Standardized Measurement Error now available in BrainVision Analyzer and MNE-Python

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A few years ago, we developed a universal metric of data quality for averaged ERPs called the standardized measurement error (click here for more information). It has been available in ERPLAB for several years, and I’m happy to announce that it is now available in BrainVision Analyzer 2.3.1 and in MNE-Python.

For BrainVision Analyzer, it is available as a Solution. At this point, you need to request it from Brain Products. Click here and scroll down to the last part of the page for more information.

For MNE-Python, it is accessed with the mne.stats.erp.compute_sme() function. Click here for details.

If you want to see some examples of how this metric can be used, check out these recent papers:

  • Zhang, G., Garrett, D. R., & Luck, S. J. (2024a). Optimal filters for ERP research I: A general approach for selecting filter settings. Psychophysiology, 61, e14531. http://doi.org/10.1111/psyp.14531 [preprint]

  • Zhang, G., Garrett, D. R., & Luck, S. J. (2024b). Optimal filters for ERP research II: Recommended settings for seven common ERP components. Psychophysiology, 61, e14530. https://doi.org/10.1111/psyp.14530 [preprint]

  • Zhang, G., Garrett, D. R., Simmons, A. M., Kiat, J. E., & Luck, S. J. (2024). Evaluating the effectiveness of artifact correction and rejection in event-related potential research. Psychophysiology, 61, e14511. https://doi.org/10.1111/psyp.14511 [preprint]

  • Zhang, G., & Luck, S. J. (2023). Variations in ERP data quality across paradigms, participants, and scoring procedures. Psychophysiology, 60, e14264. http://doi.org/10.1111/psyp.14264 [preprint]