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Introduction

This page contains questions that users could ask when they analyze their data with FieldTrip. If it is possible the answers are also provided. If any documentation already exist on the FieldTrip wiki which gives an answer, the answer should point to those pages. In some cases, may we just want to point to relevant literature.

Questions and Answers


What kind of source-reconstruction methods are implemented in FieldTrip?


The Inverse source parameter estimates from EEG/MEG data page describes under the second point which are the supported methods.

  1. dipole fitting
    1. simultaneous optimisation of position, orientation and strength
    2. symmetry constrains and/or fixed position, with free orientation and strength
  2. dipole scanning
    1. dynamic imaging of coherent sources (DICS)
    2. linear constrained minimum variance (LCMV)
    3. partial canonical coherence (PCC)
    4. multiple signal classification (MUSIC)
    5. scanning for residual variance
  3. distributed source modeling
    1. minimum norm estimation with and without noise regularisation (MNE)


What is the difference between the methods?
What kind of source-reconstruction method should I use?
  • Does it depend on the data? (EEG vs. MEG, oscillations vs event-related, realistic vs. non-realistic headmodel)


Event-related Field/Potential + time-course: MNE
Oscillatory activity + at certain point in time: beamforming (dics)
beamforming: lcmv - ?
(see Hesse, Jensen (2010) and Background of the MNE tutorial)

  • Does it depend on a priori hypothesis of the source involved? (cortical sheets vs. 3D grid)
  • Does it depend on what kind of information I am interested in? (e.g. changes in time or not)
What kind of data I need for source-reconstruction?


functional data, anatomical data, channel/electrode positions

Why should I use source-reconstruction?


point to introductionary literature

How should I do source-reconstruction?


depends on the specific method; available documentation in FT at the moment:

  • dipole fitting
    1. simultaneous optimisation of position, orientation and strength
    2. symmetry constrains and/or fixed position, with free orientation and strength

Compute forward simulated data and apply a dipole fit
Fit a dipole to the tactile ERF after mechanical stimulation
Generate simulated data

  • dipole scanning
    1. dynamic imaging of coherent sources (DICS)
    2. linear constrained minimum variance (LCMV)
    3. partial canonical coherence (PCC)
    4. multiple signal classification (MUSIC)
    5. scanning for residual variance
  • distributed source modeling
    1. minimum norm estimation with and without noise regularisation (MNE)