A linear classifier was trained using this system, biased towards activating stimulation in accordance with clinical considerations.

We used recursive feature elimination to extract relevant features, which were subsequently used to train a linear classifier.

Fisher discriminative analysis (FDA) is performed on the features of training samples and a linear classifier is generated.

The Representational Dissimilarity Matrix (RDM) used for RSA was derived from confusions of a linear classifier operating on single EEG trials.

Linear classifiers based on parameters from the front edge, back edge and interior tumor regions are examined.