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.

How to p-hack (and avoid p-hacking) in ERP Research

Luck, S. J., & Gaspelin, N. (2017). How to Get Statistically Significant Effects in Any ERP Experiment (and Why You Shouldn’t)Psychophysiology, 54, 146-157.

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In this article, we show how ridiculously easy it is to find significant effects in ERP experiments by using the observed data to guide the selection of time windows and electrode sites. We also show that including multiple factors in your ANOVAs can dramatically increase the rate of false positives (Type I errors). We provide some suggestions for methods to avoid inflating the Type I error rate.

This paper was part of a special issue of Psychophysiology on Reproducibility edited by Emily Kappenman and Andreas Keil.

Announcing the Virtual ERP Boot Camp Blog

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We have started a new blog called Virtual ERP Boot Camp on erpinfo.org to provide tips, advice, and other information about best practices for ERP research. We will also be highlighting new research that is relevant to the field.

To submit a question to our advice column, send an email to ERPquestions@gmail.com. We can't answer every question, but we will post answers to questions that we believe will be of general interest.

You can also follow us on our new Twitter account: @erpbootcamp.

Steve and Emily