EEG / MEG: Experimental Design

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EEG / MEG Experimental Design Preprocessing

• Alexandra Hopkins
• Jennifer Jung

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Outline

• Experimental 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

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MEG vs. EEG

• Both EEG and MEG signals arise from direct
  neuronal activity
• -gt postsynaptic dendritic potentials
• Electric field is distorted by changes in
  conductivity across different layers unlike
  magnetic field
• High temporal resolution ms.

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Sources of M/EEG signals

• 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

gyrus
sulcus
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Two types of MEG/EEG analysis
Event related changes (EP / ERP ERF)
Oscillatory activity cortical rhythms
(Time-frequency analysis)
Time locked to stimulus
Otten, L. (2012, November 21). EEG/MEG
Acquisition, Analysis and Interpretation, MSc
Cognitive Neuroscience, UCL
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Event Related Changes
pre-stim
post-stim
Repeats at same time
Averaging
evoked response
When response is time locked - signal averages
in!
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Evoked vs. Induced
Average trial by trial
With jitter effect - signal averages out!
(Hermann et al. 2004)
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Oscillatory activity
active awake state
resting state
falling asleep
sleep
deep sleep
coma
50 uV
1 sec
ongoing rhythms
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Oscillations

• 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

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(8 12or 13 Hz)

• Cortical and behavioral deactivation or
  inhibition
• Closed eyes

(12 30 Hz)
Alert, REM sleep Attention, and higher cognitive
function

• (30 80 Hz)

Visual awareness Binding of information Encoding,
retention and
(0 4 Hz)
Attentional and syntactic language processes Deep
sleep
(4 8 Hz)
Codes locations in space, navigation Declarative/e
pisodic memory processes Successful memory
encoding
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EP vs. ERP / ERF

• Evoked potential (EP)
• sensory processes
• short latencies (lt 100ms)
• small amplitudes (lt 1µV)
• Event related potential (EEG) / event related
  field (ERF)
• higher cognitive processes
• longer latencies (100 600ms),
• higher amplitudes (10 100µV)

used interchangeably in general
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ERP/ ERF
Averaging

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

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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

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Inferences Not Based On Prior Knowledge
Observation
Inference

• 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

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Observed vs Latent Components
Latent components
Observed waveform
OR
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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
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How quickly can the visual system differentiate
between different classes of object?
Component-independent experimental designs
Thorpe et al (1996)
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Design Strategies

• 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)

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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

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Signal-to-Noise Ratio

• Size of the noise in average (1/vN) 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

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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)

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Combining Techniques

• Why? How?
• Converging evidence, generative models
• fMRI EEG, fMRI MEG
• Drawbacks
• Signal interference
• Complex experimental design

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Outline

• Experimental 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

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PREPROCESSING IN SPM12

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

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Conversion of data

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

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Data Conversion

• 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

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Data Conversion - Example

• 128 channels selected
• Unusually flat because data contain very low
  frequencies and baseline shifts
• Viewing all channels only with a low gain

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Downsampling

• Sampling frequency is very high at acquisition
  (e.g. 2048 Hz)
• Downsampling is required for efficient data
  storage
• Sampling rate gt 2 x highest frequency in the
  signal of interest The Nyquist frequency

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Downsampling
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Aliasing
Sampling below Nyquist frequency will introduce
artefacts known as aliases.
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Downsampling SPM 12 Interface

• Downsampling reduces the file size and speeds up
  the subsequent processing steps
• At least 2x low pass filter
• e.g. 1000 to 200 Hz.

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Montaging Referencing

• Montage - representation of EEG channels
• Referential montage - have a reference electrode
  for each channel
• Identify vEOG and hEOG channels, remove several
  channels that dont 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

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RE-referencing
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Montage Referencing SPM 12 Interface
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Montage Referencing SPM 12 Interface
Review channel mapping
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Epoching
Cut out chunks of continuous data ( single
trials, referenced to stim onset)
EEG1
EEG2
EEG3
Event 1
Event 2
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Epoching

• Specify time
• e.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

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Epoching SPM 12 Interface
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Filtering

• 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

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Types of Filters

• 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.

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Examples of Filters
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Bandpass Filter
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Filtering SPM 12 Interface
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Artefacts
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Removing Artefacts
EASY

• 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

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Robust Averaging
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Removing Artefacts
HARDER

• Use your EoG!
• Regress out of your signal
• Use Independent Component Analysis (ICA)
• Eyeblinks are very stereotyped and large
• Usually 1st component

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Special thanks to our expertsBernadette and
Vladimir Litvak
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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/ldt
  ien/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