Bias in group-level EEG microstate analysis. Murphy, M., Wang, J., Jiang, C., Wang, L., Kozhemiako, N., Wang, Y., Consortium, t. G., Pan, J. Q., & Purcell, S. M. November, 2022. Pages: 2022.11.07.515464 Section: New Results![Bias in group-level EEG microstate analysis [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Microstate analysis is a promising technique for analyzing high-density electroencephalographic data, but there are multiple questions about methodological best practices. Here we focus on a commonly used approach to the analysis of group differences in microstate maps and the derived metrics based on those maps. Using simulations seeded on real data from healthy control subjects, we compared error rates for analyses performed using microstates derived from the entire dataset to analyses performed using microstates derived separately within each subgroup. The latter approach resulted in substantially higher type I error rates. These results suggest that even subtle differences in microstate topography can have profound effects on derived microstate metrics and that future studies using microstate analysis should take steps to mitigate this large source of error.
@misc{murphy_bias_2022,
title = {Bias in group-level {EEG} microstate analysis},
copyright = {© 2022, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution 4.0 International), CC BY 4.0, as described at http://creativecommons.org/licenses/by/4.0/},
url = {https://www.biorxiv.org/content/10.1101/2022.11.07.515464v1},
doi = {10.1101/2022.11.07.515464},
abstract = {Microstate analysis is a promising technique for analyzing high-density electroencephalographic data, but there are multiple questions about methodological best practices. Here we focus on a commonly used approach to the analysis of group differences in microstate maps and the derived metrics based on those maps. Using simulations seeded on real data from healthy control subjects, we compared error rates for analyses performed using microstates derived from the entire dataset to analyses performed using microstates derived separately within each subgroup. The latter approach resulted in substantially higher type I error rates. These results suggest that even subtle differences in microstate topography can have profound effects on derived microstate metrics and that future studies using microstate analysis should take steps to mitigate this large source of error.},
language = {en},
urldate = {2022-11-10},
publisher = {bioRxiv},
author = {Murphy, M. and Wang, J. and Jiang, C. and Wang, L. and Kozhemiako, N. and Wang, Y. and Consortium, the GRINS and Pan, J. Q. and Purcell, S. M.},
month = nov,
year = {2022},
note = {Pages: 2022.11.07.515464
Section: New Results},
}
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