8.19: Key Takeaways and References
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Key Takeaways
- The overarching goal in designing an artifact rejection strategy 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 implementing artifact rejection if you have a better way of reaching that goal.
- Artifacts are typically problematic for one of three reasons: 1) they are a large source of noise and therefore reduce your statistical power; 2) they differ systematically across groups or conditions, creating a confound; 3) they indicate a problem with the sensory input (e.g., closed eyes during the presentation of a visual stimulus). In most cases, you are rejecting trials with artifacts to address one or more of these issues.
- Decreasing the threshold for rejection typically reduces the confounding effects of artifacts and the problems with the sensory input, and it may also reduce the noise caused by the artifacts. However, when the threshold gets too low, the number of trials remaining in the averaged ERP waveforms gets small enough that the data quality suffers. You can use the aSME values to help find the optimal threshold for rejection.
- You will typically want to implement several different artifact detection procedures for each participant so that you can intelligently detect the different types of artifacts. This is often achieved with one procedure for detecting blinks, another for detecting eye movements, and a third for detecting C.R.A.P.
- Dealing with small but consistent eye movements is tricky, because small eye rotations are difficult to detect but can be a significant confound in experiments with lateralized stimuli or lateralized responses. The two-stage procedure deals with this by using the greater precision of averaged HEOG waveforms to determine whether the small eye movements that escape rejection are large enough to have a substantial impact.
References
Baker, D. H., Vilidaite, G., Lygo, F. A., Smith, A. K., Flack, T. R., Gouws, A. D., & Andrews, T. J. (2020). Power contours: Optimising sample size and precision in experimental psychology and human neuroscience. Psychological Methods . http://dx.doi.org/10.1037/met0000337
Eimer, M. (1994). “Sensory gating” as a mechanism for visuospatial orienting: Electrophysiological evidence from trial-by-trial cuing experiments. Perception & Psychophysics , 55 , 667–675.
Jas, M., Engemann, D. A., Bekhti, Y., Raimondo, F., & Gramfort, A. (2017). Autoreject: Automated artifact rejection for MEG and EEG data. NeuroImage , 159 , 417–429. https://doi.org/10.1016/j.neuroimage.2017.06.030
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
Lins, O. G., Picton, T. W., Berg, P., & Scherg, M. (1993). Ocular artifacts in EEG and event-related potentials I: Scalp topography. Brain Topography , 6 , 51–63.
Luck, S. J. (2012). Electrophysiological correlates of the focusing of attention within complex visual scenes: N2pc and related ERP components. In S. J. Luck & E. S. Kappenman (Eds.), The Oxford Handbook of ERP Components (pp. 329–360). Oxford University Press.
Luck, S. J. (2014). An Introduction to the Event-Related Potential Technique, Second Edition . MIT Press.
Luck, S. J., Hillyard, S. A., Mouloua, M., Woldorff, M. G., Clark, V. P., & Hawkins, H. L. (1994). Effects of spatial cuing on luminance detectability: Psychophysical and electrophysiological evidence for early selection. Journal of Experimental Psychology: Human Perception and Performance , 20 , 887–904.
Luck, S. J., Stewart, A. X., Simmons, A. M., & Rhemtulla, M. (2021). Standardized measurement error: A universal metric of data quality for averaged event-related potentials. Psychophysiology , 58 , e13793. https://doi.org/10.1111/psyp.13793
Mangun, G. R., & Hillyard, S. A. (1991). Modulations of sensory-evoked brain potentials indicate changes in perceptual processing during visual-spatial priming. Journal of Experimental Psychology: Human Perception and Performance , 17 , 1057–1074.
Nolan, H., Whelan, R., & Reilly, R. B. (2010). FASTER: Fully Automated Statistical Thresholding for EEG artifact Rejection. Journal of Neuroscience Methods , 192 (1), 152–162. https://doi.org/10.1016/j.jneumeth.2010.07.015
Talsma, D. (2008). Auto-adaptive averaging: Detecting artifacts in event-related potential data using a fully automated procedure. Psychophysiology , 45 (2), 216–228. https://doi.org/10.1111/j.1469-8986.2007.00612.x
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Yuval-Greenberg, S., Tomer, O., Keren, A. S., Nelken, I., & Deouell, L. Y. (2008). Transient induced gamma-band response in EEG as a manifestation of miniature saccades. Neuron , 58 (3), 429–441. https://doi.org/10.1016/j.neuron.2008.03.027