EEG / MEG:Experimental Design & Preprocessing
Lone HørlyckMarion Oberhuber
Outline
Experimental Design
• Technology• Signal• Inferences• Design• Limitations• Combined Measures
Preprocessing in SPM8
• Data Conversion• Montage Mapping• Epoching• Downsampling• Filtering• Artefact Removal• Referencing
Outline
Experimental Design
• Technology• Signal• Inferences• Design• Limitations• Combined Measures
Preprocessing in SPM8
• Data Conversion• Montage Mapping• Epoching• Downsampling• Filtering• Artefact Removal• Referencing
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?
average potential / field at the scalp relative to some specific event
Stimulus/EventOnset
Technology | Signal | Inferences | Design | Limitations | Combined Measures
Okay, But What Is It?
non-time locked activity (noise) lost via averaging
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:
• time course …• amplitude …• distribution across scalp …
differences in ERP
infer:
• timing …• degree of engagement …• functional equivalence …
of underlying cognitive process
Technology | Signal | Inferences | Design | Limitations | Combined Measures
Inferences Based On Prior Knowledge
An “ERP component is scalp-recorded elec-trical 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 Observed Waveform
OR
OR
many others…
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?
Technology | Signal | Inferences | Design | Limitations | Combined Measures
Outline
Experimental Design
• Technology• Signal• Inferences• Design• Limitations• Combined Measures
Preprocessing in SPM8
• Data Conversion• Downsampling• Montage Mapping• Epoching• Filtering• Artefact Removal• Referencing
PREPROCESSINGRaw 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
*.mat (data)
*.dat (other info)
*.bdf*.bin*.eeg
‘just read’ – quick and easy
define settings:- read data as ‘continuous’ or as ‘trials’- select channels- define file name
• Sampling frequency: number of samples per second taken from a continuous signal• 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)• SF should be greater than twice the maximum frequency of the signal being sampled
Downsampling
• Identify vEOG and hEOG channels, remove several channels that don’t carry EEG data;
• Specify reference for remaining channels: • single electrode reference: free from neural activity of interest• average reference: Output of all amplifiers are summed and averaged and the
averaged signal is used as a common reference for each channel
Montage and referencing
• 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
Epoching
For multisubject/batch epoching in future
Epoching
• 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• High-, low-, stop-, bandpass filter
• Any filter distorts at least some part of the signal• Gamma band activity occupies higher fequencies
compared to standard ERPs
Filtering
Reassignment of trial labels
• 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
Adding electrode locations
1. Load file
2. Change/review channel assignments
3. Set sensor positions-Assign defaults-From .mat file-From user-written locations file
Change/review 2D display of electrode locations
Artefact Removal
•Eye movements
•Eye blinks
•Head movements
•Muscle activity
•Skin potentials
•‘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 trialsNo change to data, just tagged
• Robust averaging: estimates weights (0-1) indicating how artefactual a trial is
Artefact Removal
• MR gradient artefact: • Very consistent because it’s caused by the scanner• Averaged artefact waveform template is created and
substracted from EEG data
• Ballistocardiogram (BCG) artefacts:• Caused by small movements of the leads and
electrodes following cardiac pulsation• Much less consistent• Subtracting basic function from data
• SPM8 extension: FAST; http://www.montefiore.ulg.ac.be/~phillips/FAST.html
Excursus: Concurrent EEG/fMRI
• 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
Signal averaging
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.), Event-
related 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
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