title: Classifying Regularized Sensor Covariance Matrices: An Alternative to CSP.
abstract: 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.
authors.forename: Linsey
authors.initials: L
authors.lastname: Roijendijk
authors.forename: Stan
authors.initials: S
authors.lastname: Gielen
authors.forename: Jason
authors.initials: J
authors.lastname: Farquhar
mesh_terms: D000465:Algorithms; D062207:Brain-Computer Interfaces; D003627:Data Interpretation, Statistical; D004569:Electroencephalography / Q000379:methods*; D000069550:Machine Learning; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D059629:Signal-To-Noise Ratio