artifact (artefact) reduction in eeg – and a bit of erp basics cnc, 19 november 2014 jakob heinzle...

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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