Machine learning: Decoding / MVPA

Authors: Dimitrios Pantazis, Seyed-Mahdi Khaligh-Razavi, Francois Tadel,

This tutorial illustrates how to run MEG decoding (a type of multivariate pattern analysis / MVPA) using support vector machines (SVM).


To reference this dataset in your publications, please cite Cichy et al. (2014).

Description of the decoding functions

Two decoding processes are available in Brainstorm:

These two processes work in a similar way, but they use a different classifier, so only SVM is demonstrated here.

In the context of this tutorial, we have two condition types: faces, and objects. The participant was shown different types of images and we want to decode the face images vs. the object images using 306 MEG channels.

Download and installation

Import the recordings

Select files

Decoding with cross-validation

Cross-validation is a model validation technique for assessing how the results of our decoding analysis will generalize to an independent data set.


  1. Cichy RM, Pantazis D, Oliva A (2014), Resolving human object recognition in space and time, Nature Neuroscience, 17:455–462.

  2. Guggenmos M, Sterzer P, Cichy RM (2018), Multivariate pattern analysis for MEG: A comparison of dissimilarity measures, NeuroImage, 173:434-447.

  3. King JR, Dehaene S (2014), Characterizing the dynamics of mental representations: the temporal generalization method, Trends in Cognitive Sciences, 18(4): 203-210

  4. Isik L, Meyers EM, Leibo JZ, Poggio T, The dynamics of invariant object recognition in the human visual system, Journal of Neurophysiology, 111(1): 91-102

Additional documentation

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Tutorials/Decoding (last edited 2021-09-15 15:29:26 by ?DimitriosPantazis)