Research on the Neural Dipole Localization Using a Method Combining SVM with Nonlinear Dimensionality Reduction
Jianwei Li, Youhua H. Wang, Guilong Zong, Qing Feng Wu · 2009
Electroencephalogram (EEG) source localization is well known as an import inverse problem of electrophysiology. In order to improve the accuracy of inverse calculation from EEG signal, a new method combining multidimensional SVR with nonlinear dimensionality reduction was proposed. In our study, the ISOMAP algorithm was firstly used to find the low dimensional manifolds from high dimensional EEG signal. Then, a new method of Multidimensional Support Vector Regression (MSVR) with similar iterative re-weight least square (IRWLS) was applied to discover the parameters of EEG signals. In our experiments, EEG signals of epileptic spike were adopted as the objects. The satisfactory results were obtained.