9.11: Key Takeaways and References

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Key Takeaways

Just as with artifact rejection, the overarching goal in designing an artifact correction strategy is to maximize the likelihood that you will obtain an accurate answer to the scientific question your study is designed to answer. This typically involves reducing noise to maximize statistical power, avoiding artifactual voltages, and avoiding unwanted changes in the sensory input.
You can assess noise reduction by examining the SME before and after correction.
You can assess confounds by reconstructing the data only with the artifactual ICs and looking for differences between conditions.
Artifact correction cannot be used to avoid artifactual changes in the sensory input, and you will typically want to employ artifact detection and rejection for that purpose (in addition to artifact correction) in studies with visual stimuli.
Some of the assumptions of ICA are known to be incorrect, and it is therefore imperfect. In practice, ICA works best for large and frequent artifacts, such as blinks. I therefore recommend a conservative correction strategy in which you remove only the small set of ICs that correspond with well-understood artifacts and only after you have established that they are actually problematic (i.e., reduce the data quality substantially and/or create confounds).

References

Artoni, F., Delorme, A., & Makeig, S. (2018). Applying dimension reduction to EEG data by Principal Component Analysis reduces the quality of its subsequent Independent Component decomposition. NeuroImage, 175, 176–187. https://doi.org/10.1016/j.neuroimage.2018.03.016

Berger, H. (1929). Ueber das Elektrenkephalogramm des Menschen. Archives Fur Psychiatrie Nervenkrankheiten, 87, 527–570.

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

Box, G. E. P. (1976). Science and Statistics. Journal of the American Statistical Association, 71(356), 791–799. https://doi.org/10.1080/01621459.1976.10480949

Dimigen, O. (2020). Optimizing the ICA-based removal of ocular EEG artifacts from free viewing experiments. NeuroImage, 207, 116117. https://doi.org/10.1016/j.neuroimage.2019.116117

Drisdelle, B. L., Aubin, S., & Jolicoeur, P. (2017). Dealing with ocular artifacts on lateralized ERPs in studies of visual-spatial attention and memory: ICA correction versus epoch rejection. Psychophysiology, 54(1), 83–99. https://doi.org/10.1111/psyp.12675

Groppe, D. M., Makeig, S., & Kutas, M. (2009). Identifying reliable independent components via split-half comparisons. Neuroimage, 45, 1199–1211. https://doi.org/10.1016/j.neuroimage.2008.12.038

Luck, S. J. (2014). An Introduction to the Event-Related Potential Technique, Second Edition. MIT Press.

Näätänen, R., & Kreegipuu, K. (2012). The mismatch negativity (MMN). In S. J. Luck & E. S. Kappenman (Eds.), The Oxford Handbook of Event-Related Potential Components (pp. 143–157). Oxford University Press.