Methods for Dummies M/EEG Analysis: Contrasts, Inferences and Source Localisation Diana Omigie Stjepana 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 timetime

This week Contrast and InferencesSource 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 and over what sensor area the greatest difference lies in the responses to faces and non faces. Steps 1 st level Create a sensor map image for each trial 2 nd Level Take images into an unpaired t-test across trials to compare faces to scrambled faces Use classical SPM to identify locations in space and time

2D Interpolation Transformation of discreet channels into a continuous 2D interpolated image of M/EEG signals Sensor SpaceScalp Space

MULTI DIMENSIONAL SCALP 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 DIMENSIONAL SCALP SPACE 2D 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 3D Create a 3 D space with time as added dimension. M*M*S (where S= number of samples)

MULTI DIMENSIONAL SCALP SPACE 2D 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 3D Create a 3 D space with time as added dimension. M*M*S (where S= number of samples)

MULTI DIMENSIONAL SCALP SPACE 2D 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 3D Create a 3 D space with time as added dimension. M*M*S where S= number of samples

MULTI DIMENSIONAL SCALP SPACE 2D 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 3D Create 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 8 Steps Select preprocessed EEG data file Keep default of 32 to obtain 32 by 32 pixel space. Result 1st L E V E L

New directory Faces & Scrambled faces 3D image file for each trial with dimension 32x 32x 161 1st L E V E L

Sections through X and Y expressed over time 2D x-y space interpolated from the flattened electrode locations at one point in time 1st L E V E L 3D IMAGE

1 st 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 2 nd level analysis on 1st L E V E L

1 st level analysis button Used only when you know in advance the time window that you are interested in. The Specify 1 st level button results in a 2D image with just spatial dimensions.

2 nd L E V E L

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.

2 nd L E V E L Which Buttons Do we Need to Press?

2 nd L E V E L Create a new directory Then To produce a batch window Select directory created Select two sample t test as design Make group 1 contain 1 st type of trials Make group 2 contain other type. Save batch description Run batch window

2 sample t-test Design Matrix click 2 nd L E V E L

Define F contrast Define threshold at p<.05FWE Select “Data type’’ as Scalp- time

Result showing regions within epochs where faces and non faces differ reliably Maxima [ ] & [ ] Coordinates correspond to the left and right posterior sites at 180ms

Time-frequency analysis Transform data into frequency spectrum Ideal for induced responses i.e. responses not phase locked to the stimulus onset Different 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