Classifying Regularized Sensor Covariance Matrices: An Alternative to CSP

Common spatial patterns (CSP) is a commonly used technique for classifying imagined movement type brain–computer interface (BCI) datasets. It has been very successful with many extensions and improvements on the basic technique. However, a drawback of CSP is that the signal processing pipeline contains two supervised learning stages: the first in which class- relevant spatial filters are learned and a second in which a classifier is used to classify the filtered variances. This may lead to potential overfitting issues, which are generally avoided by limiting CSP to only a few filters.