Eeg Paradigm Designer

Step 6: Verify Against Common Pitfalls

Before finalizing the paradigm, check for these non-obvious EEG-specific design flaws:

1. Overlapping ERPs from adjacent events: If ISI < the duration of the slowest component, ERPs overlap. For P3b (300--600 ms), this means ISIs under ~1200 ms create overlap. For P600 (500--800+ ms), ISIs under ~1500 ms are problematic. Use the ADJAR procedure or linear modeling (e.g., unfold toolbox) if fast ISIs are required (Woldorff, 1993; Ehinger & Dimigen, 2019).

2. Stimulus confounds masquerading as cognitive ERPs: Differences in luminance, contrast, spatial frequency, size, or retinal position between conditions produce P1/N1 differences that are sensory, not cognitive. Always equate low-level stimulus properties or use difference waveforms that cancel them (Luck, 2014, Ch. 2).

3. Inadequate baselines: If pre-stimulus activity differs between conditions (e.g., from a preceding cue or from CNV buildup during fixed foreperiods), standard baseline correction (-200 to 0 ms) will distort post-stimulus ERP measurements. Use jittered ISIs and verify baseline equivalence (Luck, 2014, Ch. 6; Alday, 2019).

4. Motor confounds with cognitive ERPs: If conditions differ in response requirements (e.g., one condition has button press, the other does not), motor-related ERPs (LRP, readiness potential) contaminate the cognitive ERP. Use conditions with identical motor responses or analyze only stimulus-locked, pre-response windows (Luck, 2014, Ch. 6).

5. Probability confounds in oddball paradigms: In P3b oddball designs, the rare target differs from the frequent standard in both probability and task relevance. To disentangle these, include a rare non-target condition (three-stimulus oddball) or use an equiprobable control (Luck, 2014, Ch. 3; Polich, 2007).

6. Physical-deviance confound in MMN: The standard and deviant stimuli differ in physical features, which can produce differential N1 responses independent of memory-trace mismatch. Use a "many-standards" or "flip-flop" control design where the same physical stimulus serves as both standard and deviant across blocks (Naatanen et al., 2007; Jacobsen & Schroger, 2001).

7. Lateralized eye movements confounding N2pc: Saccades toward the target produce HEOG artifacts that mimic the contralateral negativity of the N2pc. Enforce fixation, reject trials with HEOG deviations > +/- 16 uV (corresponding to ~1 degree eye movement), or use residual HEOG correction (Luck, 2014, Ch. 3; Woodman & Luck, 2003).

8. Insufficient error trials for ERN: Error rate depends on task difficulty. If the task is too easy (< 5% errors), you will not accumulate enough error trials. Titrate difficulty to achieve 10--25% error rate using adaptive procedures or speed-emphasis instructions (Gehring et al., 1993; Olvet & Hajcak, 2009).

9. Confounding component overlap in language ERPs: In sentence paradigms, an apparent N400 reduction may be driven by an overlapping P600 in the same condition, and vice versa. Report and interpret both components; consider component-overlap modeling (Luck, 2014, Ch. 2; Brouwer et al., 2017).

10. High-pass filter artifacts for slow components: If you plan to study CNV, P3b, N400, or P600, ensure the recording system and preprocessing pipeline allow high-pass cutoffs of <= 0.1 Hz. Cutoffs at 0.5 Hz or above create artificial distortions of broad components (Tanner et al., 2015; see erp-analysis skill).

EEG-Specific Additions to Standard Paradigm Design

When adapting a behavioral paradigm for EEG, apply these modifications:

References

• Alday, P. M. (2019). How much baseline correction do we need in ERP research? Brain Topography, 32, 167--174.
• Boudewyn, M. A., Luck, S. J., Farrens, J. L., & Kappenman, E. S. (2018). How many trials does it take to get a significant ERP effect? Psychophysiology, 55(6), e13049.
• Brouwer, H., Crocker, M. W., Venhuizen, N. J., & Hoeks, J. C. J. (2017). A neurocomputational model of the N400 and the P600 in language processing. Cognitive Science, 41, 1318--1352.
• Brunia, C. H. M., van Boxtel, G. J. M., & Bocker, K. B. E. (2012). Negative slow waves as indices of anticipation. In S. J. Luck & E. S. Kappenman (Eds.), The Oxford Handbook of ERP Components. Oxford University Press.
• Coles, M. G. H., & Rugg, M. D. (1995). Event-related brain potentials: An introduction. In M. D. Rugg & M. G. H. Coles (Eds.), Electrophysiology of Mind. Oxford University Press.
• Coles, M. G. H., Gratton, G., & Donchin, E. (1988). Detecting early communication: Using measures of movement-related potentials to illuminate human information processing. Biological Psychology, 26, 69--89.
• Duncan, C. C., et al. (2009). Event-related potentials in clinical research: Guidelines for eliciting, recording, and quantifying mismatch negativity, P300, and N400. Clinical Neurophysiology, 120(11), 1883--1908.
• Ehinger, B. V., & Dimigen, O. (2019). Unfold: An integrated toolbox for overlap correction, non-linear modeling, and regression-based EEG analysis. PeerJ, 7, e7838.
• Gehring, W. J., Goss, B., Coles, M. G. H., Meyer, D. E., & Donchin, E. (1993). A neural system for error detection and compensation. Psychological Science, 4(6), 385--390.
• Jacobsen, T., & Schroger, E. (2001). Is there pre-attentive memory-based comparison of pitch? Psychophysiology, 38(4), 723--727.
• Jensen, K. M., & MacDonald, J. A. (2023). Towards thoughtful planning of ERP studies: How participants, trials, and effect magnitude interact to influence statistical power across seven ERP components. Psychophysiology, 60(7), e14245.
• Kappenman, E. S., & Luck, S. J. (2010). The effects of electrode impedance on data quality and statistical significance in ERP recordings. Psychophysiology, 47(5), 888--904.
• 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.
• Keil, A., et al. (2014). Committee report: Publication guidelines and recommendations for studies using EEG and MEG. Psychophysiology, 51(1), 1--21.
• Luck, S. J. (2005). Ten simple rules for designing ERP experiments. In T. C. Handy (Ed.), Event-Related Potentials: A Methods Handbook. MIT Press.
• Luck, S. J. (2014). An Introduction to the Event-Related Potential Technique (2nd ed.). MIT Press.
• Naatanen, R., Paavilainen, P., Rinne, T., & Alho, K. (2007). The mismatch negativity (MMN) in basic research of central auditory processing. Clinical Neurophysiology, 118(12), 2544--2590.
• Norcia, A. M., Appelbaum, L. G., Ales, J. M., Cottereau, B. R., & Rossion, B. (2015). The steady-state visual evoked potential in vision research: A review. Journal of Vision, 15(6), 4.
• Olvet, D. M., & Hajcak, G. (2009). The stability of error-related brain activity with increasing number of trials. Psychophysiology, 46(5), 957--961.
• Osterhout, L., & Holcomb, P. J. (1992). Event-related brain potentials elicited by syntactic anomaly. Journal of Memory and Language, 31(6), 785--806.
• Polich, J. (2007). Updating P300: An integrative theory of P3a and P3b. Clinical Neurophysiology, 118(10), 2128--2148.
• Rossion, B., & Jacques, C. (2008). Does physical interstimulus variance account for early electrophysiological face sensitive responses? NeuroImage, 39(4), 1959--1966.
• Tanner, D., Morgan-Short, K., & Luck, S. J. (2015). How inappropriate high-pass filters can produce artifactual effects. Psychophysiology, 52(8), 997--1009.
• Woldorff, M. G. (1993). Distortion of ERP averages due to overlap from temporally adjacent ERPs: Analysis and correction. Psychophysiology, 30(1), 98--119.
• Woodman, G. F., & Luck, S. J. (2003). Serial deployment of attention during visual search. Journal of Experimental Psychology: Human Perception and Performance, 29(1), 121--138.

See references/ for detailed component-paradigm mapping and timing parameter tables.