A state space modeling approach to real-time phase estimation

  1. Anirudh Wodeyar  Is a corresponding author
  2. Mark Schatza
  3. Alik S Widge
  4. Uri T Eden
  5. Mark A Kramer
  1. Boston University, United States
  2. University of Minnesota, United States

Abstract

Brain rhythms have been proposed to facilitate brain function, with an especially important role attributed to the phase of low frequency rhythms. Understanding the role of phase in neural function requires interventions that perturb neural activity at a target phase, necessitating estimation of phase in real-time. Current methods for real-time phase estimation rely on bandpass filtering, which assumes narrowband signals and couples the signal and noise in the phase estimate, adding noise to the phase and impairing detections of relationships between phase and behavior. To address this, we propose a state space phase estimator for real-time tracking of phase. By tracking the analytic signal as a latent state, this framework avoids the requirement of bandpass filtering, separately models the signal and the noise, accounts for rhythmic confounds, and provides credible intervals for the phase estimate. We demonstrate in simulations that the state space phase estimator outperforms current state-of-the-art real-time methods in the contexts of common confounds such as broadband rhythms, phase resets and co-occurring rhythms. Finally, we show applications of this approach to in vivo data. The method is available as a ready-to-use plug-in for the OpenEphys acquisition system, making it widely available for use in experiments.

Data availability

All data generated or analyzed during this study, or were used to create the figures are included in the supporting files, or are available through already public data archives (https://gin.g-node.org/bnplab/phastimate, 10.6084/m9.figshare.14374355).

The following previously published data sets were used
    1. Zrenner C
    2. Galevska D
    3. Nieminen JO
    4. Baur D
    5. Stefanou MI
    6. Ziemann U
    (2020) Phastimate
    https://gin.g-node.org/bnplab/phastimate/src/master/murhythmdataset.mat, https://doi.org/10.1016/j.neuroimage.2020.116761.

Article and author information

Author details

  1. Anirudh Wodeyar

    Mathematics and Statistics, Boston University, Boston, United States
    For correspondence
    wodeyar@bu.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2577-5139
  2. Mark Schatza

    Department of Psychiatry, University of Minnesota, Minneapolis, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Alik S Widge

    Department of Psychiatry, University of Minnesota, Minneapolis, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8510-341X
  4. Uri T Eden

    Department of Mathematics and Statistics, Boston University, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Mark A Kramer

    Department of Mathematics and Statistics, Boston University, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.

Funding

National Institutes of Health (R01 EB026938)

  • Anirudh Wodeyar
  • Alik S Widge
  • Uri T Eden
  • Mark A Kramer

National Institutes of Health (R01 MH119384)

  • Alik S Widge

National Institutes of Health (R01 MH123634)

  • Alik S Widge

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Frances K Skinner, Krembil Research Institute, University Health Network, Canada

Version history

  1. Preprint posted: March 25, 2021 (view preprint)
  2. Received: March 26, 2021
  3. Accepted: September 24, 2021
  4. Accepted Manuscript published: September 27, 2021 (version 1)
  5. Version of Record published: October 22, 2021 (version 2)

Copyright

© 2021, Wodeyar et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Anirudh Wodeyar
  2. Mark Schatza
  3. Alik S Widge
  4. Uri T Eden
  5. Mark A Kramer
(2021)
A state space modeling approach to real-time phase estimation
eLife 10:e68803.
https://doi.org/10.7554/eLife.68803

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https://doi.org/10.7554/eLife.68803

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