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. 2020 Jun 4;30(10):5502–5516. doi: 10.1093/cercor/bhaa132

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© The Author(s) 2020. Published by Oxford University Press.

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

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Theta phase-reset simulation. (A) An ERP generated by simulating a theta-band phase-reset mechanism (see Methods section). Phase was reset to a preselected phase (in this case, to or 144°). Notice the sharp, high-frequency transient that is present in the ERP at time t = 0 despite being generated using a low-frequency phase-reset mechanism. (B) The intertrial phase clustering of the same simulated data as in (A). Notice that despite the mechanism of the response being a low-frequency reset, broadband phase concentration is seen, suggesting that high-frequency transients in the data can skew intertrial phase clustering metrics to show artificial, high-frequency phase clustering that is not present in the underlying data.

Inline graphic — Theta phase-reset simulation. (A) An ERP generated by simulating a theta-band phase-reset mechanism (see Methods section). Phase was reset to a preselected phase (in this case, to or 144°). Notice the sharp, high-frequency transient that is present in the ERP at time t = 0 despite being generated using a low-frequency phase-reset mechanism. (B) The intertrial phase clustering of the same simulated data as in (A). Notice that despite the mechanism of the response being a low-frequency reset, broadband phase concentration is seen, suggesting that high-frequency transients in the data can skew intertrial phase clustering metrics to show artificial, high-frequency phase clustering that is not present in the underlying data.