How to handle EEG artifacts?
How to handle EEG artifacts?
Noise and artifacts are an unavoidable aspect of EEG recordings. As such, handling them is an invaluable skill for EEG researchers to make the best of their data. In this article we present an overview of common artifacts together with tools and strategies for managing various types of artifacts in BrainVision Analyzer 2.
As EEG signals typically range in low amplitudes of tens of microvolts, they can be easily blurred by artifacts, reducing the signal to noise ratio. For example, activity from head muscles can overlap with oscillations from the brain, or movement of the cap creates distortions that affect the amplitude of an ERP. Technically speaking, artifacts add uncontrolled variability to the data, which confounds experimental observations. As such, even small artifacts can reduce statistical power in a study and alter results if they happen frequently.
EEG artifact handling strategies
Best efforts should always be made to prevent artifacts from entering EEG recordings. Nonetheless a certain level of artifact intrusion remains unavoidable, especially in the increasing field of mobile EEG and out-of-lab settings. But don’t worry, despite the unavoidability of artifacts, it is possible to obtain good quality signals with the right tools and artifact handling strategies.
EEG Physiological artifacts Eye blink ICA regression-based subtraction
The blink artifact is generated by the eye’s potential difference between the positively charged cornea and the negative retina: as the eyelid slides and the eyeball rotates during blinking the polarity is inverted and a positive current towards the scalp is created. The artifact is most prominent at frontal channels close to the eyes, reaching over a hundred microvolts in amplitude. In the frequency domain, it contains frequencies mostly in the EEG delta and theta bands.
EEG Physiological artifacts Eye movements artifact rejection ICA regression-based subtraction
Similar to blinks, lateral eye movements such as saccades generate a current away from the eyeballs, but this time towards the sides of the head, producing a box-shaped deflection with opposite polarity on each side. They are most prominent over channels close to the temples, but also affect channels outside these regions. In the frequency spectrum, the box shape created by eye movements peaks in the delta and theta bands, but has effects up to 20 Hz.
EEG Physiological artifacts Muscular artifacts
Most notably, activation during teeth clenching generates large noise that extends to the whole scalp. However, slighter artifacts are produced by other muscle groups of the head such as jaw or forehead.
Shoulder and neck tension lead to a persistent artifact that reaches lower electrodes including the mastoid region. In this case, if mastoid channels are used for re-referencing, the muscular artifact is introduced into all other channels. In frequency space, muscular artifacts are most prominent above 20 Hz, and up to 300 Hz.
EEG Physiological artifacts Pulse artifact
Pulsation of the head arteries generated by the heartbeat can lead to a slight rhythmical movement of the electrodes. In simultaneous EEG-fMRI recordings, the participant’s supine position and scanner environment magnify the artifact. However, pulse artifacts also occur sporadically under normal laboratory conditions, in participants with hypertension, or related to physical activity.
EEG Physiological artifacts Sweating - skin potentials
Even mild sweating leads to changes in the conductivity of the skin. This produces a drifting voltage on the scalp that shows as slow drifts in the recording. The drifts can vary widely in frequency and magnitude. This artifact is caused by warm environments, during physical activity, or during stress. The fluctuation induced by these drifts affects the timing and amplitude of signals in the time domain such as ERPs. In frequency space, this artifact contains power mostly in slow frequencies.
EEG Physiological artifacts Body movement
Movement of the body leads to slight displacement of the EEG cap over the scalp, especially when the cap is loose. This alters electrode impedance levels in the process, leading to artifacts. Gross movements produce large shifting voltages that can even saturate amplifiers momentarily. Slight body sway leads to gradual drift in the channels. Complex movements in certain tasks produce equally complex movements of the cap involving pulling, sliding and shaking, which affect all channels.
EEG Technical artifacts - Notch filters of 50 or 60 Hz
Line noise / Electromagnetic interference
As alternating current flows through the room’s electric wiring, it generates electromagnetic noise called “line noise”, that is picked up by the EEG cables with a frequency that depends on the local grid: 50 or 60 Hz depending on the country. Modern amplifiers do a great job in reducing this noise, however it can still enter the recording.
EEG Technical artifacts - Loose electrode contact
When the contact between the scalp and the electrode is disrupted, the signal becomes unstable. This can happen because of a loose-fitting cap, body movement or hair pushing the cap away. Loose contact more often leads to slow drifts in the signal. However, the electrochemical instabilities produced by the loose contact can lead to sudden conductance changes which manifest as an “electrode pop” in the data. This artifact can also affect ground and reference electrodes, which would consequently affect all other channels.
EEG Technical artifacts - Cable movement
Movement of the EEG cables alters their conductive properties momentarily. This produces transient signal alterations with varying shapes which depend on the type of cable movement. Most notably, cable swinging introduces oscillations at the frequency of the swing, which may overlap with EEG frequencies of interest. Modern EEG devices such as the actiCAP active electrode system, incorporate elements like amplification at the electrodes, which reduce cable movement artifacts
EEG Artifacts in EEG-fMRI TMS pulses tDCS and tACS
In simultaneous EEG-fMRI, the scanner environment induces diverse artifacts. Most notably, the magnetic gradient switching induces large currents in the EEG leads, which produce large artifactual voltages. Vibrations of the scanner further produce motion artifacts that are enhanced by the scanner’s magnetic field. Artifact handling in EEG-fMRI requires special techniques that cope with the particular nature of these artifacts.
TMS pulses generate large spikes that may lead to the saturation of the EEG amplifier. Effects on the hardware furthermore lead to a decay artifact for a few milliseconds. If there is amplifier saturation, it means that the EEG data is lost for a brief period, so the spike and decay are most frequently replaced by interpolated data.
Artifact rejection Raw Data Inspection and Artifact Rejection Transformation
Manual Inspection allows the user to scroll through the data and mark artifacts. This mode offers great flexibility but the artifact demarcation is subjective and can be very time consuming. Manual inspection is most useful for marking conspicuous artifacts, or for short data sets.
Automatic Inspection lets the user define a set of objective criteria to search for artifacts automatically:
Gradient: detects steep changes in voltage within one millisecond (e.g. electrode pops).
Amplitude: looks for absolute voltages that surpass a threshold relative to zero (e.g. ocular artifacts, large muscular artifacts).
Max-Min: searches for relative changes in amplitude beyond a defined range. Useful to find artifacts within data that already contains an offset (e.g. within drifts, DC offset).
Low Activity: searches for flat lines by finding stretches of data with unnaturally little variation
EEG Artifact rejection - Semi-Automatic Inspection
Semi-Automatic Inspection lets the user verify the artifact detection achieved through automatic criteria, by adding or removing artifact markings to the selection. It then combines the objectivity of automatic inspection with the flexibility of the manual mode.
EEG Artifact rejection Rejecting and interpolating channels
Sometimes a channel has an irreparably noisy signal throughout the entire recording. In this unfortunate situation, one option is to reject the channel entirely. In Analyzer this can be done with the Edit Channels transformation, where the channel in question can be disabled. An alternative approach is to replace the channel with an interpolated signal based on all other channels. This can be achieved through the Topographic Interpolation transformation. However, here the interpolated signal is only an estimation and should only be carefully interpreted, if at all.
EEG Independent Component Analysis ICA
EEG recordings include a mixture of signals from brain and non-brain sources. The ICA transformation in Analyzer seeks to statistically separate this mixture into its components. With the Inverse ICA transformation, the components that represent artifacts can be discarded, to reconstruct the EEG without them. These are most commonly ocular artifacts, but can also be heartbeat, localized muscular tension or other artifacts that have a single source.
For optimal component separation, at least 64 channels are recommended. You must further be careful to adequately select the components to discard without removing valid EEG. Analyzer offers a user-friendly visual display to facilitate these decisions. Even after removing components, Inverse ICA can be reprocessed to change the component selection.
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