1 / 27

Methods for Dummies M/EEG Analysis: Contrasts, Inferences and Source Localisation

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

jameskcole
Download Presentation

Methods for Dummies M/EEG Analysis: Contrasts, Inferences and Source Localisation

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Methods for DummiesM/EEG Analysis:Contrasts, Inferences and Source Localisation Diana Omigie Stjepana Kovac

  2. 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

  3. 1 0 0 0 0 time 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

  4. This week Contrast and Inferences Source Reconstruction

  5. Contrasts and Inferences using SPM 8 ……Which buttons do we need to press?

  6. EEG data acquired on 128 channel ActiveTwo system sampled at 2048HzRandomised presentation of 86 faces and 86 scrambled faces Experimental Paradigm

  7. 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

  8. 2D Interpolation Transformation of discreet channels into a continuous 2D interpolated image of M/EEG signals Sensor Space Scalp Space

  9. 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)

  10. MULTI DIMENSIONALSCALP 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)

  11. MULTI DIMENSIONALSCALP 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)

  12. MULTI DIMENSIONALSCALP 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

  13. MULTI DIMENSIONALSCALP 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

  14. 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.

  15. SPM 8Steps 1stLEVEL

  16. 1stLEVEL New directory Faces & Scrambled faces 3D image file for each trial with dimension 32x 32x 161

  17. 1stLEVEL 3D IMAGE Sections through X and Y expressed over time 2D x-y space interpolated from the flattened electrode locations at one point in time

  18. 1stLEVEL 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

  19. 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.

  20. 2ndLEVEL

  21. 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.

  22. 2ndLEVEL Which Buttons Do we Need to Press?

  23. 2ndLEVEL Create a new directory Then To produce a batch window Select directory created Select two sample t test as design Make group 1 contain 1st type of trials Make group 2 contain other type. Save batch description Run batch window

  24. 2ndLEVEL 2 sample t-testDesign Matrix click click

  25. 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

  26. Transform data into frequency spectrum Time-frequency analysis 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

More Related