Artifact (artefact) reduction in EEG – and a bit of ERP basics
CNC, 19 November 2014
Jakob Heinzle
Translational Neuromodeling Unit
EEG artefacts
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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EEG artefacts
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.
EEG artefacts
Electric field of the eyes
http://www.bem.fi/book/28/28.htm
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EEG artefacts
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.
EEG artefacts
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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