Pre-processingand the role of horses in the history of

M/EEG

Vladimir Litvak

Wellcome Trust Centre for NeuroimagingUCL Institute of Neurology

Hans Berger1873-1941

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 psychology.

(Blakemore, 1977)

At the beginning there was a horse

What do we need?

M/EEG signals

Time axis (sampling frequency and onset) Sensor locations

EventsPossible source

of artefact

~300 sensors<128 electrodes

resting state

falling asleep

sleep

deep sleep

coma

1 sec

50 uV

Evoked response vs. spontaneous activity

post-stimpre-stim

evoked response ongoing rythms

Averaging

activeawake state

Arbitrary

ASCIIMatlab

MEG

CTFNeuromagBTi-4DYokogawa

EEG

EEGLABBiosemiBrainvisionNeuroscanEGIANTSPM5…Biosig

Conversion

Fieldtrip

rawtimelockfreq

Convert button(fileio)

spm_eeg_convert

spm_eeg_ft2spm

GUI script

SPM8 dataset *.dat – binary data file

*.mat – header file

• 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 object.

• 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.

Expert’s corner

Now lets take a step back

Sensor locations

MEG:

• 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 conversion.

EEG:

• Presently only requires electrode locations. In the near future will also include a montage matrix to represent different referencing arrangements.

• 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.

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

mm.– 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.

Understanding coordinate systems

Epoching

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)

Note:• 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).

Filtering

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

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

• 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.

Unfiltered 5Hz high-pass 10Hz high-pass

45Hz low-pass 20Hz low-pass

Filtering - examples

10Hz low-pass

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.

EEG – re-referencing

EEG

MEG

Planar

Eye blink

Artefacts

Artefacts

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

• 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).

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.

Averaging - horses, once again.

Galton, 1880s

Kilner, in prep. 2009

Robust averaging

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

Time

Quiz

What is the crucial difference between M/EEG and fMRI from the point of

view of data analysis?

How can we characterize this?

?

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

Fourier analysis

Original

Different amplitude

Different phase

Fourier analysis

Fre

quen

cyWavelets

Wavelets – number of cycles

Unweighted averaging

Robust averaging

Robust averaging for TF

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• Christian Hesse• Laurence Hunt

and all the members of the methods group past and present