1. Wellcome Dept. of Imaging Neuroscience University College London
SPM for EEG/MEG
Stefan Kiebel
Wellcome Dept. of Imaging Neuroscience
University College London

2. Overview: SPM5 for EEG/MEG
Statistical Parametric Mapping
voxel-based approach
Conventional analysis
Localisation of effects
Evoked responses and power
Spatial forward modelling/
Source reconstruction
Forward model important for source reconstruction and DCM
Source reconstruction localises activity in brain space
t-contrast: tests for single dimension in parameter space
F-contrast: tests for multiple dimensions
inference at first or second level (fixed or random effects)
over conditions or groups: main effect, difference, interaction:
average over time window
parametric modulation with extrinsic variable
power comparison in time-frequency domain
Dynamic Causal Modelling
Models ERP/ERF as network activity.
Explains differences between evoked responses as modulation of connectivity.

3. EEG and MEG EEG MEG - ~1929 (Hans Berger) - Neurophysiologists
In the same way like SPM for fMRI
Use SPM2‘s methods!
However, model needs to be adapted to make proper inference
Comparison with fMRI analysis to aid illustration
Also, conventional model (to be shown) in the same framework
- ~1929 (Hans Berger)
- Neurophysiologists
From clinical system
to 64, 127, 256 sensors
- Potential V: ~10 µV
- ~1968 (David Cohen)
- Physicists
From ~ 30 to more than sensors
- Magnetic field B: ~10-13 T

4.
275 sensor axial gradiometer MEG system supplied by VSM medtech.
VSM medtech says 
Designed for unprecedented measurement accuracy, the combination of up to 275 optimum-length axial gradiometers and unique noise cancellation technology ensures the highest possible performance in some of today's most demanding urban magnetic environments.
In the same way like SPM for fMRI
Use SPM2‘s methods!
However, model needs to be adapted to make proper inference
Comparison with fMRI analysis to aid illustration
Also, conventional model (to be shown) in the same framework

5. MEG data Example: MEG study of finger somatotopy
[Meunier 2001]
400 stimulations of each finger
right
In the same way like SPM for fMRI
Use SPM2‘s methods!
However, model needs to be adapted to make proper inference
Comparison with fMRI analysis to aid illustration
Also, conventional model (to be shown) in the same framework
~ 50 ms
left
Index f
Little f

6. event-related potential/field (ERP/ERF)
single trials
. . .
average
event-related potential/field (ERP/ERF)

7. Single trial/evoked response
Voxel spaces
SPM 2D
Single trial/evoked response
sensor data
SPM 3D

8. . . . . . . . . . . . . Data (at each voxel) Single subject
Multiple subjects
Subject 1
Trial type 1
. . .
. . .
Subject j
Trial type i
. . .
. . .
Subject m
Trial type n

9. Mass univariate Time Time model specification parameter estimation
hypothesis
statistic
single voxel
time series
Intensity
SPM

10. How does SPM/EEG work? Preprocessing Projection SPM5-stats SPM{t}
SPM{F}
Control of FWE
Raw
M/EEG data
2D - scalp
mass-univariate
analysis
Single trials
Epoching
Artefacts
Filtering
Averaging, etc.
In the same way like SPM for fMRI
Use SPM2‘s methods!
However, model needs to be adapted to make proper inference
Comparison with fMRI analysis to aid illustration
Also, conventional model (to be shown) in the same framework
3D-source
space

11. 2 level hierarchical model
SPM for M/EEG
Time and
time-frequency
contrasts
M/EEG data
Design matrices
2 level hierarchical model
2D- or 3D- M/EEG data
SPM{t}
SPM{F}
Preprocessing
fMRI/
sMRI
data
Covariance
constraints
Corrected
p-values

12. Conventional analysis: example
Average between 150 and 190 ms
Example: difference
in N170 component
between trial types
PST [ms] s1 a1 s2
Trial type 1 a2 s3 a3
General linear model
(here: 2-sample t-test) s1 a4 s2 a5
Trial type 2 s3 a6

13. Summary statistics approach
Example: difference
between trial types
Contrast: average between 150 and 190 ms
2nd level contrast
-1 1
. . . = = +
Identity
matrix
second level
first level

14. Gaussian Random Fields
t59
Control of
Family-wise error
Worsley et al., Human Brain Mapping, 1996
p = 0.05
Cluster
Gaussian 10mm FWHM
(2mm pixels)
Search volume

15. Summary Conventional preprocessing in sensor space.
After preprocessing, convert to voxel-space.
Adjustment of p-values!
Analysis of power or time data.
t-contrast: tests for single dimension in parameter space
F-contrast: tests for multiple dimensions
inference at first or second level (fixed or random effects)
over conditions or groups: main effect, difference, interaction:
average over time window
parametric modulation with extrinsic variable
power comparison in time-frequency domain
SPM needed to get to the DCM bit.
Cool source reconstruction.