EEG / MEG:
Experimental Design & Preprocessing
Lena Kästner
Thomas Ditye
Outline
Experimental Design
Preprocessing in SPM8
•
•
•
•
•
•
•
•
•
•
•
•
•
Technology
Signal
Inferences
Design
Limitations
Combined Measures
Data Conversion
Montage Mapping
Epoching
Downsampling
Filtering
Artefact Removal
Referencing
Outline
Experimental Design
Preprocessing in SPM8
•
•
•
•
•
•
•
•
•
•
•
•
•
Technology
Signal
Inferences
Design
Limitations
Combined Measures
Data Conversion
Montage Mapping
Epoching
Downsampling
Filtering
Artefact Removal
Referencing
Technology | Signal | Inferences | Design | Limitations | Combined Measures
EEG & MEG
Hans Christian Orsted (1819)
Hans Berger (1924)
David Cohen (1968)
Technology | Signal | Inferences | Design | Limitations | Combined Measures
Electricity & Magnetism
apical dendrites of pyramidal cells act as dipoles
Technology | Signal | Inferences | Design | Limitations | Combined Measures
Why use EEG / MEG?
Technology | Signal | Inferences | Design | Limitations | Combined Measures
Oscillations
• alpha (3 – 18Hz):
awake, closed eyes
• beta (18 – 30Hz):
awake, alert; REM sleep
• gamma (> 30Hz):
memory (?)
• delta (0.5 – 4 Hz):
deep sleep
• theta (4 – 8Hz):
infants, sleeping adults
Technology | Signal | Inferences | Design | Limitations | Combined Measures
EP vs. ERP / ERF
• evoked potential
– short latencies (< 100ms)
– small amplitudes (< 1μV)
– sensory processes
• event related potential / field
– longer latencies (100 – 600ms),
– higher amplitudes (10 – 100μV)
– higher cognitive processes
Technology | Signal | Inferences | Design | Limitations | Combined Measures
Okay, But What Is It?
Stimulus/Event
Onset
average potential / field at the scalp relative to some specific event
Technology | Signal | Inferences | Design | Limitations | Combined Measures
Okay, But What Is It?
Averaging
non-time locked activity (noise) lost via averaging
Technology | Signal | Inferences | Design | Limitations | Combined Measures
Evoked vs. Induced
(Hermann et al. 2004)
Technology | Signal | Inferences | Design | Limitations | Combined Measures
ERS & ERD
• event related synchronization
– oscillatory power increase
– associated with activity decrease?
• event related desynchronization
– oscillatory power increase
– associated with activity increase?
long time windows, not phase-locked
Technology | Signal | Inferences | Design | Limitations | Combined Measures
Inferences Not Based On Prior Knowledge
observe:
infer:
• time course …
• amplitude …
• distribution across scalp …
• timing …
• degree of engagement …
• functional equivalence …
differences in ERP
of underlying cognitive process
Technology | Signal | Inferences | Design | Limitations | Combined Measures
Inferences Not Based On Prior Knowledge
(Rugg & Curran 2007)
Technology | Signal | Inferences | Design | Limitations | Combined Measures
Inferences Based On Prior Knowledge
An “ERP component is scalp-recorded electrical activity that is generated in a given
neuroanatomical module when a specific
computational operation is performed.”
(Luck 2004, p. 22)
Technology | Signal | Inferences | Design | Limitations | Combined Measures
Observed vs. Latent Components
Latent Components
OR
OR
many others…
Observed Waveform
Technology | Signal | Inferences | Design | Limitations | Combined Measures
Observed vs. Latent Components
Latent Components
Observed Waveform
Technology | Signal | Inferences | Design | Limitations | Combined Measures
Design Strategies
•
•
•
•
•
•
focus on specific, large, easily isolable component
use well-studied experimental manipulations
exclude secondary effects
avoid stimulus confounds (conduct control study)
vary conditions within rather than between trials
avoid behavioral confounds
Technology | Signal | Inferences | Design | Limitations | Combined Measures
Sources of Noise in EEG
• EEG activity not elicited by stimuli
– e.g. alpha waves
• trial-by-trial variations
• articfactual bioelectric activity
– eye blinks, eye movement, muscle activity, skin potentials
• environmental electrical activity
– e.g. from monitors
Technology | Signal | Inferences | Design | Limitations | Combined Measures
Signal-to-Noise
• noise said to average out
• number of trials:
–
–
–
–
large component: 30 – 60 per condition
medium component: 150 – 200 per condition
small component: 400 – 800 per condition
double with children or psychiatric patients
Technology | Signal | Inferences | Design | Limitations | Combined Measures
Limitations
• ambiguous relation between observed ERP and latent
components
• signal distorted en route to scalp
– arguably worse in EEG than MEG (head as “spherical
conductor”)
• MEG: application restrictions
– patients with implants
• poor localization (cf. “inverse problem”)
Technology | Signal | Inferences | Design | Limitations | Combined Measures
The Best of All – Combining Techniques?
• MEG & EEG
– simultaneous application
– complementary information about current sources
– joint approach to approximate inverse solution
… and how about fMRI?
Technology | Signal | Inferences | Design | Limitations | Combined Measures
The Best of All – Combining Techniques?
• EEG & fMRI
– simultaneous application
– e.g. spontaneous EEG-fMRI, evoked potential-fMRI
– problem: scanner artifacts
Technology | Signal | Inferences | Design | Limitations | Combined Measures
The Best of All – Combining Techniques?
• MEG & fMRI
– no simultaneous application
– co registration (scalp-surface matching)
– use structural scan:
infer grey matter position to constrain inverse solution
– run same experiment twice:
use BOLD activation map to bias inverse solution
Technology | Signal | Inferences | Design | Limitations | Combined Measures
Summary – General Design Considerations
•
•
•
•
•
large trial numbers, few conditions
avoid confounds
focus on specific effect, use established paradigm
take care when averaging
combined measures?
Technology | Signal | Inferences | Design | Limitations | Combined Measures
Summary – Specific EEG Considerations
•
•
•
•
•
amplifier and filter settings
sampling frequency
number, type, location of electrodes
reference electrodes
additional physiological measures?
Technology | Signal | Inferences | Design | Limitations | Combined Measures
Summary – Specific MEG Considerations
•
•
•
•
amplifier and filter settings
sampling frequency
equipment and participant compatible with MEG?
need to digitize 3D head or recording position?
Outline
Experimental Design
Preprocessing in SPM8
•
•
•
•
•
•
•
•
•
•
•
•
•
Technology
Signal
Inferences
Design
Limitations
Combined Measures
Data Conversion
Downsampling
Montage Mapping
Epoching
Filtering
Artefact Removal
Referencing
PREPROCESSING
Raw data to averaged ERP (EEG) or
ERF (MEG) using SPM 8
Conversion of data
Convert data from its native machine-dependent format to
MATLABbased SPM format
*.bdf
*.bin
*.eeg
*.mat (data)
‘just read’ – quick and easy
*.dat (other info)
define settings:
- read data as ‘continuous’ or as ‘trials’
- select channels
- define file name
•
•
•
•
128 channels
Unusually flat because data
contain very low frequencies
and baseline shifts
Viewing all channels only with a
low gain
Intensity rescaling
Downsampling
•
•
•
•
Sampling frequency: number of samples per second taken from a continuous signal
SF should be greater than twice the maximum frequency of the signal being sampled
Data are usually acquired with a very high sampling rate (e.g. 2048 Hz)
Downsampling reduces the file size and speeds up the subsequent processing steps
(e.g. 200 Hz)
Montage and referencing
•
•
Identify vEOG and hEOG channels, remove several channels that don’t carry EEG
data;
Specify reference for remaining channels:
• average reference: Output of all amplifiers are summed and averaged and the
averaged signal is used as a common reference for each channel
• single electrode reference: free from neural activity of interest (e.g. mastoid)
Epoching
•
•
•
•
•
Cut out chunks of continuous data (= single trials)
Specify time window associated with triggers [prestimulus time, poststimulus time]
Baseline-correction: automatic; the mean of the prestimulus time is subtracted from
the whole trial
Segment length: at least 100 ms for baseline-correction; the longer the more
artefacts
Padding: adds time points before and after each trial to avoid ‘edge effects’ when
filtering
For multisubject/batch
epoching in future
Filtering
•
•
•
EEG data consist of signal and noise
Some noise is sufficiently different in frequency content from the signal. It can be
suppressed by attenuating different frequencies.
Non-neural physiological activity (skin/sweat potentials); noise from electrical outlets
•
SPM8: Butterworth filter
•
•
Any filter distorts at least some part of the signal
Gamma band activity occupies higher fequencies
compared to standard ERPs
Reassignment of trial labels
Adding electrode locations
•
•
•
•
Not essential because SPM recognizes most common settings automatically
(extended 10/20 system)
However, these are default locations based on electrode labels
Actual location might deviate from defaults
Individually measured electrode locations can be imported and used as templates
Change/review 2D display
of electrode locations
1. Load file
2. Change/review channel
assignments
3. Set sensor positions
-Assign defaults
-From .mat file
-From user-written locations file
Artefact Removal
•
•
•
Artefacts: Eye movements, eye blinks, head movements, sweating, ‘boredom’ (alpha
waves), …
It’s best to avoid artefacts in the first place
• Blinking: avoid contact lenses; have short blocks and blink breaks
• EMG: make subjects relax, shift position, open mouth slightly
• Alpha waves: more runs, shorter length; variable ISI; talk to subjects
Removal
• Hand-picked
• Automatic SPM functions:
• Thresholding (e.g. 200 μV): 1st – bad channels, 2nd – bad trials
No change to data, just tagged
• Robust averaging: estimates weights (0-1) indicating how artefactual a
trial is
Excursus: Concurrent EEG/fMRI
•
MR gradient artefact:
• Very consistent because it’s caused by the scanner
• Averaged artefact waveform is created on the basis
of event markers
• Subtract template
•
Ballistocardogram (BCG) artefacts:
• Caused my small movements of the leads and
electrodes following cardiac pulsation
• Much less consistent
• PCA: Definition of a basis function by running PCA,
fitting, subtracting from data
•
SPM8 extension: FAST;
http://www.montefiore.ulg.ac.be/~phillips/FAST.html
Signal averaging
•
•
S/N ratio increases as a function of the square root of the number of trials
It’s better to decrease sources of noise than to increase number of trials
Visualization, stats, reconstruction, …
References
•
•
•
•
•
•
•
•
Ashburner, J. et al. (2010). SPM8 Manual. http://www.fil.ion.ucl.ac.uk/spm/
Hermann, C. et al. (2004). Cognitive functions of gammaband activity: memory match and
utilization. Trends in Cognitive Science, 8(8), 347-355.
Luck, R. L. (2005). Ten simple rules for designing ERP experiments. In T. C. Handy (Ed.), Eventrelated potentials: a methods handbook. Cambridge, MA: MIT Press.
Otten, L. J. & Rugg, M. D. (2005). Interpreting event-related brain potentials. In T. C. Handy (Ed.),
Event-related potentials: a methods handbook. Cambridge, MA: MIT Press.
Rippon, G. (2006). Electroencephalography. In C. Senior, T. Russell, & M. S. Gazzaniga (Eds.),
Methods in Mind.
Rugg, M.D. & Curran, T. (2007). Event-related potentials and recognition memory. Trends in
Cognitive Science, 11(6), 251-257.
Singh, K. D. (2006). Magnetoencephalography. In C. Senior, T. Russell, & M. S. Gazzaniga (Eds.),
Methods in Mind.
MfD slides from previous years
(with special thanks to Matthias Gruber and Nick Abreu for their EEG signal illustrations)
Thank You!
… and next week: contrasts, inference and
source localization