Xueqin Shen

Classification of Mental Task From EEG Signals Using Immune Feature Weighted Support Vector Machines

The classification of mental tasks is one of key issues of EEG-based brain computer interface (BCI). Differentiating classes of mental tasks from EEG signals is challenging because EEG signals are nonstationary and nonlinear. Owing to its powerful capacity in solving nonlinearity problems, support vector machine (SVM) method has been widely used as a classification tool. Traditional SVMs, however, assume that each feature of a sample contributes equally to classification accuracy, which is not necessarily true in real applications. In addition, the parameters of SVM and the kernel function also affect classification accuracy. In this study, immune feature weighted SVM (IFWSVM) method was proposed. Immune algorithm (IA) was then introduced in searching for the optimal feature weights and the parameters simultaneously. IFWSVM was used to multiclassify five different mental tasks. Theoretical analysis and experimental results showed that IFWSVM has better performance than traditional SVM.

Research on MRI brain segmentation algorithm with the application in model-based EEG/MEG

MRI head image segmentation is a key issue for real head and brain construction in EEG/MEG applications. In the paper, methods for finding brain and contours were presented, and real calculation models for EEG and MEG research were constructed.

Enhancement of Infrared Image Based on the Retinex Theory

The infrared imaging technique can be used to image the temperature distribution of the body. Its hopeful to be applied to the diagnosis and prediction of many diseases. Image processing is necessary to enhance the original infrared images because of the blurring. In this paper, the image enhancement technique based on the Retinex theory is studied. The algorithms such as Frackle-McCann algorithm, McCann99 algorithm, single-scale Retinex algorithm and multi-scale Retinex algorithm are applied to the enhancement of gray infrared image. The acceptable results are obtained and compared.

Research on the Segmentation of MRI Image Based on Multi-Classification Support Vector Machine

In head MRI image, the boundary of each encephalic tissue is highly complicated and irregular. It is a real challenge to traditional segmentation algorithms. As a new kind of machine learning, support vector machine (SVM) based on statistical learning theory (SLT) has high generalization ability, especially for dataset with small number of samples in high dimensional space. SVM was originally developed for two-class classification. It is extended to solve multi-class classification problem. In this paper, 57 dimensional feature vectors for MRI image are selected as input for SVM. The segmentation of MRI image based on the multi-classification SVM (MCSVM) is investigated. As our experiment demonstrates, the boundaries of 7 kinds of encephalic tissues are extracted successfully, and it can reach satisfactory generalization accuracy. Thus, SVM exhibits its great potential in image segmentation.

Classifying the multiplicity of the EEG source models using sphere-shaped support vector Machines

Support vector machines (SVMs) are learning algorithms derived from statistical learning theory, and originally designed to solve binary classification problems. How to effectively extend SVMs for multiclass classification problems is still an ongoing research issue. In this paper, a sphere-shaped SVM for multiclass problems is presented. Compared with the classical plane-shaped SVMs, the number of convex quadratic programming problems and the number of variables in each programming are smaller. Such SVM classifier is applied to the electroencephalogram (EEG) source localization problem, and the multiplicity of source models is determined according to the potentials recorded on the scalp. Experimental results indicate that the sphere-shaped SVM based classifier is an effective and promising approach for this task.

3-D Reconstruction of Encephalic Tissue in MR Images Using Immune Sphere-Shaped SVMs

In the brain MR images, the boundary of each encephalic tissue is highly irregular. Traditional 3-D reconstruction algorithms are challenged. Owing to its powerful capacity in solving nonlinearity problems, the sphere-shaped support vector machines (SSSVMs) is applied in the 3-D reconstruction. Selecting parameters for SSSVM and the kernel function, however, is a complicated issue. Appropriate parameters can make the model more flexible and help to obtain more accurate data description. In this study, immune algorithm (IA) is used in searching for the optimal parameters. Immune SSSVM (ISSSVM) is proposed to reconstruct the 3-D encephalic tissues in MR images. As shown by the experiment of this study, each encephalic tissue can be reconstructed efficiently, and satisfied accuracy and visual effect can be obtained.

Research on Automatic Fingerprint Classification Based on Support Vector Machine

Automatic finger classification is an important part of fingerprint automatic identification system (FAIS). Its function is to provide a search system for large size database. Accurate classification can reduce searching time and expediate matching speed. Support vector machine (SVM) is a new learning technique based on statistical learning theory (SLT). SVM was originally developed for two-class classification. It was extended to solve multi-class classification problem. A hierarchical SVM with clustering algorithm based on stepwise decomposition was established to intellectively classify 5 classes of fingerprints. The design principle was proposed and the classification algorithm was implemented. SVM not only has more solid theoretical foundation, it also has greater generalization ability as our experiment demonstrates. The experimental results show that SVM is effective and surpasses other classical classification techniques

Multi-Layer Support Vector Machine and its Application

Based on statistical learning theory (SLT), support vector machine (SVM), which is a new kind of machine learning method that is used for classification and regression. SVM is considered as two layers learning machine since it maps the original space into a high dimensional feature space, i.e., input layer and high dimensional feature space layer. If the high dimensional feature space layer is considered as a new problems input layer and the new problem is also solved by SVM, the new problem can be solved by SVMs named multi-layer SVM (MLSVM). MLSVM is composed of input layer and at least one layer high dimensional feature space layer. In this paper, m-th order ordinary differential equations are solved by MLSVM for regression. Experimental results indicate that MLSVM can effectively solve the problem of ordinary differential equations. Thus, MLSVM exhibits its great potential to solve other complex problems

Research on 3D Modeling for Head MRI Image Based on Immune Sphere-Shaped Support Vector Machine

In head MRI image sequences, the boundary of each encephalic tissue is highly complicated and irregular. It is a real challenge to traditional 3D modeling algorithms. Support vector machine (SVM) based on statistical learning theory has solid theoretical foundation. sphere-shaped SVM (SSSVM) was originally developed for solving some special classification problems. In this paper, it is extended to image 3D modeling which tries to find the smallest hypersphere enclosing target data in high dimensional space by kernel function. However, selecting parameter is a complicated problem which directly affects modeling accuracy. Immune algorithm (IA), mainly applied to optimization, is used to search optimal parameter for SSSVM. So, immune SSSVM (ISSSVM) is proposed to construct the 3D models for encephalic tissues. As our experiment demonstrates, the models are constructed and reach satisfactory modeling accuracies. Theory and experiment indicate ISSSVM exhibits its great potential in image 3D modeling.

Research on Medical Diagnosis Decision Support System for Acid-base Disturbance Based on Support Vector Machine

Support vector machine (SVM) is a new learning technique based on statistical learning theory (SLT). In this paper, a medical diagnosis decision system (MDDSS) based on SVM has been established to intellectively diagnose 4 types of acid-base disturbance. SVM was originally developed for two-class classification. It is extended to solve multi-class classification problem named hierarchical SVM with clustering algorithm based on stepwise decomposition. Compared with other classical classification techniques, SVM not only has more solid theoretical foundation, it also has greater generalization ability as our experiment demonstrates. Thus, SVM exhibits its great potential in MDDSS