Chapter 8: EEG Artifacts, Their Detection, Influence, and Removal

This chapter goes over EEG artifacts, what they look like in data and the problems they cause, and how to remove them (and ramifications of trying to do so). Noise generally stems from the physical (eye movements, blinks, amplifier saturation), but also the cognitive (irrelevant processing). While this chapter discusses how to attenuate a lot of it, it is stressed that EEG ultimately is not a noise free environment.

8.1 Removing Data Based on Independent Components Analysis

Independent Components Analysis is a technique where one decomposes the EEG time series data into a set of independent components for the purposes of identifying independent sources of variants. Real world example would be using multiple microphones around a room of talking people, and using the weighted averages of the sounds from each microphone to deduce who is saying what. The result of independent component analysis would be a set of weights for each microphone such that the weighted sum of all the microphones isolates the voice of one person. With EEG, independent components analysis results in a set of weights of all electrodes such that each component is a weighted sum of activity at all electrodes, that can be used to isolate sources of brain electrical signals.

This can be used to clean EEG data by identifying components that hold artifacts. This can be judged via topography, and time-domain /frequency spectrum. However, it is never truly possible to completely separate noise and data. Its best to remain conservative and only remove what is clearly noise (such as with blink artifacts, which are the easiest to identify due to their topographical and time based characteristics).

[Figure 8.1]

The maximum number of components is the number of electrodes used in the experiment. Practically speaking, if you have many electrodes, it may be reasonable to pick a smaller number of components.

8.2 Removing Trials because of Blinks

It is not always practical to remove every trial that happens to contain blinks. Thankfully, blinks do not destroy EEG signal, but linearly sum on-top of it, which means it can be removed relatively easily. This can be done with regression techniques, but also with independent components analysis, which is effective for blinks and ocular motor artifacts. 

Telling subjects to blink and specific times can cause increased cognitive load that may not be intended. However, if user blinks during a stimulus, it may impact their response to it. In addition, long blinks may indicate tiredness, in which the data may need to be rejected for that reason.

8.3 Removing Trials because of Oculomotor Activity