EEG artefacts


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EEG artefacts

  1. 1. EEG ARTEFACTS <ul><li>Dr Chris Brown </li></ul><ul><li>Manchester Cognitive Electrophysiology </li></ul>
  2. 2. What are artefacts? <ul><li>Unwanted electrical activity arising from different sources, other than cerebral activity. </li></ul>
  3. 3. What causes artefacts? <ul><li>The EEG is a highly sensitive recording device, easily interrupted by other electrical activity of very high voltages . </li></ul>
  4. 4. Identifying artefacts <ul><li>Some readily distinguished, others so closely resemble cerebral activity that their interpretation is taxing even to the most experienced electroencephalographer. </li></ul>
  5. 5. Removing artefacts <ul><li>Slow (0-4 Hz) and high (more 20 Hz) frequency bands of EEG may pick up artefacts, such as eye movements and muscle activity, and therefore should be evaluated with caution. </li></ul><ul><li>Despite the use of artefact rejection algorithms, the failure to accurately distinguish true physiological rhythmicity from artefacts requires expert assessment. </li></ul>
  6. 6. CLASSIFICATION <ul><li>Physiological : from patient’s own generator sources (other than the brain). </li></ul><ul><li>– Eye movement </li></ul><ul><li>– Muscle (EMG) </li></ul><ul><li>– Movement </li></ul><ul><li>– Cardiogenic </li></ul><ul><li>– Sweat </li></ul><ul><li>Extraphysiological : Externally generated e.g. instrumental & environmental. </li></ul><ul><li>- 50Hz </li></ul>
  7. 7. Cardiac artefacts <ul><li>ECG artefact - poorly formed QRS complex </li></ul><ul><li>Ballistocardiographic - movement of head or body with cardiac contractions. </li></ul><ul><li>Pacemaker - generalized across scalp, shorter duration. </li></ul><ul><li>Pulse - electrode resting on blood vessel, follows ECG artefact. </li></ul>
  8. 8. ECG artefacts
  9. 9. BCG artefacts
  10. 10. Pacemaker artefacts
  11. 11. Pulse artefacts
  12. 12. ELECTRODE ARTEFACTS <ul><li>Types: </li></ul><ul><li>Two forms: </li></ul><ul><ul><li>brief transients limited to one electrode, e.g. </li></ul></ul><ul><ul><ul><li>Electrode pop - spontaneous discharges. </li></ul></ul></ul><ul><ul><ul><li>Electrode/lead movement. </li></ul></ul></ul><ul><ul><li>low frequency rhythms across scalp region, e.g. </li></ul></ul><ul><ul><ul><li>Perspiration - undulating waves >2 sec. </li></ul></ul></ul><ul><ul><ul><li>Salt bridge - lower amplitude. </li></ul></ul></ul><ul><ul><ul><li>Movement artefact. </li></ul></ul></ul>
  13. 13. Lead movement
  14. 14. Sweat artefact
  15. 15. Salt bridge artefact
  16. 16. EXTERNAL DEVICE ARTEFACT <ul><li>50 Hz ambient electrical noise </li></ul><ul><li>Mechanical artefacts - ventilators, circulator pumps </li></ul>
  17. 17. 50 Hz artefact
  18. 18. 50Hz noise from the standard AC electrical line current <ul><li>This noise can be diminished by the proper grounding of the equipment (both computer and amplifiers). </li></ul><ul><li>It could be also eliminated by a so-called notch filter which selectively removes 50 Hz activity from the signal. </li></ul><ul><li>This noise could be attenuated by obtaining good contact of electrodes with the scalp. The electrode impedance less than 10 kOhms is desirable. </li></ul>
  19. 19. Electrical motor
  20. 20. Muscle artefact
  21. 21. Muscle and movement artefact
  22. 22. Muscle Artefact <ul><li>EMG artifact starts as low as 12 Hz and ranges to 300 Hz. Most of the spectrum lies between 30-150 Hz. </li></ul><ul><li>Sites F3, F4, T3, T4, P3, P4 can pick up EMG the masseter and temporalis muscles. </li></ul><ul><li>Posterior electrodes can pick up EMG from occipitalis, trapezius and supraspinal muscles. </li></ul><ul><li>To avoid this type of artefact one can relax or position the head properly. </li></ul><ul><li>Fz, Cz, Pz can give a relatively pure EEG signal. </li></ul>
  23. 23. OCULAR ARTEFACT <ul><li>Blink </li></ul><ul><li>Eye flutter </li></ul><ul><li>Lateral gaze </li></ul><ul><li>Slow/rowing eye movements </li></ul><ul><li>Lateral rectus spike </li></ul>
  24. 24. Ocular, blinks and electroretinal activity <ul><li>Eye movement and blinks artifacts occur in the delta range 0-4 Hz and occur over the anterior part of the scalp. </li></ul>
  25. 25. Blink artifact
  26. 26. Eye flutter artefact
  27. 27. Lateral eye movement
  28. 28. Slow eye movement
  29. 29. Removing artefacts: methods <ul><li>Regression-based approach: </li></ul><ul><ul><li>Regression analyses are used to define the amplitude relation between one or more electro-oculogram (EOG) channels and each EEG channel. </li></ul></ul><ul><ul><li>Correction involves subtracting the estimated proportion of the EOG from the EEG. </li></ul></ul><ul><ul><li>Caveat: Bidirectional contamination. If ocular potentials can contaminate EEG recordings, then brain electrical activity can also contaminate the EOG recordings. Therefore, subtracting a linear combination of the recorded EOG from the EEG may not only remove ocular artefacts but also interesting cerebral activity. </li></ul></ul><ul><ul><ul><li>Filtering methods exist to improve this – filtering out high frequency activity from the EOG prior to calculating regression coefficients. </li></ul></ul></ul>
  30. 30. Removing artefacts: methods <ul><li>Independent Component Analysis </li></ul><ul><ul><li>Transforms data collected at single scalp channels to spatially transformed &quot;virtual channels“. </li></ul></ul><ul><ul><li>The independent components (&quot;virtual channels“) are maximally temporally independent from each other. </li></ul></ul><ul><ul><li>These information sources may represent either: </li></ul></ul><ul><ul><ul><li>synchronous or partially synchronous activity within one (or possibly more) cortical patch(es) </li></ul></ul></ul><ul><ul><ul><li>activity from non-cortical sources (e.g., potentials induced by eyeball movements or produced by single muscle activity, line noise, etc.). </li></ul></ul></ul>
  31. 31. Practicalities in using ICA <ul><li>ICA works best when given a large amount of basically similar and mostly clean data. </li></ul><ul><li>When the number of channels (N) is large (>>32) then a very large amount of data may be required to find N components. </li></ul><ul><li>When insufficient data are available, find fewer than N components. </li></ul><ul><li>ICA will not work well if using concatenated data from </li></ul><ul><ul><li>radically different EEG states </li></ul></ul><ul><ul><li>different electrode placements </li></ul></ul><ul><ul><li>containing non-stereotypic noise </li></ul></ul>
  32. 32. Selecting artefactual ICA components <ul><li>What are the main criteria to determine if a component is brain-related or artefact? </li></ul><ul><ul><li>the component the scalp map </li></ul></ul><ul><ul><li>the component time course </li></ul></ul><ul><ul><li>the component activity power spectrum </li></ul></ul><ul><ul><li>(if event-related data epochs), the ”erp image”. </li></ul></ul>
  33. 34. Artefact or brain activity? <ul><li>Eye artefact for three reasons: </li></ul><ul><ul><li>The smoothly decreasing EEG spectrum (bottom panel) is typical of an eye artefact; </li></ul></ul><ul><ul><li>The scalp map shows a strong far-frontal projection typical of eye artefacts; </li></ul></ul><ul><ul><li>It is possible to see individual eye movements in the component ”erp image” (top-right panel). </li></ul></ul>
  34. 35. Artefact or brain activity? <ul><li>Muscle artefact , because: </li></ul><ul><ul><li>Spatially localized </li></ul></ul><ul><ul><li>High power at high frequencies (20-50 Hz and above). </li></ul></ul>
  35. 36. Artefact or brain activity? <ul><li>Line noise : </li></ul><ul><ul><li>Regular interference clear from trial 65 onwards. </li></ul></ul>
  36. 37. What does brain activity look like? <ul><li>Brain-related components have: </li></ul><ul><ul><li>Dipole-like scalp maps; </li></ul></ul><ul><ul><li>Spectral peaks at typical EEG frequencies (i.e., 'EEG-like' spectra); </li></ul></ul><ul><ul><li>Regular ERP-image plots (meaning that the component does not account for activity occurring in only a few trials). </li></ul></ul>
  37. 38. <ul><li>Brain activity : </li></ul><ul><ul><li>strong alpha band peak near 10 Hz; </li></ul></ul><ul><ul><li>scalp distribution compatible with a left occipital cortex brain source; </li></ul></ul><ul><ul><li>Regular ERP image plot. </li></ul></ul>Artefact or brain activity?
  38. 39. What if a component looks to be &quot;half artefact, half brain-related&quot;? <ul><li>Either: </li></ul><ul><ul><li>ignore it, and check it isn’t adversely affecting your ERPs to much. </li></ul></ul><ul><ul><li>try re-running ICA decomposition again on a cleaner data subset (e.g. after removing very noisy epochs/segments of data). Removing artefactual epochs containing one-of-a-kind artefacts is very useful for obtaining 'clean' ICA components.  </li></ul></ul><ul><ul><li>use other ICA training parameters. </li></ul></ul>
  39. 40. Optimal ICA strategy (according to online EEGlab tutorial) <ul><li>Visually reject unsuitable portions of the continuous data. </li></ul><ul><li>Separate the data into suitable short data epochs. </li></ul><ul><li>Perform ICA on these epochs to derive their independent components. </li></ul><ul><li>Reject short data epochs on the derived components. </li></ul><ul><li>Visually inspect and select data epochs for rejection. </li></ul><ul><li>Reject the selected data epochs. </li></ul><ul><li>Perform ICA a second time on the pruned collection of data epochs </li></ul><ul><ul><li>This may improve the quality of the ICA decomposition, revealing more independent components accounting for neural, as opposed to mixed artifactual activity. </li></ul></ul><ul><ul><li>If desired, the ICA unmixing and sphere matrices may then be applied to (longer) data epochs from the same continuous data. Longer data epochs are useful for time/frequency analysis, and may be desirable for tracking other slow dynamic features. </li></ul></ul><ul><li>Inspect and reject the components. Note that components should NOT be rejected before the second ICA, but after. </li></ul>