Averaging all samples recorded during a block of continuous stimulation in a block-design fMRI experiment. The rationale behind this technique is, that averaging might lead to an improved signal-to-no [..]

A model that maps an arbitrary feature space into a discrete set of labels.

An internal to PyMVPA term to describe a classifier which is usually a proxy to the main classifier which it wraps to provide additional data preprocessing (e.g. feature selection) before actually tra [..]

A technique to assess the generalization of the constructed model by the analysis of accuracy of the model predictions on presumably independent dataset.

A chunk is a group of samples. In PyMVPA chunks define independent groups of samples (note: the groups are independent from each other, not the samples in each particular group). This information is i [..]

An attribute of a learner which might be enabled or disabled, grouped within .ca attributes collection. If enabled, it might cause additional computation and memory consumption, so the “heaviest” cond [..]

Visualization of the generalization performance of a classifier. Each row of the matrix represents the instances in a predicted class, while each column represents the samples in an actual (target) cl [..]

In PyMVPA a dataset is the combination of samples, and their Dataset attributes.

An arbitrary auxiliary information that is stored in a dataset.

This term is usually used to refer to the application of machine learning or pattern recognition techniques to brainimaging datasets, and therefore is another term for MVPA. Sometimes also ‘brain-read [..]