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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