MVPA-Light: A Classification and Regression Toolbox for Multi-Dimensional Data

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ID: 171469
2020
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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.
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treder2020mvpalightfrontiers Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Treder, Matthias S.;
Journal Frontiers in neuroscience
Year 2020
DOI
DOI not found
URL
https://www.frontiersin.org/article/10.3389/fnins.2020.00289/full
Keywords
Therapeutics. Pharmacology Chemical technology Computer applications to medicine. Medical informatics Science (General) Mechanical engineering and machinery neurosciences. biological psychiatry. neuropsychiatry mathematics electronic computers. computer science

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