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이다빛(David Lee),박상훈(Sang-Hoon Park),이희재(Hee-Jae Lee),이상국(Sang-Goog Lee) 한국정보기술학회 2017 한국정보기술학회논문지 Vol.15 No.6
Brain-computer interface (BCI) is a technology that can be used as augmentative and alternative communication (AAC) for people such as the elderly or the disabled who are restricted or impaired in physical function. In order for BCI to be used as AAC, it is important to select appropriate feature extraction and classification methods because the electroencephalogram (EEG) signal is non-linear and non-stationary. This study proposes a feature extraction and classification method of motor imagery EEG using convolutional neural network (CNN). The CNN, most commonly used in the field of images, uses a large number of training data to avoid the problem of overfitting. If the amount of training data is small, the CNN cause overfitting problems. Therefore, in this study, the CNN suitable with small amount of training data was designed for motor imagery based BCI, and then the motion imaginary EEG was learned and classified. The performance of the proposed method is shown to be about 3.8~4.5% in terms of average accuracy through comparison with existing machine learning methods.
웨이블릿 변환과 주성분 분석 기반의 결합 특징 벡터를 이용한 동작 상상 EEG 분류
이다빛(David Lee),박상훈(Sang-Hoon Park),이희재(Hee-Jae Lee),이상국(Sang-Goog Lee) 한국정보기술학회 2017 한국정보기술학회논문지 Vol.15 No.4
The brain-computer interface is a communication channel that controls and manipulates mechanism by transmitting human thoughts from a human to a mechanism. To do this, it is necessary to extract appropriate electroencephalogram (EEG) features. In this paper, we propose a combined feature vectors based on wavelet transform and principal component analysis to extract features of EEG signals. The proposed method consists of three steps: In the first step, the wavelet transform is applied to extract feature vectors of the motor imagery EEG. In the second step, the principal component analysis was used to reduce the dimensionality of the feature vectors. In the third step, the nonlinear support vector machines was applied to classify left or right hand motor imagery EEG. The performance of the proposed method was evaluated in terms of accuracy through three different motor imagery EEG datasets. The results of this study showed that the proposed method achieved higher accuracy in the motor imagery EEG classification. The results of this study show that the proposed method improve the classification accuracy of some subjects by 0.4~4.3% compared to existing single feature methods, thus achieves a high classification accuracy of 82.8%.
상호 정보와 희소 학습을 이용하여 동작 상상 뇌파의 분류 정확도 향상
이다빛(David Lee),박상훈(Sang-Hoon Park),이희재(Hee-Jae Lee),이상국(Sang-Goog Lee) 한국정보기술학회 2017 한국정보기술학회논문지 Vol.15 No.8
Brain-computer interface (BCI) is a technology that directly connects a human brain and a computer to control and manipulate the computer through electroencephalogram. The common spatial pattern (CSP) is the most famous method for extracting discriminative features from BCI based on motor imagery electroencephalogram. However, CSP is sensitive to the operational frequency band, and this operational frequency band is subject-specific. In this paper, we propose a method to extract subject-specific features based on mutual information and sparse learning. The proposed method divides the 4~40Hz band into 17 sub-bands using a fifth order Butterworth filter. Then, CSP is applied to each sub-band to extract CSP features. Thereafter, mutual information is used to select the four most discriminating feature sets from the CSP features and sparse learning was used to eliminate redundancy in the four pairs of features selected. Finally, support vector machine was used for learning and classification. The performance of the proposed method was evaluated using formally available BCI Competition III and IV. The proposed method showed higher average classification accuracy than the results of CSP, FBCSP and SFBCSP.
Small Sample Setting 동작 상상 EEG 분류를 위한 필터 뱅크 기반 정규화 공통 공간 패턴 앙상블 기법 연구
박상훈(Sang-Hoon Park),이다빛(David Lee),이상국(Sang-Goog Lee) 한국정보기술학회 2017 한국정보기술학회논문지 Vol.15 No.4
For the last few years, BCI(Brain-Computer Interface) researches have been pursued in the fields of medicine and engineering. Specially, the CSP(Common Spatial Pattern) method was widely used because it is useful for motor imagery eeg classification. However, it is difficult to select the frequency range according to the subjects, and it shows low performance in the SSS(Small Sample Setting) situation. In this paper, we propose a filter bank based regularized CSP to solve the frequency range selection problem and the problem in SSS situation. The proposed method consists of four steps. First, the filter bank is used to divide the signal. Second, normalized CSP is applied to each signal. Third, the features are selected by the MIBIF(Mutual Information Based Individual Feature) method. Fourth, the classification and the ensemble is based on the selected feature. The proposed method showed better performance than did the conventional methods. Especially, it showed better performance in SSS situation.
동작 상상 EEG 분류를 위한 Ruelle-Takens 매립정리 기반 정규화 공통 공간 패턴
박상훈(Sang-Hoon Park),이다빛(David Lee),이상국(Sang-Goog Lee) 한국정보기술학회 2017 한국정보기술학회논문지 Vol.15 No.7
The Brain-Computer Interface (BCI) research is being actively pursued in the areas of signal processing, engineering, and rehabilitation medicine. Common Spatial Pattern (CSP) is very useful as a method for analyzing motor imagery Electroencephalogram (EEG) and is used in many studies. However, the CSP method shows poor performance in the Small Sample Setting (SSS) situation. In addition, noise and non-stationary in the EEG interfere with classification. In this paper, we propose the Ruelle-Takens embedding theorem based regularized common spatial Pattern(RTR-CSP) to overcome these problems. The proposed method consists of four steps. First, The obtained EEG is band-pass filtered in the 7~30Hz band. Second, we create a new signal matrix with time delay added. Third, regularized CSP is applied to signal. finally, the signal is classified through an ensemble. Compared with CSP, R-CSP and CSSP methods, the proposed method yielded 12.53%, 5.24% and 1.99% better performance, respectively.
동작 상상 EEG 분류를 위한 필터 뱅크 기반 정규화 공통 공간 패턴
박상훈(Sang-Hoon Park),김하영(Ha-Young Kim),이다빛(David Lee),이상국(Sang-Goog Lee) Korean Institute of Information Scientists and Eng 2017 정보과학회논문지 Vol.44 No.6
Recently, motor imagery electroencephalogram(EEG) based Brain-Computer Interface (BCI) systems have received a significant amount of attention in various fields, including medicine and engineering. The Common Spatial Pattern(CSP) algorithm is the most commonly-used method to extract the features from motor imagery EEG. However, the CSP algorithm has limited applicability in Small-Sample Setting(SSS) situations because these situations rely on a covariance matrix. In addition, large differences in performance depend on the frequency bands that are being used. To address these problems, 4-40Hz band EEG signals are divided using nine filter-banks and Regularized CSP(R-CSP) is applied to individual frequency bands. Then, the Mutual Information-Based Individual Feature(MIBIF) algorithm is applied to the features of R-CSP for selecting discriminative features. Thereafter, selected features are used as inputs of the classifier Least Square Support Vector Machine (LS-SVM). The proposed method yielded a classification accuracy of 87.5%, 100%, 63.78%, 82.14%, and 86.11% in five subjects(“aa”, “al”, “av”, “aw”, and “ay”, respectively) for BCI competition III dataset Iva by using 18 channels in the vicinity of the motor area of the cerebral cortex. The proposed method improved the mean classification accuracy by 16.21%, 10.77% and 3.32% compared to the CSP, R-CSP and FBCSP, respectively The proposed method shows a particularly excellent performance in the SSS situation.
필터 뱅크 정규화 공통 공간-스펙트럼 패턴을 이용한 동작 상상 뇌파 분류
박상훈(Sang-Hoon Park),이다빛(David Lee),이상국(Sang-Goog Lee) 한국정보기술학회 2017 한국정보기술학회논문지 Vol.15 No.10
Recently, the Brain-Computer Interface(BCI) research based on motor imagery Electroencephalogram(EEG) receive attention in various fields such as engineering, medicine, and industry. The various methods are proposed for motor imagery EEG classification. Among them, Common Spatial Pattern(CSP) method is very effective in classifying motor imagery EEG. Therefore, it is applied in many studies. However, the CSP includes several problems. First, it is highly dependent on the frequency band. Second, it performs relatively weak in environments with a small number of data. Third, it does not consider the non-stationary of EEG. In this study, we propose filter bank regularized common spatio-spectral pattern method for overcoming these problems. The proposed method regularizes the time delay signal and non-time delay signal obtained from the filter bank, and extracts the features. The extracted features are classified through the ensemble. The proposed method outperforms than CSP, R-CSP, CSSP and FBCSP methods.