EEG feature extraction using common spatial pattern with spectral graph decomposition

This paper investigates the application of spectral graph decomposition method in improving the discriminative quality of spectral components of the common spatial pattern for seizure classification. The aim is to improve the variance between the normal and abnormal EEG patterns in feature extraction process. The affinity matrix of the graph spectral decomposition that derive the Laplacian matrix encodes the dataset of the CSP covariance matrix as a correlation instead of Euclidean (or Minkowski) distance on Gaussian kernel function. The feature vector containing pattern of abnormality is sorted in order of the magnitude of their simple statistical mean values. The results obtained show that the CSP-Spectral Graph Decompsotion approach seems to provide a better discriminative features than CSP feature extraction process.