Artifact (artefact) reduction in EEG –
and a bit of ERP basics
CNC, 19 November 2014
Jakob Heinzle
Translational Neuromodeling Unit
Overview
• Basic Principles of ERP recording (Luck
Chapter 3)
• Averaging, Artifact Rejection and Artefact
Correction (Chapter 4)
• A multiple source approach to the
correction of eye artifacts (Berg and
Scherg, 1994)
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Hansen’s axiom
• “There is no substitute for good data!”
• Get your data “free of noise” during
recording already.
– No electromagnetic contamination (Faraday
cages, no screens inside etc.)
– No eye movements, no muscle artifacts, no
sweating (Instruct subjects and make it
comfortable for them.)
– No bridging etc. (careful setup of caps etc.)
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Basics of ERP (EEG) recording
• Electrodes (Ground and Reference)
– Often Mastoid reference (average over both
mastoids)
– Signal is A – (Lm/2 + Rm/2), where all A, Lm
and Rm are voltages relative to ground.
– Sometimes average reference.
• Typical size of ERP is about 10 mV
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EEG electrodes
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Sources of noise
• Everything that can cause a voltage
difference between two electrodes and is
not of “brain origin”
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Environmental noise
• Electrical noise in the environment
– power line AC (50 Hz), Video monitors
(refresh rate), Impedance changes at
electrodes, bridges, …
• Reduce noise as much as possible
– Faraday cages, shielded room, etc.
– Reduce impedance at electrodes (gel, scratch
surface of skin, …)
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Amplification, Filtering and Digitization
• Active amplifiers increase signal to range
that is then digitized into 4096 (212)
discrete steps.
– Set gain of amplifier to use entire range
• High pass filtering of signal (often 0.01 Hz)
• Sampling rate depends on low pass filter of
amplifier  Nyquist.
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Averaging
• In most cases ERP signals are averaged.
– Assumptions: Signal always the same and
only EEG noise varies from trial to trial.
– If noise is independent of ERP it is reduced by
a factor 1/sqrt(n)
• “It is usually much easier to improve the
quality of your data by decreasing sources
of noise than by increasing the number of
trials.”
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Averaging
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Latency variability
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Overlap between trials
Problematic if different
for different trial types.
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Averaging
• Area measures are less sensitive to
latency variability.
• Response locked averaging.
• Woody filter. Iterative template matching,
template calculation technique.
• Time locked spectral averaging.
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Time locked spectral averaging
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Steady state ERP
Use overlap and drive responses into a steady state.
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Typical artefacts from participant
• Eye blinks
• Eye movements
• Muscle activity
• Skin potentials
• Heart artefacts
• …
All of those can create large signals and
might be correlated with the task.
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Some examples
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How to deal with artefacts
• Artefact rejection: Remove all trials that
contain contaminated data.
• Artefact correction: Use all data, but try to
correct for the artefacts.
• But, best thing is always to avoid artefacts
as much as possible.
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Post-processing of artefacts
• Detecting artefacts is a signal detection
problem.
• Problem: Threshold for artefact detection.
Typical ROC type problem (True positive
vs. false positive)
In general: Define artifact measure, detect
artifacts, reject artifacts.
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Electric field of the eyes
http://www.bem.fi/book/28/28.htm
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Example: Blinks
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Eye movement artifact correction
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Basic idea – component model
• EEG data is modeled as sum of EEG and
eye artefact components.
• Spatial distribution (scalp distribution)
activated by a temporally evolving factor.
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What are the components?
• Eye components are derived from a
calibration session prior to the experiment.
– Eye movements into different directions and
blinks (every 2 secs).
– PCA on this data: 3 components explain 95%
of variance.
• EEG components are fitted dipole sources,
or combination of assumed dipoles.
– No details here, different paper of the authors.
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Different models
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Eye movement results
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Eye movement results
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Testing the method
Use “artefact free” data and data with
artefacts.
For both compare optimizing (dipole fitting),
surrogate and traditional method.
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fMRI results –
Visuomotor mismatch specific activation
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Residual variance in individual subjects
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Results - Maps
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Results - Maps
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Spatial accuracy (consistency)
Compared to uncorrected model without EOG electrodes.
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Results
• Optimized methods seems to be best
• Artefact rejection does not remove all eye
movement artefacts.
• Ground truth is not known, but they take
one of the fitted results to compare.
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ICA based artefact removal
• Independent component analysis (ICA) can be used to
find independent sources and exclude sources that come
from artifacts.
𝑥 𝑡 = 𝐴 ∙ 𝑠(𝑡)
• ICA assumes x(t) is a linear mixture of (maximally)
independent sources.
• For details see e.g.:
– ICA general: Hyvärinen and Oja, Neural Networks, 13(4-5):411-430, 2000
– ICA in EEG: Delorme et al, IEEE 2005 and many other papers from Scott
Makeig’s group.
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Some more sources
• Some EEG artifacts reviewed:
– https://www.youtube.com/watch?v=1LftSdvNXh0
• Web based EEG Atlas
– http://eeg.neurophysiology.ca
• Saccadic spike artefact in MEG
– Carl et al, Neuroimage 59:1657 2012
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