Support vector machine method using in EEG signals study of epileptic spike

Abstract

Support vector machine (SVM) is a new method of Machine Learning. SVR algorithms are normally only used for single-output systems now. Several SVR models were evaluated to identify one appropriate for multi-input multi-output systems, which require a much more complex control system. Based on good understanding of the SVM theory and algorithm, our studies discussed the multi-dimensional support vector regression (MSVR) and improved its algorithms. 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, MSVR is first applied in inverse problems, it has the advantages of simpler operation, faster convergence and better effect compared with single output SVR.