@misc{10481/73512,
year = {2022},
month = {2},
url = {http://hdl.handle.net/10481/73512},
abstract = {Background and Objective: The study of brain function has recently expanded from classical univariate to multivariate analyses. These multivariate, machine learning-based algorithms afford neuroscientists extracting more detailed and richer information from the data. However, the implementation of these procedures is usually challenging, especially for researchers with no coding experience. To address this problem, we have developed MVPAlab, a MATLAB-based, flexible decoding toolbox for multidimensional electroencephalography and magnetoencephalography data. Methods: The MVPAlab Toolbox implements several machine learning algorithms to compute multivariate pattern analyses, cross-classification, temporal generalization matrices and feature and frequency contri- bution analyses. It also provides access to an extensive set of preprocessing routines for, among others, data normalization, data smoothing, dimensionality reduction and supertrial generation. To draw statisti- cal inferences at the group level, MVPAlab includes a non-parametric cluster-based permutation approach. Results: A sample electroencephalography dataset was compiled to test all the MVPAlab main function- alities. Significant clusters (p < 0.01) were found for the proposed decoding analyses and different config- urations, proving the software capability for discriminating between different experimental conditions. Conclusions: This toolbox has been designed to include an easy-to-use and intuitive graphic user interface and data representation software, which makes MVPAlab a very convenient tool for users with few or no previous coding experience. In addition, 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.},
organization = {Spanish Government
	PID2019-111187GB-I00	
	BES-2017-079769},
organization = {MCIN/AEI},
organization = {FEDER "Una manera de hacer Europa''
	RTI2018-098913-B100},
organization = {Junta de Andalucía},
organization = {European Commission
	CV20-45250	
	A-TIC-080-UGR18	
	BTIC-586-UGR20	
	P20-00525},
organization = {Universidad de Granada/CBUA},
publisher = {Elsevier},
keywords = {Machine learning},
keywords = {Classifications},
keywords = {Cross-classification},
keywords = {Decoding},
keywords = {Cross-validation},
keywords = {Multivariate pattern analysis},
keywords = {MVPA},
keywords = {EEG},
keywords = {MEG},
keywords = {MVPAlab toolbox},
title = {MVPAlab: A machine learning decoding toolbox for multidimensional electroencephalography data},
doi = {10.1016/j.cmpb.2021.106549},
author = {López García, David and González Peñalver, José María and Gorriz Sáez, Juan Manuel and Ruz Cámara, María},
}