Methods for Dummies

M/EEG Analysis:Contrasts, Inferences and

Source Localisation

Diana OmigieStjepana Kovac

Last week revisited

What do we measure with EEG and MEG?

Why use these techniques?

What do we do we do with the raw data we record?• Downsampling• Montage Mapping• Epoching• Filtering• Artefact Removal• Averaging

What Next?Event related potentials (ERPs) are signal-averaged epochs of EEG that are time-locked to the onset of a stimulus

A waveform is a time series that plots scalp voltage (µV, T) over time (ms)

However,We might also want

• to carry out statistics comparing between conditions, subjects ..

• to localise the generators of the electrical activity

0100200300400500600-300-200-1000100200300msfemto T MRT15 (235)trial 1 1 trial 2 2

0100200300400500600-300-200-1000100200300msfemto T MRT15 (235)trial 1 1 trial 2 2

0100200300400500600-300-200-1000100200300

msfemto T MRT15 (235)trial 1 1 trial 2 2

This week

Contrast and Inferences Source Reconstruction

Contrasts and Inferences using SPM 8

……Which buttons do we need to press?

EEG data acquired on 128 channel ActiveTwo system sampled at 2048Hz

Randomised presentation of 86 faces and 86 scrambled faces

Experimental Paradigm

Aim:Identify at what point in time andover what sensor area the greatest

difference lies in the responses to faces and non faces.

Steps

2D Interpolation

Transformation of discreet channels into a continuous 2D interpolated image of M/EEG signals

Sensor Space Scalp Space

MULTI DIMENSIONALSCALP SPACE

create a 2D space by flattening the sensor locations and interpolating between them to create an image of M*M pixels ( where M=number of channels)

or

Create a 3 D space with time as added dimension. M*M*S (where S= number of samples)

MULTI DIMENSIONALSCALP SPACE

2Dcreate a 2D space by flattening the sensor locations and interpolating between them to create an image of M*M pixels ( where M=number of channels)

or 3DCreate a 3 D space with time as added dimension. M*M*S (where S= number of samples)

MULTI DIMENSIONALSCALP SPACE

2Dcreate a 2D space by flattening the sensor locations and interpolating between them to create an image of M*M pixels ( where M=number of channels)

or 3DCreate a 3 D space with time as added dimension. M*M*S (where S= number of samples)

MULTI DIMENSIONALSCALP SPACE

2Dcreate a 2D space by flattening the sensor locations and interpolating between them to create an image of M*M pixels ( where M=number of channels)

or 3DCreate a 3 D space with time as added dimension. M*M*S where S= number of samples

MULTI DIMENSIONALSCALP SPACE

2Dcreate a 2D space by flattening the sensor locations and interpolating between them to create an image of M*M pixels ( where M=number of channels)

or 3DCreate a 3 D space with time as added dimension. M*M*S where S= number of samples

Time

Background

Random Field theory allows us to: • make N dimensional spaces from sensor locations. • take into account the spatial correlation across pixels.

• correct for multiple statistical comparisons.

SPM 8Steps

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New directory

Faces & Scrambled faces

3D image file for each trial with dimension

32x 32x 161

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Sections through X and Y expressed over time

2D x-y space interpolated from the flattened electrode

locations at one point in time

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3D IMAGE

1st level analysis of EEG data is• not about modeling the data ( as in fMRI)• the transformation of data from

filename.mat and filename.dat format to image files (N1fT1 format)

• a necessary step to create the images which we carry out 2nd level analysis on

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1st level analysis button

• Used only when you know in advance the time window that you are interested in.

• The Specify 1st level button results in a 2D image with just spatial dimensions.

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Smoothing

• Important step to take before 2nd level analysis (In SPM, use smooth images function in the drop down other menu)

• Used to adjust images so that they better conform to the assumptions of random field theory

• Necessary for taking into consideration spatial and temporal variability between subjects

• General guiding principle: Let smoothing kernel match the data feature you need to enhance. Try to smooth the images with different kernels and see what looks best.

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Which Buttons Do weNeed to Press?

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Create a new directory Then

To produce a batch windowSelect directory created

Select two sample t test as designMake group 1 contain 1st type of trials

Make group 2 contain other type.

Save batch descriptionRun batch window

2 sample t-testDesign Matrix

click

click

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Result showing regions within epochs where faces and non faces differ reliably

Maxima [-13 -78 180] & [21 -68 180]

Coordinates correspond to the left and right posterior sites at 180ms

Time-frequency analysisTransform data into frequency spectrum

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MRO33 (200)

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Ideal for induced responses i.e. responses not phase locked to the stimulus onsetDifferent methods but SPM uses the Morlet Waveform Transform ( mathematical functions which breaks a signal into different components)Trade off between time resolution and frequency resolution