Correntropy induced metric based common spatial patterns

Abstract

Common spatial patterns (CSP) is a widely used method in the field of electroencephalogram (EEG) signal processing. The goal of CSP is to find spatial filters that maximize the ratio between the variances of two classes. The conventional CSP is however sensitive to outliers because it is based on the L2-norm. Inspired by the correntropy induced metric (CIM), we propose in this work a new algorithm, called CIM based CSP (CSP-CIM), to improve the robustness of CSP with respect to outliers. The CSP-CIM searches the optimal solution by a simple gradient based iterative algorithm. A toy example and a real EEG dataset are used to demonstrate the desirable performance of the new method.