ERP Boot Camp Tip: General Hints for Processing Data

EEG/ERP data are noisy and complicated, and it's easy to make mistakes or miss problems. Here are some hints for avoiding common problems that arise in EEG/ERP data collection and processing.

Start by running one subject and then doing a fairly complete analysis of that subject's data.  You will likely find some kind of problem (e.g., a problem with the event codes) that you need to fix before you run any more subjects.  Make sure you check the number of trials in each condition to make sure that it exactly matches what you expect.  Also, make sure you check the behavioral data, and not just the ERPs.  If you collect data from multiple subjects before doing a complete analysis, there's about a 50% chance that you will find a problem that requires that you throw out all of the data that you've collected, which will make you very sad. Do not skip this step! 

Once you verify that everything in your task, data collection procedures, and analysis scripts is working correctly, you can start collecting data from multiple additional subjects.  However, you should do a preliminary analysis of each subject's data within 48 hours of collecting the data (i.e., up to and including the point of plotting the averaged ERP waveforms).  This allows you to detect a problem (e.g., a malfunctioning electrode) before you collect data from a bunch of subjects with the same problem. This is especially important if you are not the one collecting the data and are therefore not present to notice problems during the actual recording session. 

The first time you process the data from a given subject, don't do it with a script!  Instead, process the data "by hand" (using a GUI) so that you can make sure that everything is OK with the subject's data.  There are many things that can go wrong, and this is your chance to find problems.  The most important things to look at are: the raw EEG before any processing, the EEG data after artifact detection, the time course and scalp distribution of any ICA components being excluded, the number of trials rejected in each condition, and the averaged ERP waveforms.  We recommend that you set artifact rejection parameters individually for each subject, because different people can have very different artifacts.  One size does not fit all.  (In a between-subjects design, the person setting the parameters should be blind to group membership to avoid biasing the results.)  These parameters can then be saved in an Excel file for future use and for reporting in journal articles.

If you need to re-analyze your data (e.g., with a different epoch length), it's much faster to do this with a script.  Your script can read in any subject-specific parameters from the Excel file.  Also, it's easy to make a mistake when you do the initial analysis "by hand," so re-analyzing everyone with a script prior to statistical analysis is a good idea. However, it is easy to make mistakes in scripting as well, so it's important to check the results of every step of processing in your script for accuracy.  It can also be helpful, especially if you are new to scripting, to have another researcher look through your data processing procedures to check for accuracy. 

Bottom line: Scripts are extremely useful for reanalyzing data, but they should not be used for the initial analysis.  Also, don't just borrow someone else's script and apply it to your data.  If you don't fully understand every step of a script (including the rationale for the parameters), don't use the script.

Hints for Processing Data.jpg

ERP Boot Camp Tip: What does the polarity of an ERP component mean?

We are often asked whether it means something whether a component is positive (e.g., P2 and P3) or negative (e.g., N1, N400, error-related negativity).  The answer, for the most part, is "no".

First, every ERP component will be positive on one side of the head and negative on the other side.  We often don't "see" the other side of a component (e.g., the negative side of the P3) because (a) the opposite-polarity side is in a place without any electrodes (e.g., the bottom of the skull), (b) the opposite-polarity side is obscured by other components, or (c) the opposite-polarity side is spatially diffuse (low amplitude and broadly distributed).  But it's there!

As the figure below shows, there are 4 factors that determine the polarity of an ERP component.  If we knew 3, we could in principle determine the 4th by knowing the polarity.  In practice, we never know 3 so we cannot determine the 4th.  In particular, although the polarity depends on whether the ERP arises from excitatory or inhibitory neurotransmission, we cannot ordinarily determine whether a component represents excitation or inhibition from its polarity.

Polarity.jpg

Mainly, polarity is used to help identify a given component.  For example, if our active electrodes are near Pz and our reference electrodes are near the mastoids, we can be sure that the P3 will be a positive voltage.  Beyond that, polarity doesn't tell us much.

ERP Boot Camp Tip: Stimulus Duration

Here's a really simple tip regarding the duration of visual stimuli in ERP experiments: In most cases, the duration of a visual stimulus should be either (a) between 100 and 200 ms or (b) longer than the time period that you will be showing in your ERP waveforms.  

timing.jpg

Here's the rationale: If your stimulus is shorter than ~100 ms, it is effectively the same as a 100 ms stimulus with lower contrast (look into Bloch's Law if you're interested in the reason for this).  For example, if you present a stimulus for 1 ms, the visual system will see this as being essentially identical to a very dim 100-ms stimulus.  As a result, there is usually no point is presenting a visual stimulus for less than 100 ms (unless you are using masking).

Once a stimulus duration exceeds ~100 ms, it produces an offset response as well as an onset response.  For example, the image shown here illustrates what happens with a 500-ms stimulus duration: There is a positive bump at 600 ms that is the P1 elicited by the offset of the stimulus.  This isn't necessarily a problem, but it makes your waveforms look weird.  You don't want to waste words explaining this in a paper.  So, if you need a long duration, make it long enough that the offset response is after the end of the time period you'll be showing in your waveforms.

The offset response gets gradually larger as the duration exceeds 100 ms.  With a 200-ms duration, the offset response is negligible.  So, if you want to give your participants a little extra time for perceiving the stimulus (but you don't want a very long duration), 200 ms is fine.  We usually use 200 ms in our schizophrenia studies.

Also, if the stimulus is <=200 ms, there is not much opportunity for eye movements (unless the stimulus is lateralized).  If you present a complex stimulus for >200 ms, you will likely get eye movements.  This may or may not be a significant problem, depending on the nature of your study.

Timing/phase distortions produced by filters

Yael, D., Vecht, J. J., & Bar-Gad, I. (2018). Filter Based Phase Shifts Distort Neuronal Timing Information. eNeuro 11 April 2018, ENEURO.0261-17.2018; DOI: 10.1523/ENEURO.0261-17.2018

This new paper describes how filters can distort the timing/phase of neurophysiological signals, including LFPs, ECoG, MEG, and EEG/ERPs.

See also the following papers (written with boot camp alumns Darren Tanner and Kara Morgan-Short), which show how improper filtering can create artificial effects (e.g., making a P600 look like an N400).

Tanner, D., Morgan-Short, K., & Luck, S. J. (2015). How inappropriate high-pass filters can produce artifactual effects and incorrect conclusions in ERP studies of language and cognition. Psychophysiology, 52, 997-1009.

Tanner, D., Norton, J. J., Morgan-Short, K., & Luck, S. J. (2016). On high-pass filter artifacts (they’re real) and baseline correction (it's a good idea) in ERP/ERMF analysis. Journal of Neuroscience Methods, 266, 166–170.

Bottom line: Filters are a form of controlled distortion that must be used carefully.  The more heavily you filter, the more you are distorting the temporal information in your signal.

How Many Trials Should You Include in Your ERP Experiment?

Boudewyn, M. A., Luck, S. J., Farrens, J. L., & Kappenman, E. S. (in press). How many trials does it take to get a significant ERP effect? It depends. Psychophysiology.

One question we often get asked at ERP Boot Camps is how many trials should be included in an experiment to obtain a stable and reliable version of a given ERP component. It turns out there is no single answer to this question that can be applied across all ERP studies. 

In a recent paper published in Psychophysiology in collaboration with Megan Boudewyn, a project scientist at UC Davis, we demonstrated how the number of trials, the number of participants, and the magnitude of the effect interact to influence statistical power (i.e., the probability of obtaining p<.05). One key finding was that doubling the number of trials recommended by previous studies led to more than a doubling of statistical power under many conditions. Interestingly, increasing the number of trials had a bigger effect on statistical power for within-participants comparisons than for between-group analyses. 

The results of this study show that a number of factors need to be considered in determining the number of trials needed in a given ERP experiment, and that there is no magic number of trials that can yield high statistical power across studies. 

Replication, Robustness, and Reproducibility in Psychophysiology

 

Interested in learning more about issues affecting reproducibility and replication in psychophysiological studies? Check out the articles in this special issue of Psychophysiology edited by Andreas Keil and me featuring articles by many notable researchers in the field.

Andreas and I will be discussing these issues and more with other researchers at a panel the opening night of the Society for Psychophysiological Research (SPR) annual meeting in Quebec City October 3-7

Decoding the contents of working memory from scalp EEG/ERP signals

Bae, G. Y., & Luck, S. J. (2018). Dissociable Decoding of Working Memory and Spatial Attention from EEG Oscillations and Sustained Potentials. The Journal of Neuroscience, 38, 409-422.

You've probably seen MVPA and other decoding methods in fMRI, but did you know that it's possible to decode information from the scalp distribution of EEG/ERP signals?

In this recent paper, we show that it is possible to decode the exact orientation of a stimulus as it is being held in working memory from sustained (CDA-like) ERPs.  A key finding is that we could decode both the orientation and the location of the attended stimulus with these sustained ERPs, whereas alpha-band EEG signals contained information only about the location.  

Our decoding accuracy was only about 50% above the chance level, but it's still pretty amazing that such precise information can be decoded from brain activity that we're recording from electrodes on the scalp!

Stay tuned for more cool EEG/ERP decoding results — we will be submitting a couple more studies in the near future.