eeg / meg: experimental design & preprocessing lena kästner thomas ditye

EEG / MEG:Experimental Design & Preprocessing

Lena KästnerThomas Ditye

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


Why use EEG / MEG?


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


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


Okay, But What Is It?

average potential / field at the scalp relative to some specific event

Stimulus/EventOnset


Okay, But What Is It?

non-time locked activity (noise) lost via averaging

Averaging


Evoked vs. Induced

(Hermann et al. 2004)


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


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


Inferences Not Based On Prior Knowledge

(Rugg & Curran 2007)


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)


Observed vs. Latent Components

Latent Components Observed Waveform

OR

OR

many others…


Observed vs. Latent Components

Latent Components Observed Waveform


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


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


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


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”)


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?



• EEG & fMRI– simultaneous application– e.g. spontaneous EEG-fMRI, evoked potential-fMRI– problem: scanner artifacts



• 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


Summary – General Design Considerations

• large trial numbers, few conditions • avoid confounds• focus on specific effect, use established paradigm• take care when averaging• combined measures?


Summary – Specific EEG Considerations

• amplifier and filter settings• sampling frequency• number, type, location of electrodes• reference electrodes• additional physiological 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

• Technology• Signal• Inferences• Design• Limitations• Combined Measures

Preprocessing in SPM8

• 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

*.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

• 128 channels

• Unusually flat because data contain very low frequencies and baseline shifts

• Viewing all channels only with a low gain

• Intensity rescaling

• 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)

Downsampling

• 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)

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

• 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

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

• 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 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 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

Excursus: Concurrent EEG/fMRI

http://www.montefiore.ulg.ac.be/~phillips/FAST.html

• 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)

http://www.fil.ion.ucl.ac.uk/spm/

Thank You!

… and next week: contrasts, inference and source localization

eeg / meg: experimental design & preprocessing lena kästner thomas ditye

Documents

design strategies

measures preprocessing

activity increase

activity decrease

sensory processes event

use eeg meg

eeg meg hans berger

nontime locked activity