7: Inspecting the EEG and Interpolating Bad Channels
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Learning Objectives
In this chapter, you will learn to:
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Inspect the raw data to determine what problems exist that might require intervention
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Determine whether an EEG channel is “bad” and replace the bad channel with interpolated values
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Identify “by eye” the most common types of artifacts, including blinks, saccadic eye movements, and muscle noise
I often say that it’s a miracle meaningful brain activity can be recorded from electrodes on the skin overlying the skull. Although the EEG is miraculous, it's also an imperfect measure of brain activity. There are many types of artifacts that can contaminate the data, and some of the electrodes may have poor electrical connections to the scalp, creating
bad channels
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Several preprocessing steps are necessary prior to averaging so that artifacts and bad channels don’t lead you to draw incorrect conclusions from the ERPs. In particular, we can replace the data from bad channels by interpolation from the good channels, and we can apply artifact rejection and correction to minimize the impact of artifacts.
Prior to performing these steps, it is
extremely important
that you first perform a visual inspection of a given participant’s EEG so that you understand what kinds of problems are present in that dataset. You also need to spend a lot of time looking at raw EEG data when you are learning to perform preprocessing so that you learn how to identify the most common types of problems.
The goal of this chapter is to teach you how visually inspect EEG data and how to identify bad channels and common artifacts. You’ll also learn how to replace bad channels with interpolated data. The subsequent chapters will cover artifact rejection and artifact correction. We’ll look at data from one participant in the mismatch negativity (MMN) experiment from the ERP CORE (Kappenman et al., 2021).