3.4: Exercise- “Bad” Data

So far, we’ve been looking at really clean data. However, the reality of ERP research (and most areas of human neuroscience) is that you often get some participants with really noisy data. And noisy data is the norm for most participants in some research areas (and large numbers of participants are needed to make up for it). For example, imagine trying to record the EEG from wiggly 2-year-olds. You’d get all kinds of movement artifacts, and they won’t sit through an hour of data collection the way a paid adult will. But you’ll also see some noisy data in studies of calm, compliant adults. So, no matter what kind of ERP research you’re interested in, you’ll need to deal with noisy data.

In this exercise, we’ll look at one of the 40 participants in the full N400 study whose data were problematic (Subject 30). This participant wasn’t horrible—all of our participants were college students who were pretty compliant with our instructions, and we know a lot of tricks for optimizing the data quality in EEG recordings (see Farrens et al., 2019 for a detailed description of our EEG recording protocol). However, the data from this participant were problematic in a way that we see in 5-10% of our college student population.

To see this participant’s data, set the current folder to the folder named Bad_Subject inside the Chapter_3 folder. This folder contains the original EEG dataset file, the EEG dataset file after all preprocessing steps (including artifact detection), and the averaged ERPset file.

Start by loading the averaged ERP datafile (30_N400.erp) from this participant using the ERPsets panel and selecting Bins 3 and 4 in the Bin & Channel Selection panel. You should see a very noisy waveform for the related target words, but the waveform for the unrelated target words is missing. If you look at the aSME data quality metric in the View Data Quality Metrics panel, you’ll find an aSME value of 0 for every channel at every time point for Bin 3 (related targets), and a value of NaN for Bin 4 (unrelated targets). NaN is an abbreviation for not a number, and it’s what Matlab uses when something can’t be computed (e.g., when it requires dividing by zero).

Now plot the ERP waveforms for the prime words (Bins 1 and 2) and look at the aSME values for these words. The waveforms are noisy, and the aSME values are somewhat higher than those for the 10 participants you processed in the previous exercises. But at least it looks like there is valid data for these bins.

Your job now is to figure out what has gone wrong with Bins 3 and 4 for this participant. In Section 2.11 of Chapter 2, I made a point of describing several pieces of information you should verify while processing a participant’s data. Use those hints to figure out what went wrong with this participant. You might want to load the EEGset that was used for the averaging process to check this information (30_N400_preprocessed_filt_elist_bins_be_ar_from_script.set). Once you’ve figured out the problem, you can read the text box below to make sure your answer was correct (but no peaking until you’ve figured it out for yourself!).

I hope you’ve now figured out the problems with Subject 30. I included this example to drive home a point that I made in Chapter 2, namely that you really need to pay close attention when you’re initially processing each participant’s data. Don’t just run a script and hope for the best. Look at the number of event codes, the number of accepted and rejected trials, the continuous EEG, and the epochs that were marked by the artifact detection process. If you don’t, your data will be filled with C.R.A.P. (which is an acronym for Commonly Recorded Artifactual Potentials, but is also used to describe a wide variety of miscellaneous data problems, such as incorrect event codes). So, if you want your experiments to yield robust, statistically significant, and accurate results, make sure to closely examine the data from every participant at each major stage of processing.

Before going to the next step, change the Current Folder back to the main Chapter_3 folder.

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