MEG Reading Group

Performing parameter-free permutation testing on the basis of Student’s t-tests with BESA Statistics 1.0

I will show how parameter-free permutation testing can be performed with the BESA software ( The algorithm implemented in this statistical test is based on: Maris & Oostenveld (2007) -- In this paper, we show how ElectroEncephaloGraphic (EEG) and MagnetoEncephaloGraphic (MEG) data can be analyzed statistically using nonparametric techniques. Nonparametric statistical tests offer complete freedom to the user with respect to the test statistic by means of which the experimental conditions are compared. This freedom provides a straightforward way to solve the multiple comparisons problem (MCP) and it allows to incorporate biophysically motivated constraints in the test statistic, which may drastically increase the sensitivity of the statistical test. The paper is written for two audiences: (1) empirical neuroscientists looking for the most appropriate data analysis method, and (2) methodologists interested in the theoretical concepts behind nonparametric statistical tests. For the empirical neuroscientist, a large part of the paper is written in a tutorial-like fashion, enabling neuroscientists to construct their own statistical test, maximizing the sensitivity to the expected effect. And for the methodologist, it is explained why the nonparametric test is formally correct. This means that we formulate a null hypothesis (identical probability distribution in the different experimental conditions) and show that the nonparametric test controls the false alarm rate under this null hypothesis. Maris, E. and Oostenveld, R. | Nonparametric statistical testing of EEG- and MEG-data | J Neurosci Methods. 2007; 164(1): 177-190 | doi:10.1016/j.jneumeth.2007.03.024 Link: besa-statistics-overview