Abstract
The study of brain cognitive function has recently expanded from classical univariate to multivariate analyses. In combination with different non-invasive neuroimaging modalities, these techniques unveil how cognitive processes are coded in space or in time. Moreover, recent trends allow fusion methods to combine signals of different nature and offer both spatial and temporal coherent information. This work reviews and implements in the MVPAlab Toolbox the Representational Similarity Analysis (RSA) for electroencephalography signals, which is a preliminary step to EEG-fMRI data fusion. To evaluate this methodology we have built a demo dataset from a pre-recorded EEG experiment designed to study differences in preparation between perceptual expectation and selective attention. We discuss the strengths and the versatility of this multivariate technique and its potential applications on multimodal data fusion. The complete source code is fully-integrated in the MVPAlab Toolbox, which increases the broad number of already implemented analyses and the versatility of the tool.
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Acknowledgments
This research was supported by the Spanish Ministry of Science 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 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).
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López-García, D., González-Peñalver, J.M., Górriz, J.M., Ruz, M. (2022). Representational Similarity Analysis: A Preliminary Step to fMRI-EEG Data Fusion in MVPAlab. In: Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., de la Paz López, F., Adeli, H. (eds) Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications. IWINAC 2022. Lecture Notes in Computer Science, vol 13258. Springer, Cham. https://doi.org/10.1007/978-3-031-06242-1_9
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