Alexandre Gramfort

Alexandre Gramfort

Lecture 1: Decoding MEG data (demo).

Lecture 2: The impact of tools and modelling assumptions

A multivariate non-parametric approach is demonstrated, based in the following approach: if there is an effect in the data, we should be able to learn it from a fraction of such data and then make predictions out of independent new data. MVPA/decoding benefits MEG analysis by leveraging information of all channels and time points, increasing evidence (this is the base of cluster level analysis: improves statistical power looking a neighbouring power and frequencies).

 

Click here for a tutorial.

Neurotechnology is working to maximize neuroimaging spatial and temporal resolution, but better sensors and better statistical tools are needed to make the best of the data we can have. Here, concepts of M/EEG are presented from a computer vision approach, “take home messages” (THM) are given (sensors’ locations/orientations, SNR, etc.) and analytic strategies are compared. Later, advantages of MNE (minimum norm estimates) software for M/EEG are stated as an alternative to promote sparse solutions with non-stationary sources.