An experimental study to compare CSP and TSM techniques to extract features during motor imagery tasks

Abstract

Common spatial pattern (CSP) is a well-established technique to extract features from electroencephalographic recordings for classification purpose in motor imagery brain-computer interface (BCI).The CSP algorithm is a mathematical procedure used for separating a multivariate signal into additive components which have maximum differences in variance between two windows; in other terms, CSP increases the signal variance for one condition while minimizing the variance for the other condition. Features computed by means of CSP are fed to a data classifier in order to discriminate between two mental tasks. A novel technique to achieve feature extraction is tangentspace mapping (TSM) that insists on spatial covariance matrices computed from the recorded electroencephalogram signals (EEG). TSM is based on Riemannian geometry, which allows one to estimate statistical features of data distributions over non-Euclidean spaces. The aim of this chapter is twofold: first, to provide a new data-visualization tool to visually inspect data distributions on the Riemannian space of spatial covariance matrices and its tangent bundle; second, to present an experimental comparison of CSP and TSM feature extraction, in conjunction with two classification methods, namely, support-vector machine and linear discriminant analysis. In particular, the experimental comparison performed on a number of data sets will show the superiority of TSM-based feature extraction over CSP.