으로써 BMI의 R&D 및 시장진입을 위한 전략을 제시하였다.

• 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.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.7
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• pp.468-474
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• 2005

Brain-machine interface (BMI) based on neuronal spike trains is regarded as one of the most promising means to restore basic body functions of severely paralyzed patients. The spike train decoding algorithm, which extracts underlying information of neuronal signals, is essential for the BMI. Previous studies report that a linear filter is effective for this purpose and there is no noteworthy gain from the use of nonlinear mapping algorithms, in spite of the fact that neuronal encoding process is obviously nonlinear. We designed several decoding algorithms based on the linear filter, and two nonlinear mapping algorithms using multilayer perceptron (MLP) and support vector machine regression (SVR), and show that the nonlinear algorithms are superior in general. The MLP often showed unsatisfactory performance especially when it is carelessly trained. The nonlinear SVR showed the highest performance. This may be due to the superiority of the SVR in training and generalization. The advantage of using nonlinear algorithms were more profound for the cases when there are false-positive/negative errors in spike trains.

• 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 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 Biomedical Engineering Research
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• v.31 no.1
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• pp.1-13
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• 2010

There are a great numbers of disabled individuals who cannot freely move or control specific parts of their body because of serious neurological diseases such as spinal cord injury, amyotrophic lateral sclerosis, brainstem stroke, and so on. Brain-computer interfaces (BCIs) can help them to drive and control external devices using only their brain activity, without the need for physical body movements. Over the past 30 years, several Bel research programs have arisen and tried to develop new communication and control technology for those who are completely paralyzed. Thanks to the rapid development of computer science and neuroimaging technology, new understandings of brain functions, and most importantly many researchers' efforts, Bel is now becoming 'practical' to some extent. The present review article summarizes the current state of electroencephalogram (EEG)-based Bel, which have been being studied most widely, with specific emphasis on its basic concepts, system developments, and prospects for the future.

• 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.

• Journal of Biomedical Engineering Research
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• v.31 no.5
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• pp.359-364
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• 2010

The purpose of the brain-computer (machine) interface (BCI or BMI) is to provide a method for people with damaged sensory and motor functions to use their brain to control artificial devices and restore lost ability via the devices. Functional electrical stimulation (FES) is a method of applying low level electrical currents to the body to restore or to improve motor function. The purpose of this study was to develop a SSVEP-based BCI rehabilitation training system with FES for spinal cord injured individuals. Six electrodes were attached on the subjects' scalp ($PO_Z$, $PO_3$, $PO_4$, $O_z$, $O_1$ and $O_2$) according to the extended international 10-20 system, and reference electrodes placed at A1 and A2. EEG signals were recorded at the sampling rate of 256Hz with 10-bit resolution using a BIOPAC system. Fast Fourier transform(FFT) based spectrum estimation method was applied to control the rehabilitation system. FES control signals were digitized and transferred from PC to the microcontroller using Bluetooth communication. This study showed that a rehabilitation training system based on BCI technique could make successfully muscle movements, inducing electrical stimulation of forearm muscles in healthy volunteers.

• 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.