Digital Library[ Search Result ]
A Dual Filter-based Channel Selection for Classification of Motor Imagery EEG
David Lee, Hee Jae Lee, Snag-Hoon Park, Sang-Goog Lee
http://doi.org/10.5626/JOK.2017.44.9.887
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.
Face Detection using Orientation(In-Plane Rotation) Invariant Facial Region Segmentation and Local Binary Patterns(LBP)
Hee-Jae Lee, Ha-Young Kim, David Lee, Sang-Goog Lee
http://doi.org/10.5626/JOK.2017.44.7.692
Face detection using the LBP based feature descriptor has issues in that it can not represent spatial information between facial shape and facial components such as eyes, nose and mouth. To address these issues, in previous research, a facial image was divided into a number of square sub-regions. However, since the sub-regions are divided into different numbers and sizes, the division criteria of the sub-region suitable for the database used in the experiment is ambiguous, the dimension of the LBP histogram increases in proportion to the number of sub-regions and as the number of sub-regions increases, the sensitivity to facial orientation rotation increases significantly. In this paper, we present a novel facial region segmentation method that can solve in-plane rotation issues associated with LBP based feature descriptors and the number of dimensions of feature descriptors. As a result, the proposed method showed detection accuracy of 99.0278% from a single facial image rotated in orientation.
Filter-Bank Based Regularized Common Spatial Pattern for Classification of Motor Imagery EEG
Sang-Hoon Park, Ha-Young Kim, David Lee, Sang-Goog Lee
http://doi.org/10.5626/JOK.2017.44.6.587
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.
Motor Imagery EEG Classification Method using EMD and FFT
David Lee, Hee-Jae Lee, Sang-Goog Lee
Electroencephalogram (EEG)-based brain-computer interfaces (BCI) can be used for a number of purposes in a variety of industries, such as to replace body parts like hands and feet or to improve user convenience. In this paper, we propose a method to decompose and extract motor imagery EEG signal using Empirical Mode Decomposition (EMD) and Fast Fourier Transforms (FFT). The EEG signal classification consists of the following three steps. First, during signal decomposition, the EMD is used to generate Intrinsic Mode Functions (IMFs) from the EEG signal. Then during feature extraction, the power spectral density (PSD) is used to identify the frequency band of the IMFs generated. The FFT is used to extract the features for motor imagery from an IMF that includes mu rhythm. Finally, during classification, the Support Vector Machine (SVM) is used to classify the features of the motor imagery EEG signal. 10-fold cross-validation was then used to estimate the generalization capability of the given classifier., and the results show that the proposed method has an accuracy of 84.50% which is higher than that of other methods.
Search
Journal of KIISE
- ISSN : 2383-630X(Print)
- ISSN : 2383-6296(Electronic)
- KCI Accredited Journal
Editorial Office
- Tel. +82-2-588-9240
- Fax. +82-2-521-1352
- E-mail. chwoo@kiise.or.kr