Multilinear generalization of Common Spatial Pattern

The common spatial patterns (CSP) algorithm has been widely used in EEG classification and brain computer interface (BCI). In this paper, we propose a multilinear formulation of the CSP, termed as TensorCSP or common tensor discriminant analysis (CTDA) for high-order tensor data. As a natural extension of CSP, the proposed algorithm uses the analogous optimization criteria in CSP and a new framework for simultaneous optimization of projection matrices on each mode based on tensor analysis theory is developed. Experimental results demonstrate that our proposed algorithm is able to improve classification accuracy of multi-class motor imagery EEG.