EEG artefacts arise from unwanted electrical activity from sources other than the brain, such as eye movements, muscle activity, and environmental noise. Identifying artefacts can be challenging as some resemble brain activity. Methods for removing artefacts include filtering, regression-based approaches, and independent component analysis, which transforms scalp channel data into spatially independent sources that may represent brain or non-brain activity. Careful inspection of component properties like scalp maps, time courses, and spectra is needed to classify them as representing brain activity or artefacts.

1. EEG ARTEFACTS Dr Chris Brown Manchester Cognitive Electrophysiology

2. What are artefacts? Unwanted electrical activity arising from different sources, other than cerebral activity.

3. What causes artefacts? The EEG is a highly sensitive recording device, easily interrupted by other electrical activity of very high voltages .

4. Identifying artefacts Some readily distinguished, others so closely resemble cerebral activity that their interpretation is taxing even to the most experienced electroencephalographer.

5. Removing artefacts 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. Despite the use of artefact rejection algorithms, the failure to accurately distinguish true physiological rhythmicity from artefacts requires expert assessment.

6. CLASSIFICATION Physiological : from patient’s own generator sources (other than the brain). – Eye movement – Muscle (EMG) – Movement – Cardiogenic – Sweat Extraphysiological : Externally generated e.g. instrumental & environmental. - 50Hz

7. Cardiac artefacts ECG artefact - poorly formed QRS complex Ballistocardiographic - movement of head or body with cardiac contractions. Pacemaker - generalized across scalp, shorter duration. Pulse - electrode resting on blood vessel, follows ECG artefact.

8. ECG artefacts

9. BCG artefacts

10. Pacemaker artefacts

11. Pulse artefacts

12. ELECTRODE ARTEFACTS Types: Two forms: brief transients limited to one electrode, e.g. Electrode pop - spontaneous discharges. Electrode/lead movement. low frequency rhythms across scalp region, e.g. Perspiration - undulating waves >2 sec. Salt bridge - lower amplitude. Movement artefact.

13. Lead movement

14. Sweat artefact

15. Salt bridge artefact

16. EXTERNAL DEVICE ARTEFACT 50 Hz ambient electrical noise Mechanical artefacts - ventilators, circulator pumps

17. 50 Hz artefact

18. 50Hz noise from the standard AC electrical line current This noise can be diminished by the proper grounding of the equipment (both computer and amplifiers). It could be also eliminated by a so-called notch filter which selectively removes 50 Hz activity from the signal. This noise could be attenuated by obtaining good contact of electrodes with the scalp. The electrode impedance less than 10 kOhms is desirable.

19. Electrical motor

20. Muscle artefact

21. Muscle and movement artefact

22. Muscle Artefact EMG artifact starts as low as 12 Hz and ranges to 300 Hz. Most of the spectrum lies between 30-150 Hz. Sites F3, F4, T3, T4, P3, P4 can pick up EMG the masseter and temporalis muscles. Posterior electrodes can pick up EMG from occipitalis, trapezius and supraspinal muscles. To avoid this type of artefact one can relax or position the head properly. Fz, Cz, Pz can give a relatively pure EEG signal.

23. OCULAR ARTEFACT Blink Eye flutter Lateral gaze Slow/rowing eye movements Lateral rectus spike

24. Ocular, blinks and electroretinal activity Eye movement and blinks artifacts occur in the delta range 0-4 Hz and occur over the anterior part of the scalp.

25. Blink artifact

26. Eye flutter artefact

27. Lateral eye movement

28. Slow eye movement

29. Removing artefacts: methods Regression-based approach: Regression analyses are used to define the amplitude relation between one or more electro-oculogram (EOG) channels and each EEG channel. Correction involves subtracting the estimated proportion of the EOG from the EEG. 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. Filtering methods exist to improve this – filtering out high frequency activity from the EOG prior to calculating regression coefficients.

30. Removing artefacts: methods Independent Component Analysis Transforms data collected at single scalp channels to spatially transformed "virtual channels“. The independent components ("virtual channels“) are maximally temporally independent from each other. These information sources may represent either: synchronous or partially synchronous activity within one (or possibly more) cortical patch(es) activity from non-cortical sources (e.g., potentials induced by eyeball movements or produced by single muscle activity, line noise, etc.). http://sccn.ucsd.edu/wiki/Chapter_09:_Decomposing_Data_Using_ICA

31. Practicalities in using ICA ICA works best when given a large amount of basically similar and mostly clean data. When the number of channels (N) is large (>>32) then a very large amount of data may be required to find N components. When insufficient data are available, find fewer than N components. ICA will not work well if using concatenated data from radically different EEG states different electrode placements containing non-stereotypic noise

32. Selecting artefactual ICA components What are the main criteria to determine if a component is brain-related or artefact? the component the scalp map the component time course the component activity power spectrum (if event-related data epochs), the ”erp image”.

34. Artefact or brain activity? Eye artefact for three reasons: The smoothly decreasing EEG spectrum (bottom panel) is typical of an eye artefact; The scalp map shows a strong far-frontal projection typical of eye artefacts; It is possible to see individual eye movements in the component ”erp image” (top-right panel).

35. Artefact or brain activity? Muscle artefact , because: Spatially localized High power at high frequencies (20-50 Hz and above).

36. Artefact or brain activity? Line noise : Regular interference clear from trial 65 onwards.

37. What does brain activity look like? Brain-related components have: Dipole-like scalp maps; Spectral peaks at typical EEG frequencies (i.e., 'EEG-like' spectra); Regular ERP-image plots (meaning that the component does not account for activity occurring in only a few trials).

38. Brain activity : strong alpha band peak near 10 Hz; scalp distribution compatible with a left occipital cortex brain source; Regular ERP image plot. Artefact or brain activity?

39. What if a component looks to be "half artefact, half brain-related"? Either: ignore it, and check it isn’t adversely affecting your ERPs to much. 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. use other ICA training parameters.

40. Optimal ICA strategy (according to online EEGlab tutorial) Visually reject unsuitable portions of the continuous data. Separate the data into suitable short data epochs. Perform ICA on these epochs to derive their independent components. Reject short data epochs on the derived components. Visually inspect and select data epochs for rejection. Reject the selected data epochs. Perform ICA a second time on the pruned collection of data epochs This may improve the quality of the ICA decomposition, revealing more independent components accounting for neural, as opposed to mixed artifactual activity. 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. Inspect and reject the components. Note that components should NOT be rejected before the second ICA, but after.