Key Takeaways

• The overarching goal for your EEG preprocessing is to maximize the likelihood that you will obtain an accurate answer to the scientific question your study is designed to answer. You can ignore any of my specific suggestions for your preprocessing pipeline if you have a better way of reaching that goal.
• You can more easily obtain an accurate answer to your scientific question if you look carefully at each participant’s data prior to doing the preprocessing. By examining the data, you’ll be able to adjust the preprocessing to reflect the unique problems of each individual participant’s data.
• The standardized measurement error (SME) provides a useful metric for knowing whether a “bad channel” is really problematic with respect to the analyses you will be performing with your data.
• Interpolation is a low-risk procedure when the channel being interpolated will not be used for your main analyses. But if the channel will be used in your main analyses, you need to think carefully about whether to interpolate the channel or exclude the participant from analysis.

References

Bigdely-Shamlo, N., Mullen, T., Kothe, C., Su, K.-M., & Robbins, K. A. (2015). The PREP pipeline: Standardized preprocessing for large-scale EEG analysis. Frontiers in Neuroinformatics, 9. https://doi.org/10.3389/fninf.2015.00016

Kappenman, E. S., Farrens, J. L., Zhang, W., Stewart, A. X., & Luck, S. J. (2021). ERP CORE: An open resource for human event-related potential research. NeuroImage, 225, 117465. https://doi.org/10.1016/j.neuroimage.2020.117465