Glossary

The literature concerning the application of multivariate pattern analysis procedures to neuro-scientific datasets contains a lot of specific terms to refer to procedures or types of data, that are of particular importance. Unfortunately, sometimes various terms refer to the same construct and even worse these terms do not necessarily match the terminology used in the machine learning literature. The following glossary is an attempt to map the various terms found in the literature to the terminology used in this manual.

Block-averaging

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-noise ratio. However, averaging further decreases the number of samples in a dataset, which is already very low in typical fMRI datasets, especially in comparison to the number of features/voxels. Block-averaging might nevertheless improve the classifier performance, if it indeed improves signal-to-noise and the respective classifier benefits more from few high-quality samples than from a larger set of lower-quality samples.

Classifier

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

Meta-classifier

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 training and/or testing of the wrapped classifier.

Cross-validation

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

Chunk

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 important in the context of a cross-validation procedure, as it is required to measure the classifier performance on independent test datasets to be able to compute unbiased generalization estimates. This is of particular importance in the case of fMRI data, where two successively recorded volumes cannot be considered as independent measurements. This is due to the significant temporal forward contamination of the hemodynamic response whose correlate is measured by the MR scanner.

Conditional Attribute

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” conditional attributes are disabled by default.

Confusion Matrix

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) class. Each cell provides a count of how many samples of the target class were (mis)classifier into the corresponding class. In PyMVPA instances of ConfusionMatrix class provide not only confusion matrix itself but a bulk of additional statistics.

Dataset

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

Dataset attribute

An arbitrary auxiliary information that is stored in a dataset.

Decoding

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-reading’ is used as another alternative.

Epoch

Sometimes used to refer to a group of successively acquired samples, and, thus, related to a chunk.

Exemplar

Another term for sample.

Feature

A variable that represents a dimension in a dataset. This might be the output of a single sensor, such as a voxel, or a refined measure reflecting specific aspect of data, such as a specific spectral component.

Feature attribute

Analogous to a sample attribute, this is a per-feature vector of auxiliary information that is stored in a dataset.

Feature Selection

A technique that targets detection of features relevant to a given problem, so that their selection improves generalization of the constructed model.

fMRI

This acronym stands for functional magnetic resonance imaging.

Generalization

An ability of a model to perform reliably well on any novel data in the given domain.

Label

A label is a special case of a target for specifying discrete categories of samples in a classification analyses.

Learner

A model that upon training given some data (samples and may be targets) develops an ability to map an arbitrary feature space of samples into another space. If targets were provided, such learner is called supervised and tries to achieve mapping into the space of targets. If the target space defined by a set of discrete set of labels, such learner is called a classifier.

Machine Learning

A field of Computer Science that aims at constructing methods, such as classifiers, to integrate available knowledge extracted from existing data.

MVPA

This term originally stems from the authors of the Matlab MVPA toolbox, and in that context stands for multi-voxel pattern analysis (see Norman et al., 2006). PyMVPA obviously adopted this acronym. However, as PyMVPA is explicitly designed to operate on non-fMRI data as well, the ‘voxel’ term is not appropriate and therefore MVPA in this context stands for the more general term multivariate pattern analysis.

Neural Data Modality

A reflection of neural activity collected using some available instrumental method (e.g., EEG, fMRI).

Processing object

Most objects dealing with data are implemented as processing objects. Such objects are instantiated once, with all appropriate parameters configured as desired. When created, they can be used multiple times by simply calling them with new data.

Sample

A sample is a vector with observations for all feature variables.

Sample attribute

A per-sample vector of auxiliary information that is stored in a dataset. This could, for example, be a vector identifying specific chunks of samples.

Sensitivity

A sensitivity is a score assigned to each feature with respect to its impact on the performance of the learner. So, for a classifier, sensitivity of a feature might describe its influence on generalization performance of the classifier. In case of linear classifiers, it could simply be coefficients of separating hyperplane given by weight vector. There exist additional scores which are similar to sensitivities in terms of indicating the “importance” of a particular feature – examples are a univariate anova score or a noise_perturbation measure.

Sensitivity Map

A vector of several sensitivity scores – one for each feature in a dataset.

Spatial Discrimination Map (SDM)

This is another term for a sensitivity map, used in e.g. Wang et al. (2007).

Statistical Discrimination Map (SDM)

This is another term for a sensitivity map, used in e.g. Sato et al. (2008), where instead of raw sensitivity the result of significance testing is assigned.

Statistical Learning

A field of science related to machine learning which aims at exploiting statistical properties of data to construct robust models, and to assess their convergence and generalization performances.

Supervised

Is a learner which obtains both samples data and targets within a training dataset.

Target

A target associates each sample in the dataset with a certain category, experimental condition or, in case of a regression problem, with some metric variable. In case of supervised learning algorithm targets define the model to be trained, and provide the “ground truth” for assessing the model’s generalization performance.

Time-compression

This usually refers to the block-averaging of samples from a block-design fMRI dataset.

Training Dataset

Dataset which is used for training of the learner.

Testing Dataset

Dataset which is used to assess the generalization of the learner.

Weight Vector

See Sensitivity.