• Journal of Computing Science and Engineering
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• 제7권2호
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• pp.132-138
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• 2013

Multi-modal techniques have received increasing interest in the neuroscientific and brain computer interface (BCI) communities in recent times. Two aspects of multi-modal imaging for BCI will be reviewed. First, the use of recordings of multiple subjects to help find subject-independent BCI classifiers is considered. Then, multi-modal neuroimaging methods involving combined electroencephalogram and near-infrared spectroscopy measurements are discussed, which can help achieve enhanced and robust BCI performance.

• Journal of Computing Science and Engineering
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• 제7권2호
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• pp.112-121
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• 2013

The last years have seen a rise of interest in using electroencephalography-based brain computer interfacing methodology for investigating non-medical questions, beyond the purpose of communication and control. One of these novel applications is to examine how signal quality is being processed neurally, which is of particular interest for industry, besides providing neuroscientific insights. As for most behavioral experiments in the neurosciences, the assessment of a given stimulus by a subject is required. Based on an EEG study on speech quality of phonemes, we will first discuss the information contained in the neural correlate of this judgement. Typically, this is done by analyzing the data along behavioral responses/labels. However, participants in such complex experiments often guess at the threshold of perception. This leads to labels that are only partly correct, and oftentimes random, which is a problematic scenario for using supervised learning. Therefore, we propose a novel supervised-unsupervised learning scheme, which aims to differentiate true labels from random ones in a data-driven way. We show that this approach provides a more crisp view of the brain states that experimenters are looking for, besides discovering additional brain states to which the classical analysis is blind.

• 한국산학기술학회논문지
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• 제16권11호
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• pp.7508-7512
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• 2015

뇌파로 의도를 접속하여 기계를 작동하는 뇌-기기 접속(Brain-Computer Interface, BCI) 기반의 인간-로봇상호작용(Human-Robot Interaction, HRI) 플랫폼을 제안한다. 사람의 뇌파로 의도를 포착하고 포착된 뇌파 신호에서 의도를 추출하거나 연관시키고 추출된 의도로 기기를 작동하게 하는 포착, 처리, 실행을 수행하는 플랫폼의 설계, 운용 및 구현 과정을 소개한다. 제안된 플랫폼의 구현 사례로 처리기에 구현된 상호작용 게임과 처리기를 통한 외부 장치 제어가 기술되었다. BCI 기반 플랫폼의 의도와 감지 사이의 신뢰성을 확보하기 위한 다양한 시도들을 소개한다. 제안된 플랫폼과 구현 사례는 BCI 기반의 새로운 기기 제어 작동 방식의 실현으로 확장될 것으로 기대된다.

• 한국지능시스템학회논문지
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• 제13권4호
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• pp.427-432
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• 2003

사람의 뇌 속에 있는 신경 세포들은 여러 정보 처리 활동을 하면서 전기적인 신호를 발생시키는데 이를 두피 표면에서 측정한 것이 뇌파이다. 이러한 뇌파는 임상에서 주로 이용되어 왔으나 근래에는 이러한 뇌파를 이용하여 컴퓨터와 통신하거나 기기를 제어할 수 있는 이른바 BCI(Brain-Computer Interface)에 대한 연구가 대두되고 있다. BCI 연구의 궁극적 목표는 다양한 정신상태에 따른 뇌파의 특성을 파악하여 컴퓨터나 기기 등을 제어하는 것이다. 이를 위하여 본 연구에서는 좀 더 정확하고 신뢰성 있는 기기 제어를 위해 피험자의 의지대로 발생시킨 잡파를 이용하여 방향 제어 시스템을 구현하였다. 뇌파에 포함된 잡파 중 구별될 수 있는 특징을 나타내는 잡파를 선택하고 이들의 패턴을 인식하고 분류한 후 이를 제어신호로 변환하여 방향을 제어하는 시스템을 구현하였다.

• 대한인간공학회지
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• 제26권2호
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• pp.167-171
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• 2007

Brain computer interface is the technology of interface for next generation. Recently, user intention has been tried to be recognized for interfacing a computer. EEG plays important role in developing practical application in this area. Much research has focused on extracting EEG commander generated by human movement. ERD/ERS has generally accepted as important EEG parameters for prediction of human movement. However, There has been difference between initial movement indicated by ERD/ERS and real movement. Therefore, this study was to determine the time differences for brain interface by ERD/ERS. Five university students performed ten repetitive movements. ERD/ERS was determined according to movement execution and the significant pattern showed the difference between movement execution and movement indication of ERD/ERS.

• 안보군사학연구
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• 통권1호
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• pp.249-267
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• 2003

EEG is an electrical signal, which occurs during information processing in the brain. These EEG signals have been used clinically, but nowadays we are mainly studying Brain-Computer Interface (BCI) such as interfacing with a computer through the EEG, controlling the machine through the EEG. The ultimate purpose of BCI study is specifying the EEG at various mental states so as to control the computer and machine. This research makes the controlling system of directions with the artifact that are generated from the subject's will, for the purpose of controlling the machine correctly and reliably. We made the system like this. First, we select the particular artifact among the EEG mixed with artifact, then, recognize and classify the signals' pattern, then, change the signals to general signals that can be used by the controlling system of directions.

• ETRI Journal
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• 제44권4호
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• pp.672-685
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• 2022

This article proposes a subject-independent application of brain-computer interfacing (BCI). A 32-channel Electroencephalography (EEG) device is used to measure imagined speech (SI) of four words (sos, stop, medicine, washroom) and one phrase (come-here) across 13 subjects. A deep long short-term memory (LSTM) network has been adopted to recognize the above signals in seven EEG frequency bands individually in nine major regions of the brain. The results show a maximum accuracy of 73.56% and a network prediction time (NPT) of 0.14 s which are superior to other state-of-the-art techniques in the literature. Our analysis reveals that the alpha band can recognize SI better than other EEG frequencies. To reinforce our findings, the above work has been compared by models based on the gated recurrent unit (GRU), convolutional neural network (CNN), and six conventional classifiers. The results show that the LSTM model has 46.86% more average accuracy in the alpha band and 74.54% less average NPT than CNN. The maximum accuracy of GRU was 8.34% less than the LSTM network. Deep networks performed better than traditional classifiers.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.260-263
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• 2003

EEC is an electrical signal, which occurs during information processing in the brain. These EEG signals has been used clinically, but nowadays we are mainly studying Brain-Computer Interface(BCI) such as interfacing with a computer through the EEG controlling the machine through the EEG The ultimate purpose of BCI study is specifying the EEG at various mental states so as to control the computer and machine. A BCI has to perform two tasks, the parameter estimation task, which attemps to describe the properties of the EEG signal and the classification task, which separates the different EEC patterns based on the estimated parameters. First, we have to do parameter estimation of EEG to embody BCI system. It is important to improve performance of classifier, But, It is not easy to do parameter estimation by reason of EEG is sensitivity and undergo various influences. Therefore, this research should do parameter estimation and classification of the EEG to use various analysis algorithm.