• Journal of Sensor Science and Technology
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• v.12 no.6
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• pp.289-297
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• 2003

In this paper, we propose accurate classification method of an EEG signals during a mental tasks. In the experimental task, subjects achieved through the process of responding to visual stimulus, understanding the given problem, controlling hand motions, and select a key. To recognize the subjects' selection time, we analyzed with 4 types feature from the filtered brain waves at frequency bands of $\alpha$, $\beta$, $\theta$, $\gamma$ waves. From the analysed features, we construct specific rules for each subject meta rules including common factors in all subjects. In this system, the architecture of the neural network is a three layered feedforward networks with one hidden layer which implements the error back propagation learning algorithm. Applying the algorithms to 4 subjects show 87% classification success rates. In this paper, the proposed detection method can be a basic technology for brain-computer-interface by combining with 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.

• 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.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2011.05a
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• pp.37-38
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• 2011

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2001.05a
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• pp.271-275
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• 2001

두뇌-컴퓨터 인터페이스로 활용하기 위한 시도로서, 인간의 가장 간단한 의사 표시인 긍/부정 의사와 관련한 뇌파를 측정하고 시간-주파수 분석을 수행하였다. 선행 연구 결과와 뇌파 측정 실험 조건에 대해 살펴 보고, 시간-주파수 분석 결과로부터 긍/부정 반응 동작에 따른 뇌파 변화에 대해 토론하였다.

• Communications of the Korean Institute of Information Scientists and Engineers
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• v.22 no.2
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• pp.20-34
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• 2004

최근 외신을 통해서 보도된 원숭이를 이용한 BCI (Brain-Computer Interface) 실험에 관한 보도 (Carmena et al., 2003)나 배우 Keanu Reeves가 세 편에 걸쳐서 열연한 영화 Matrix를 통해서나 뇌-컴퓨터 접속이라는 주제가 대중적으로 많은 인기를 끌고 있다. 배우 KeanuReeves는 1995년에도 Fox 영화사에서 제작된 Jonny Mnemonic Superbit Collection(국내 소개명 코드명J)라는 영화에서 뇌-컴퓨터접속기술을 소개한 바 있는데 그리 큰 인길르 끌지는 못하였다가 최근 몇 년간에 걸친 Matrix 쓰리즈에서 뇌-컴퓨터 접속기술을 범대중적으로 소개하는데 성공하였다.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2011.11a
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• pp.11-12
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• 2011

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2002.05a
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• pp.114-117
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• 2002

뇌-컴퓨터 인터페이스로 활용하기 위한 시도로서, 인간의 가장 간단한 의사 표시인 긍/부정 의사와 관련한 뇌파를 측정하고 시간-주파수 분석(단시간 푸리에 변환)을 수행하였다. 반응 동작과 관련한 $\mu$파와 $\beta$파, 그리고 인지 정보 처리와 관련한 ${\gamma}$파의 시간에 따른 변화를 살펴보고 선행 결과들과 비교, 토론하였다.

• Journal of KIISE
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• v.44 no.9
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• pp.887-892
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• 2017

Brain-computer interface (BCI) is a technology that controls computer and transmits intention by measuring and analyzing electroencephalogram (EEG) signals generated in multi-channel during mental work. At this time, optimal EEG channel selection is necessary not only for convenience and speed of BCI but also for improvement in accuracy. The optimal channel is obtained by removing duplicate(redundant) channels or noisy channels. This paper propose a dual filter-based channel selection method to select the optimal EEG channel. The proposed method first removes duplicate channels using Spearman's rank correlation to eliminate redundancy between channels. Then, using F score, the relevance between channels and class labels is obtained, and only the top m channels are then selected. The proposed method can provide good classification accuracy by using features obtained from channels that are associated with class labels and have no duplicates. The proposed channel selection method greatly reduces the number of channels required while improving the average classification accuracy.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.671-674
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• 2004

We propose accurate classification method of EEG signals during mental tasks. In the experimental task, the tasks of subjects show 3 major measurements; there are mathematical tasks, color decision tasks, and Chinese phrase tasks. The classifier implemented for this work is a feed-forward neural network that trained with the error back-propagation algorithm. The new BCI system is proposed by using neural network. In this system, tr e architecture of the neural network is composed of three layers with a feed-forward network, which implements the error back propagation-learning algorithm. By applying this algorithm to 4 subjects, we achieved $95{\%}$ classification rates. The results for BCI mathematical task experiments show performance better than those of the Chinese phrase tasks. The selection time of each task depends on the mental task of subjects. 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.