MVPAlab Toolbox — v1.2.2

Software Release Notes - MVPAlab Toolbox v1.2.2:

• This update solves a bug in the sliding filter analysis: Dot indexing is not supported for variables of this type.

MVPAlab Toolbox — v1.2.1

Software Release Notes - MVPAlab Toolbox v1.2.1:

• This update solves a dimensionality problem caused by a singleton dimension generated in the sliding-filter analysis when only one subject was selected.

MVPAlab Toolbox — v1.2.0

Software Release Notes - MVPAlab Toolbox v1.2.0:

This update introduces new features, enhancements, and bug fixes to improve your experience. Please read on for more details:

• New feature: Channel selection: Now you have the ability to select a specific set of channels that will be employed for computing the decoding analyses. This allows you to focus on the channels that are most relevant to your analysis.

• Data preprocessing: We have added a configurable gaussian kernel for data smoothing.

• Bug Fixes: We have addressed several bugs reported by our users. These bug fixes include resolving issues related to graphical representation.

Full Changelog: v1.1.4...v1.2.0

MVPAlab Toolbox — v1.1.4

Release notes:

• The EEGlab data format (.set files) is now supported.

MVPAlab Toolbox — v1.1.3

Release notes:

• The cluster-based permutation test now supports one-tailed and two-tailed modalities.
• Added support for asymmetric train and test windows for temporal generalization analyses.
• Demo files have been updated.
• Bugfixes: The number of frequencies for the sliding filter analysis was always set to one.

MVPAlab Toolbox — v1.1.2

Release notes:

• Added new color scheme: Fusion.
• Added support for CV-MVCC.
• Fixed some fprintf.
• Bugfixes: The minimum value of folds is set to two in the GUI.

MVPAlab Toolbox — v1.1.1

Release notes:

• Bugfixes: EEGLAB data is now correctly imported.
• Updated version tag system.

MVPAlab Toolbox — v1.1.0

Release notes:

• MVPAlab now supports a standard data format.
• Fixed some spelling mistakes.
• Some plotting functions have been renamed.

MVPAlab (v1.0.0): A Machine Learning decoding toolbox for multidimensional electroencephalography data

MVPAlab is a MATLAB-based and very flexible decoding toolbox for multidimensional electroencephalography and magnetoencephalography data. The MVPAlab Toolbox implements several machine learning algorithms to compute multivariate pattern analyses, cross-classification, temporal generalization matrices and feature and frequency contribution analyses. This toolbox has been designed to include an easy-to-use and very intuitive graphic user interface and data representation software, which makes MVPAlab a very convenient tool for those users with few or no previous coding experience. However, MVPAlab is not for beginners only, as it implements several high and low-level routines allowing more experienced users to design their own projects in a highly flexible manner.

BETA v.0.1.0

This is a pre-release version of MVPAlab Toolbox.