02_MEEG_Preprocessing by xiuliliaofz


and the role of horses in the history of

           Vladimir Litvak

Wellcome Trust Centre for Neuroimaging
      UCL Institute of Neurology
At the beginning there was a horse

                      Berger originally had intended to study
                      astronomy. While he was serving in the German
                      army in the early 1890s, his horse slipped down
                      an embankment, nearly seriously injuring Berger.
                      His sister many miles away had a feeling he was
                      in danger and got her father to telegram him. This
                      astonished him so much that he switched to study
     Hans Berger                                       (Blakemore, 1977)
What do we need?

                                                  Possible source
 M/EEG signals
                                                    of artefact

            Time axis
  (sampling frequency and onset)        Sensor locations
                  ~300 sensors
<128 electrodes
Evoked response vs. spontaneous activity

         pre-stim         post-stim
                                       awake state

                                      resting state

                                      falling asleep


                                        deep sleep

                                                                        50 uV
                                                           1 sec

                    evoked response                    ongoing rythms

     MEG            EEG                  Arbitrary             Fieldtrip
                                  GUI             script
     CTF            EEGLAB               ASCII                 raw
     Neuromag       Biosemi              Matlab                timelock
     BTi-4D         Brainvision
     Yokogawa                                                  freq

        Convert button
           (fileio)                                        spm_eeg_ft2spm

                               SPM8 dataset
                           *.dat – binary data file
                             *.mat – header file
Expert’s corner

 • The *.mat file contains a struct, named D, which is
   converted to an meeg object by spm_eeg_load.

 • The *.dat file is memory-mapped and linked to the

 • Special functions called ‘methods’ provide a
   simple interface for getting information from the
   object and updating it and ensure that the header
   data remain consistent.
Now lets take a step back
Sensor locations

 • Requires quite complex sensor representation including locations and
   orientations of the coils and the way MEG channels are derived from
   the sensors.
 • Sensor representation is read automatically from the original dataset at


 • Presently only requires electrode locations. In the future will also
   include a montage matrix to represent different referencing
 • Usually electrode locations do not come with the EEG data.
 • SPM assigns default electrode locations for some common systems
   (extended 10-20, Biosemi, EGI – with user’s input).
 • Individually measured locations can be loaded; requires co-registration.
Understanding coordinate systems

 Coordinate systems can differ in their origin, units and orientation.

 • MNI coordinates are defined using landmarks inside the brain.
     – Advantage: locations can be related to anatomy
     – Disadvantage: co-registration to a structural scan is required

 • Head coordinates are defined based on the fiducials. Commonly used
   for MEG, but the definition differs between different MEG systems.
     – Advantage: once the location of the head is expressed in head coordinates,
       it can be combined with sensor locations even if the subject moves.
     – Disadvantage: requires fiducials; if the fiducials are moved, the coordinate
       system changes.

 • Device coordinates are defined relative to some point external to the
   subject and fixed with respect to the measuring device.
     – Advantage: head locations can be compared between different
       experiments and subjects.
     – Disadvantage: head location needs to be tracked.
Understanding coordinate systems

 In SPM8

 • Before co-registration
    – MEG sensors are represented in head coordinates in
    – EEG sensors can be represented in any Cartesian
      coordinate system. Units are transformed to mm.

 • After co-registration
    – MEG sensor representation does not change. The head
      model is transformed to head coordinates.
    – EEG sensors are transformed to MNI coordinates.

 Definition: Cutting segments around events.

 Need to know:
 • What happens (event type, event value)
 • When it happens (time of the events)

 Need to define:
 • Segment borders
 • Trial type (can be different triggers => single trial type)

 • SPM8 only supports fixed length trials (but there are ways to
   circumvent this).
 • The epoching function also performs baseline correction (using
   negative times as the baseline).

  • High-pass – remove the DC offset and slow trends in the

  • Low-pass – remove high-frequency noise. Similar to

  • Notch (band-stop) – remove artefacts limited in frequency,
    most commonly line noise and its harmonics.

  • Band-pass – focus on the frequency of interest and
    remove the rest. More suitable for relatively narrow
    frequency ranges.
Filtering - examples

         Unfiltered    5Hz high-pass   10Hz high-pass

       45Hz low-pass   20Hz low-pass   10Hz low-pass
EEG – re-referencing

      Average reference
EEG – re-referencing

 • Re-referencing can be used to sensitize sensor
   level analysis to particular sources (at the
   expense of other sources).

 • For other purposes (source reconstruction and
   DCM) it is presently necessary to use average
   reference. This will be relaxed in the future.

 • Re-referencing in SPM8 is done by the Montage
   function that can apply any linear weighting to the
   channels and has a wider range of applications.

            Eye blink




  • SPM8 has an extendable artefact detection
    function where plug-ins implementing different
    detection methods ca be applied to subsets of

  • Presently, amplitude thresholding, jump detection
    and flat segment detection are implemented.

  • Plug-in contributions are welcome.

  • In addition, topography-based artefact correction
    method is available (in MEEGtools toolbox).
Averaging - horses, once again.

                           In the 1870s Sir Francis Galton (1822-1911),
                           became the first scientific sportsman. He
                           derived the common facial features of
                           winning horses by photographically
                           superimposing heads of race-winning
                           thoroughbreds. In addition to its innovation to
                           sports, this is a first in the extraction of
                           common features and blurring of noise.

                           Encouraged by his horse racing success
                           Galton went on to identify the common
                           physical features in the faces of violent
                           criminals and murderers of the 1880s. He
                           hoped to be able to detect potential violent
                           offenders before they committed their crimes.
                           He superimposed photographs to obtain
                           composites that, in this case, turned out to be
                           nothing out of the ordinary.
Galton, 1880s
Robust averaging

                   Kilner, in prep. 2009
Robust averaging

 • Robust averaging is an iterative procedure that
   computes the mean, down-weights outliers, re-
   computes the mean etc. until convergence.

 • It relies on the assumption that for any channel and
   time point most trials are clean.

 • The number of trials should be sufficient to get a
   distribution (at least a few tens).

 • Robust averaging can be used either in combination
   with or as an alternative to trial rejection.
So that’s how we got to this point

       What is the crucial difference
        between M/EEG and fMRI
         from the point of view of
               data analysis
How can we characterize this?

Fourier analysis

               • Joseph Fourier (1768-1830)
               • Any complex time series can be broken
               down into a series of superimposed sinusoids
               with different frequencies
Fourier analysis


                   Different amplitude

                   Different phase
Methods of spectral estimation – example 1
      Morlet 3 cycles                    Hilbert transform

      Morlet 5 cycles

      Morlet 7 cycles                        Multitaper
Methods of spectral estimation – example 2

     Morlet 5 cycles                          Morlet w. fixed window

     Morlet 7 cycles   Optimized multitaper     Hilbert transform

     Morlet 9 cycles                                Multitaper
Robust averaging for TF

                                 Unweighted averaging

              Robust averaging
Thanks to:

The people who contributed material to
this presentation (knowingly or not):

                                   • Stefan Kiebel
                                   • Jean Daunizeau
                                   • Gareth Barnes
                                   • James Kilner
                                   • Robert Oostenveld
                                   • Hillel Pratt
                                   • Arnaud Delorme
                                   • Laurence Hunt

and all the members of the methods group past and present

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