EEG ARTEFACTS

Dr Chris BrownManchester Cognitive

Electrophysiology

What are artefacts?

• Unwanted electrical activity arising from different sources, other than cerebral activity.

What causes artefacts?

• The EEG is a highly sensitive recording device, easily interrupted by other electrical activity of very high voltages .

Identifying artefacts

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

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.

CLASSIFICATIONPhysiological : 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

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.

ECG artefacts

BCG artefacts

Pacemaker artefacts

Pulse artefacts

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.

Lead movement

Sweat artefact

Salt bridge artefact

EXTERNAL DEVICE ARTEFACT

• 50 Hz ambient electrical noise• Mechanical artefacts - ventilators,

circulator pumps

50 Hz artefact

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.

Electrical motor

Muscle artefact

Muscle and movement artefact

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.

OCULAR ARTEFACT

• Blink• Eye flutter• Lateral gaze• Slow/rowing eye movements• Lateral rectus spike

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.

Blink artifact

Eye flutter artefact

Lateral eye movement

Slow eye movement

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.

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

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

Selecting artefactual ICA components

• What are the main criteria to determine if a component is brain-related or artefact?

1. the component the scalp map 2. the component time course3. the component activity power spectrum 4. (if event-related data epochs), the ”erp

image”.

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).

Artefact or brain activity?

• Muscle artefact, because:– Spatially localized – High power at high

frequencies (20-50 Hz and above).

Artefact or brain activity?

• Line noise:– Regular interference

clear from trial 65 onwards.

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).

• 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?

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.

Optimal ICA strategy(according to online EEGlab tutorial)

1. Visually reject unsuitable portions of the continuous data.2. Separate the data into suitable short data epochs.3. Perform ICA on these epochs to derive their independent

components.4. Reject short data epochs on the derived components. 5. Visually inspect and select data epochs for rejection.6. Reject the selected data epochs.7. Perform ICA a second time on the pruned collection of data

epochs1. This may improve the quality of the ICA decomposition,

revealing more independent components accounting for neural, as opposed to mixed artifactual activity.

2. 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.

8. Inspect and reject the components. Note that components should NOT be rejected before the second ICA, but after.