Melancholia EEG Classification Based on CSSD and SVM

摘要

It takes an important role to get the disease information from melancholia electroencephalograph (EEG). Firstly, A common spatial subspace decomposition (CSSD) method was used to extract features from 16-channel EEG of melancholia and normal healthy persons. Then based on support vector machines (SVM), a classifier was designed to train and test its classification capability between Melancholia and healthy persons. The results indicated that the proposed method can reach a higher accuracy as 95% in EEG classification, while the accuracy of the method based on wavelet is only 88%.That is, the proposed method is feasible for the melancholia diagnosis and research.