![]() For example, if participants are looking leftward in one condition and rightward in another condition, correcting for the EOG voltage produced by the eye movements doesn’t eliminate the confound of a different sensory input in the two conditions.At any level, students should recognize that users of technology have different needs and preferences and that not everyone chooses to use, or is able to use, the same technology products. ![]() Artifact correction doesn’t help at all with sensory input problems. That might be enough to produce a significant confound. For example, if correction reduces the blinks by 99%, the remaining blink activity would still be 1-2 µV in the frontal channels (because uncorrected blinks are typically 100-200 µV in these channels). Correction can also help with systematic confounds, but only to the extent that the correction fully removes the artifacts and doesn’t produce any new artifacts. However, we’ll switch to a visual experiment in the last part of the chapter to examine how ocular artifacts might alter the sensory input.Īrtifact correction can be much better than artifact rejection for addressing the problem of reduced statistical power, because we get to keep all of our epochs. The first exercises in this chapter will use data from an auditory experiment so that we won’t need to deal with this issue initially. Similarly, a deflection in the horizontal EOG can mean that the eyes weren’t pointed at the center of the display. For example, if a blink occurs just before or during the stimulus presentation, this means that the stimulus wasn’t actually seen by the participant. In visual experiments, EOG artifacts can indicate a problem with the sensory input. As we will see in one of the exercises in this chapter, this is not just a theoretical possibility. For example, if participants blink more in response to deviant stimuli than in response to the standards, we will see a difference between deviants and standards in the averaged ERPs that is due to EOG activity rather than to brain activity. Artifacts can produce systematic confounds in our studies. As a result, we need to balance the need to eliminate epochs with large artifacts with the need to include as many epochs as possible. However, when we reject epochs containing artifacts, we have fewer epochs in our averages, and that also makes the averages noisier and decreases our power. This makes our amplitude and latency measurements less precise, which in turn decreases our statistical power. Artifacts add noise to the data, reducing the signal-to-noise ratio (SNR) of our averaged ERPs. There are three common ways in which artifacts can be problematic from this perspective: We therefore reject epochs that problematic artifacts, defined as artifacts that interfere with the fundamental goal described in the previous chapter: accurately answering the scientific question that the experiment was designed to address. If we rejected every epoch containing an artifact, we wouldn’t have any data left. ![]() That is, the scalp EEG signal is always a mixture of brain activity, non-neural biological signals (e.g., skin potentials, EMG), and non-biological signals (e.g., line noise from nearby electrical devices). Obviously, we don’t want any artifacts in the epochs that we will be using to make our averaged ERPs! However, every single time point in every scalp EEG recording in human history contains artifactual activity. Let’s start by asking why we reject epochs containing artifacts.
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