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DeFleCT: Design of Flexible Cross-Talk Functions for Spatial Filtering of EEG/MEG data

The DeFleCT framework has been formulated and demonstrated in simulation in this paper:

Hauk O, Stenroos M.
A framework for the design of flexible cross-talk functions for spatial filtering of EEG/MEG data: DeFleCT.
Human Brain Mapping 2013

This page provides the

  • Matlab tools that implement the DeFleCT method and enable easy visualization of data on the cortex,
  • Data set that was used for making the examples in the paper
  • Scripts that produce the results of the examples in the paper.


Data set and code package

For this study, we used the sample data set of MNE software that we processed according to the MNE manual: geometries for cortically-constrained source space and three-shell head model were created, sensors and the head geometry were co-registered, and a three-shell BEM model & the lead-field matrices for MEG and EEG were built. In addition, an estimate of the noise covariance matrix was constructed from pre-stimulus data. These were then imported to Matlab.

The model data can be downloaded here (24 MB).

Matlab functions and scripts that implement DeFleCT and produce the results presented in the paper can be downloaded here. Before using the codes, read conditions from readme.txt.

Further questions should be addressed to Matti Stenroos or Olaf Hauk.

For more neuroimaging-related issues, please visit our CBU Wiki pages, or have a look at recent MEG publications from the CBU.


Brain activation estimated from EEG and MEG data is the basis for a number of time-series analyses. In these applications, it is essential to minimize "leakage" or "cross-talk" of the estimates among brain areas. Here, we present a novel framework that allows the design of flexible cross-talk functions (DeFleCT), combining three types of constraints: (1) full separation of multiple discrete brain sources, (2) minimization of contributions from other (distributed) brain sources, and (3) minimization of the contribution from measurement noise. Our framework allows the design of novel estimators by combining knowledge about discrete sources with constraints on distributed source activity and knowledge about noise covariance. These estimators will be useful in situations where assumptions about sources of interest need to be combined with uncertain information about additional sources that may contaminate the signal (e.g. distributed sources), and for which existing methods may not yield optimal solutions. We also show how existing estimators, such as maximum-likelihood dipole estimation, L2 minimum-norm estimation, and linearly-constrained minimum variance as well as null-beamformers, can be derived as special cases from this general formalism. The performance of the resulting estimators is demonstrated for the estimation of discrete sources and regions-of-interest in simulations of combined EEG/MEG data. Our framework will be useful for EEG/MEG studies applying time-series analysis in source space as well as for the evaluation and comparison of linear estimators.