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Artifact (artefact) reduction in EEG – and a bit of ERP basics CNC, 19 November 2014 Jakob Heinzle Translational Neuromodeling Unit.

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Presentation on theme: "Artifact (artefact) reduction in EEG – and a bit of ERP basics CNC, 19 November 2014 Jakob Heinzle Translational Neuromodeling Unit."— Presentation transcript:

1 Artifact (artefact) reduction in EEG – and a bit of ERP basics CNC, 19 November 2014 Jakob Heinzle Translational Neuromodeling Unit

2 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) EEG artefacts2

3 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.) EEG artefacts3

4 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  V EEG artefacts4

5 EEG electrodes EEG artefacts5

6 Sources of noise Everything that can cause a voltage difference between two electrodes and is not of “brain origin” EEG artefacts6

7 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, …) EEG artefacts7

8 Amplification, Filtering and Digitization Active amplifiers increase signal to range that is then digitized into 4096 (2 12 ) 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. EEG artefacts8

9 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.” EEG artefacts9

10 Averaging EEG artefacts10

11 Latency variability EEG artefacts11

12 Overlap between trials EEG artefacts12 Problematic if different for different trial types.

13 Averaging Area measures are less sensitive to latency variability. Response locked averaging. Woody filter. Iterative template matching, template calculation technique. Time locked spectral averaging. EEG artefacts13

14 Time locked spectral averaging EEG artefacts14

15 Steady state ERP EEG artefacts15 Use overlap and drive responses into a steady state.

16 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. EEG artefacts16

17 Some examples EEG artefacts17

18 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. EEG artefacts18

19 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 artefacts19

20 Electric field of the eyes EEG artefacts20

21 Example: Blinks EEG artefacts21

22 Eye movement artifact correction EEG artefacts22

23 Basic idea – component model EEG artefacts23 EEG data is modeled as sum of EEG and eye artefact components. Spatial distribution (scalp distribution) activated by a temporally evolving factor.

24 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. EEG artefacts24

25 Different models EEG artefacts25

26 Eye movement results EEG artefacts26

27 Eye movement results EEG artefacts27

28 Testing the method EEG artefacts28 Use “artefact free” data and data with artefacts. For both compare optimizing (dipole fitting), surrogate and traditional method.

29 fMRI results – Visuomotor mismatch specific activation EEG artefacts29

30 EEG artefacts30

31 Residual variance in individual subjects EEG artefacts31

32 Results - Maps EEG artefacts32

33 Results - Maps EEG artefacts33

34 Spatial accuracy (consistency) EEG artefacts34 Compared to uncorrected model without EOG electrodes.

35 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. EEG artefacts35

36 ICA based artefact removal Independent component analysis (ICA) can be used to find independent sources and exclude sources that come from artifacts. EEG artefacts36 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.

37 Some more sources Some EEG artifacts reviewed: – Web based EEG Atlas – Saccadic spike artefact in MEG –Carl et al, Neuroimage 59:1657 2012 EEG artefacts37

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