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This research was supported by the Spanish Ministry of Sci- ence and Innovation under the PID2019–111187GB-I00 grant, by the MCIN/AEI/10.13039/50110 0 011033/ and FEDER “Una manera de hacer Europa’’ under the RTI2018-098913-B100 project, by the Consejería de Economía, Innovación, Ciencia y Empleo (Junta de Andalucía) and FEDER under CV20-45250, A-TIC-080-UGR18, B- TIC-586-UGR20 and P20-00525 projects. The first author of this work is supported by a scholarship from the Spanish Ministry of Science and Innovation (BES-2017–079769). Funding for open ac- cess charge: Universidad de Granada / CBUA. The sample EEG dataset was extracted from an original experiment previously ap- proved by the Ethics Committee of the University of Granada., 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., Spanish Government PID2019-111187GB-I00 BES-2017-079769, MCIN/AEI, FEDER "Una manera de hacer Europa'' RTI2018-098913-B100, Junta de Andalucía, European Commission CV20-45250 A-TIC-080-UGR18 BTIC-586-UGR20 P20-00525, Universidad de Granada/CBUA

Recognizing familiar individuals is achieved by the brain by combining cues from several sensory modalities, including the face of a person and her voice. Here we used functional magnetic resonance (fMRI) and a whole-brain, searchlight multi-voxel pattern analysis (MVPA) to search for areas in which local fMRI patterns could result in identity classification as a function of sensory modality. We found several areas supporting face or voice stimulus classification based on fMRI responses, consistent with previous reports; the classification maps overlapped across modalities in a single area of right posterior superior temporal sulcus (pSTS). Remarkably, we also found several cortical areas, mostly located along the middle temporal gyrus, in which local fMRI patterns resulted in identity "cross-classification": vocal identity could be classified based on fMRI responses to the faces, or the reverse, or both. These findings are suggestive of a series of cortical identity representations increasingly abstracted from the input modality.