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Representational Similarity Analysis: A Preliminary Step to fMRI-EEG Data Fusion in MVPAlab

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Part of the Lecture Notes in Computer Science book series (LNCS,volume 13258)


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.


  • Representational Similarity Analysis
  • RSA
  • Multimodal data fusion
  • MVPAlab
  • Electroencephalography

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  • DOI: 10.1007/978-3-031-06242-1_9
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  1. Górriz, J.M., et al.: Artificial intelligence within the interplay between natural and artificial computation: advances in data science, trends and applications. Neurocomputing 410, 237–270 (2020)

    CrossRef  Google Scholar 

  2. Fahrenfort, J.J., van Driel, J., van Gaal, S., Olivers, C.N.L.: From ERPs to MVPA using the Amsterdam Decoding and Modeling toolbox (ADAM). Front. Neurosci. 12, 368 (2018)

    CrossRef  Google Scholar 

  3. Treder, M.S.: MVPA-light: a classification and regression toolbox for multi-dimensional data. Front. Neurosci. 14(June), 1–19 (2020)

    Google Scholar 

  4. Bode, S., Feuerriegel, D., Bennett, D., Alday, P.M.: The Decision Decoding ToolBOX (DDTBOX) - a multivariate pattern analysis toolbox for event-related potentials. Neuroinformatics 17(1), 27–42 (2019)

    CrossRef  Google Scholar 

  5. López-García, D., Peñalver, J.M.G., Górriz, J.M., Ruz, M.: MVPAlab: a machine learning decoding toolbox for multidimensional electroencephalography data. Comput. Methods Programs Biomed. 214, 106549 (2022)

    Google Scholar 

  6. Oostenveld, R., Fries, P., Maris, E., Schoffelen, J.M.: FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput. Intell. Neurosci. 2011, 156869 (2011)

    Google Scholar 

  7. Hanke, M., et al.: PyMVPA: a unifying approach to the analysis of neuroscientific data. Front. Neuroinform. 3, 1–13 (2009)

    Google Scholar 

  8. Gramfort, A., et al.: MEG and EEG data analysis with MNE-Python. Front. Neurosci. 7, 1–13 (2013)

    Google Scholar 

  9. Cichy, R.M., Oliva, A.: A M/EEG-fMRI fusion primer: resolving human brain responses in space and time. Neuron 107(5), 772–781 (2020)

    Google Scholar 

  10. Kriegeskorte, N., Mur, M., Bandettini, P.: Representational similarity analysis - connecting the branches of systems neuroscience. Front. Syst. Neurosci. 2, 1–28 (2008)

    Google Scholar 

  11. Ma, D.S., Correll, J., Wittenbrink, B.: The Chicago face database: a free stimulus set of faces and norming data. Behav. Res. Methods 47(4), 1122–1135 (2015).

  12. López-García, D., Sobrado, A., González-Peñalver, J.M., Górriz, J.M., Ruz, M.: Multivariate pattern analysis of electroencephalography data in a demand-selection task. In: Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds.) IWINAC 2019. LNCS, vol. 11486, pp. 403–411. Springer, Cham (2019).

  13. López-García, D., Sobrado, A., Peñalver, J.M.G., Górriz, J.M., Ruz, M: Multivariate pattern analysis techniques for electroencephalography data to study flanker interference effects. Int. J. Neural Syst. 30(7), 2050024 (2020)

    Google Scholar 

  14. Delorme, A., Makeig, S.: EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134(1), 9–21 (2004)

    CrossRef  Google Scholar 

  15. Nili, H., Wingfield, C., Walther, A., Su, L., Marslen-Wilson, W., Kriegeskorte, N.: A toolbox for representational similarity analysis. PLoS Comput. Biol. 10(4), e1003553 (2014)

    Google Scholar 

  16. Kriegeskorte, N., Kievit, R.A.: Representational geometry: integrating cognition, computation, and the brain. Trends Cogn. Sci. 17(8), 401–412 (2013)

    Google Scholar 

  17. Popal, H., Wang, Y., Olson, I.R.: A guide to representational similarity analysis for social neuroscience. Soc. Cogn. Affect. Neurosci. 14(11), 1243–1253 (2019)

    Google Scholar 

  18. Walther, A., Nili, H., Ejaz, N., Alink, A., Kriegeskorte, N., Diedrichsen, J.: Reliability of dissimilarity measures for multi-voxel pattern analysis. Neuroimage 137, 188–200 (2016)

    Google Scholar 

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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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Correspondence to David López-García .

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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.

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