8: Artifact Detection and Rejection
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Learning Objectives
In this chapter, you will learn to:
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Conceptualize artifact rejection in terms of the overarching goal of accurately answering the scientific question that your experiment was designed to address
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Implement algorithms that work particularly well for detecting blinks, saccadic eye movements, and a broad class of artifacts termed
commonly rejected artifactual potentials
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Select optimal parameters for the artifact detection parameters
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Evaluate the effectiveness of your artifact rejection procedures on the averaged ERP waveforms, including both data quality and confounds
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Implement a two-stage artifact rejection procedure for ensuring that experiments with lateralized stimuli or lateralized responses are not contaminated by small but consistent eye movements
EEG recordings are often filled with large artifacts. In most areas of research, blinks are the most problematic. They’re large (often 200 µV), occur frequently (on over 50% of trials in many participants), and may differ systematically across groups or conditions, creating a significant confound if they aren’t properly addressed. In research with infants, small children, or people who are required to move around during the task, movement-related artifacts are also a major issue. In my own area of research, eye movements toward lateralized targets are the most significant artifact.
However, in most of the ERP papers I read that use artifact rejection, it doesn’t seem that much thought went into the strategy for dealing with artifacts. These papers typically use a very primitive algorithm for detecting trials with artifacts, and they use the same rejection threshold for all participants (even though artifacts differ quite a bit across individuals). This chapter is designed to help you conceptualize and implement artifact rejection in a more sophisticated manner, allowing you to minimize artifact-related confounds and maximize your data quality.