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 or watch our quick 3-minute video overview). 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]