MVPA-Light: a classification and regression toolbox for multi-dimensional data

Treder, Matthias S. ORCID: https://orcid.org/0000-0001-5955-2326 2020. MVPA-Light: a classification and regression toolbox for multi-dimensional data. Frontiers in Neuroscience 14 , 289. 10.3389/fnins.2020.00289

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Abstract

MVPA-Light is a MATLAB toolbox for multivariate pattern analysis (MVPA). It provides native implementations of a range of classifiers and regression models, using modern optimization algorithms. High-level functions allow for the multivariate analysis of multi-dimensional data, including generalization (e.g., time x time) and searchlight analysis. The toolbox performs cross-validation, hyperparameter tuning, and nested preprocessing. It computes various classification and regression metrics and establishes their statistical significance, is modular and easily extendable. Furthermore, it offers interfaces for LIBSVM and LIBLINEAR as well as an integration into the FieldTrip neuroimaging toolbox. After introducing MVPA-Light, example analyses of MEG and fMRI datasets, and benchmarking results on the classifiers and regression models are presented.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Frontiers Media
ISSN:	1662-4548
Date of First Compliant Deposit:	14 September 2020
Date of Acceptance:	12 March 2020
Last Modified:	05 May 2023 05:56
URI:	https://orca.cardiff.ac.uk/id/eprint/134801

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