EEG / MEG:Experimental Design & Preprocessing

Alexandra HopkinsJennifer Jung

OutlineExperimental Design

• fMRI M/EEG• Analysis

– Oscillatory activity– EP

• Design• Inferences• Limitations• Combined Measures

Preprocessing in SPM12

• Data Conversion• Montage Mapping• Epoching• Downsampling• Filtering• Artefact Removal• Referencing

MEG vs. EEG

● Both EEG and MEG signals arise from direct neuronal activity

-> postsynaptic dendritic potentials● Electric field is distorted by changes in

conductivity across different layers unlike magnetic field

● High temporal resolution ~ms.

Sources of M/EEG signals

gyrus

sulcus

● MEG sensors only detect tangential components of fields from cortical pyramidal neurons

● Less sensitive to deeper regions

● EEG signal consists of both tangential and radial components of fields

Two types of MEG/EEG analysis

Event related changes(EP / ERP – ERF)

Oscillatory activity – cortical rhythms (Time-frequency analysis)

Otten, L. (2012, November 21). EEG/MEG Acquisition, Analysis and Interpretation, MSc Cognitive Neuroscience, UCL

Time locked to stimulus

post-stimpre-stim

evoked response

Averaging

Event Related Changes

Repeats at same time

When response is time locked - signal averages in!

Evoked vs. Induced

(Hermann et al. 2004)With jitter effect - signal averages out!

Average trial by trial

resting state

falling asleep

sleep

deep sleep

coma

1 sec

50 uV

ongoing rhythms

activeawake state

Oscillatory activity

• Non-averaged data collected during continuous stimulation or task performance (or during rest) lends itself to analysis of spectral power.

• Signals can be decomposed into a sum of pure frequency components which gives information on the signal power at each frequency.

• i.e. We can do Fourier analysis and look at spectra (not-event related – break data in arbitrary segments and do some averaging

Oscillations

Cortical and behavioral deactivation or inhibitionClosed eyes

Alert, REM sleepAttention, and higher cognitive function

Attentional and syntactic language processesDeep sleep

Codes locations in space, navigationDeclarative/episodic memory processesSuccessful memory encoding

(8 – 12or 13 Hz)

(12 – 30 Hz)

(0 – 4 Hz)

(4 – 8 Hz)

(30 – 80 Hz)Visual awarenessBinding of informationEncoding, retention and

EP vs. ERP / ERF• Evoked potential (EP)

– sensory processes– short latencies (< 100ms)– small amplitudes (< 1μV)

• Event related potential (EEG) / event related field (ERF)– higher cognitive processes– longer latencies (100 – 600ms),– higher amplitudes (10 – 100μV)

used interchangeably in general

Non-time locked activity(noise) lost via averaging over trials

Averaging

ERP/ ERF

Experimental design

• Number of trials– EP: 120 trials, 15-20% will be excluded– Oscillatory activity: 40-50 trials

• Duration of stimuli / task– Short: Averaged EP is fine– (Very) long: spectrotemporal analysis on averaged

EP or non-averaged data• Collecting Behavioral Responses

Inferences Not Based On Prior Knowledge

• Same ERP pattern

• Timing signals

• Distribution across scalp

• Differences in ERP across conditions and time

• Invariant patterns of neural activity from specific cognitive processes

• Timing of cognitive processes

• Degree of engagement

• Functional equivalence of underlying cognitive process

Observation Inference

Observed vs Latent Components

Latent componentsObserved waveform

OR

Design Strategies• Focus on specific, large and easily isolated

component– E.g., P3, N400, LRP, N2pc…

• Use well-studied experimental manipulations

- Similar conditions

• Component-independent experimental designs

- Very hard to study anything interesting

Luck, Ten Simple Rules for Designing and Interpreting ERP Experiments

How quickly can the visual system differentiate between different classes of object?

Thorpe et al (1996)

Component-independent experimental designs

• Avoid confounds and misinterpretations– Physical stimulus confounds

• Side effect– What you manipulated indirectly influences other things

• Vary conditions within rather than between blocks- Fatigue effect● Be cautious of behavioural confounds - Motor evoked potentials (MEPs)

Design Strategies

Sources of Noise in M/EEG● M/EEG activity not elicited by stimuli

– e.g. alpha waves → relaxed but alert ● Trial-to-trial variability in the ERP components - variations in neural and cognitive activity → trial by trial consistency ● Artefactual bioelectric activity - eye blinks, eye movement, cardiac and muscular activity, skin

potentials → keep electrode impedances low ● Environmental electrical activity - power lines, SQUID jumps, noisy, broken or saturated sensors →

shielding

Signal-to-Noise Ratio

• Size of the noise in average = (1/√N) ×R• 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: restrictions with magnetic implants

• Poor localization (cf. “inverse problem”)

• Why? How?– Converging evidence, generative models– fMRI + EEG, fMRI + MEG

• Drawbacks– Signal interference– Complex experimental design

Combining Techniques

OutlineExperimental Design

• fMRI M/EEG• Analysis

– Oscillatory activity– EP

• Design• Inferences• Limitations• Combined Measures

Preprocessing in SPM12

• Data Conversion• Montage Mapping• Epoching• Downsampling• Filtering• Artefact Removal• Referencing

PREPROCESSING IN SPM12

• Goal: get from raw data to averaged ERP (EEG) or ERF (MEG) using SPM12

Conversion of data

• Convert data from its native machine-dependent format to MATLAB based SPM format

*.mat(data)

*.dat(other info)

*.bdf*.bin*.eeg

Define settings:

•Read data as continuous or as trials (is raw data already divided into trials?)

•Select channels

•Define file name

•‘just read’ option is a convenient way to look at all the data quickly

Data Conversion

• 128 channels selected• Unusually flat because

data contain very low frequencies and baseline shifts

• Viewing all channels only with a low gain

*.mat (data)

*.dat (other info)

Data Conversion - Example

• Sampling frequency is very high at acquisition (e.g. 2048 Hz)

• Downsampling is required for efficient data storage

• Sampling rate > 2 x highest frequency in the signal of interest = The Nyquist frequency

Downsampling

Downsampling

Aliasing

Sampling below Nyquist frequency will introduce artefacts known as aliases.

•Downsampling reduces the file size and speeds up the subsequent processing steps

•At least 2x low pass filter e.g. 1000 to 200 Hz.

Downsampling: SPM 12 Interface

• Montage - representation of EEG channels • Referential montage - have a reference electrode for each

channel

• 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 e.g. Cz• Average reference: Output of all amplifiers are summed and

averaged and the averaged signal is used as a common reference for each channel, like a virtual electrode and less biased

Montaging & Referencing

RE-referencing

Montage & Referencing: SPM 12 Interface

Review channel mapping

Montage & Referencing: SPM 12 Interface

Cut out chunks of continuous data (= single trials, referenced to stim onset)

EEG1

EEG2

EEG3

Event 1 Event 2

Epoching

• Specify timee.g. 100 ms prestimulus - 600 ms poststimulus = single epoch/trial

• Baseline-correction: automatic; mean of the pre-stimulus time is subtracted from the whole trial

• Padding: adds time points before and after each trial to avoid ‘edge effects’ when filtering

Epoching

Epoching: SPM 12 Interface

• M/EEG data consist of signal and noise• Noise of different frequency; filter it out• Any filter distorts at least some part of the signal

but reduces file size• Focus on signal of interest - boost signal to noise

ratio• SPM12: Butterworth filter

• High-, low-, band-pass or bandstop filter

Filtering

•High-pass – filters out low-frequency noise, removes the DC offset and slow drifts in the data e.g. sweat and non-neural physiological activity

•Low-pass – remove high-frequency noise. Similar to smoothing e.g. muscle activity, neck

•Notch (band-stop) – remove artefacts limited in frequency, most commonly electrical line noise and its harmonics. Usually around 50/60Hz.

•Band-pass – focus on the frequency of interest and remove the rest. More suitable for relatively narrow frequency ranges.

Types of Filters

Examples of Filters

Bandpass Filter

Filtering: SPM 12 Interface

Artefacts

• Removal• Visual inspection - reject trials • 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

EASY

Removing Artefacts

Robust Averaging

• Use your EoG!• Regress out of your signal

• Use Independent Component Analysis (ICA)• Eyeblinks are very stereotyped and large• Usually 1st component

HARDER

Removing Artefacts

Special thanks to our expertsBernadette and Vladimir Litvak

References• Ashburner, J. et al. (2010). SPM8 Manual. http://www.fil.ion.ucl.ac.uk/spm/ • Hansen, C.P., Kringelbach M.L., Salmelin, R. (2010) MEG: An Introduction to Methods. Oxford

University Press,• Hermann, C. et al. (2004). Cognitive functions of gammaband activity: memory match and

utilization. Trends in Cognitive Science, 8(8), 347-355.• Herrmann, C. S., Grigutsch, M., & Busch, N. A. (2005). EEG oscillations and wavelet analysis. In T.

C. Handy (Ed.), Event-related potentials: A methods handbook (pp. 229-259). Cambridge, MA: MIT Press.

• Luck, S. J. (2005). Ten simple rules for designing ERP experiments. In T. C. Handy (Ed.), Event-related potentials: a methods handbook. Cambridge, MA: MIT Press.

• Luck, S. J. (2010). Powerpoint Slides from ERP Boot Camp Lectures. http://erpinfo.org/Members/ldtien/bootcamp-lecture-pptx

• Otten, L. (2012, November 21). EEG/MEG Acquisition, Analysis and Interpretation, MSc Cognitive Neuroscience, UCL

• 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..

• Sauseng, P., & Klimesch, W. (2008). What does phase information of oscillatory brain activity tell us about cognitive processes? [Review]. Neuroscience and Biobehavioral Reviews, 32(5), 1001-1013. doi: 10.1016/j.neubiorev.2008.03.014

• http://sccn.ucsd.edu/~jung/artifact.html• MfD slides from previous years