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A State Space Modeling Approach to Real-Time Phase Estimation

View ORCID ProfileAnirudh Wodeyar, Mark Schatza, View ORCID ProfileAlik S. Widge, View ORCID ProfileUri T. Eden, View ORCID ProfileMark A. Kramer
doi: https://doi.org/10.1101/2021.03.25.437024
Anirudh Wodeyar
1Department of Mathematics and Statistics, Boston University
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  • For correspondence: awodeyar@uci.edu
Mark Schatza
2Department of Psychiatry, University of Minnesota
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Alik S. Widge
2Department of Psychiatry, University of Minnesota
3Department of Neuroscience, University of Minnesota
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Uri T. Eden
1Department of Mathematics and Statistics, Boston University
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Mark A. Kramer
1Department of Mathematics and Statistics, Boston University
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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.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted March 25, 2021.
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A State Space Modeling Approach to Real-Time Phase Estimation
Anirudh Wodeyar, Mark Schatza, Alik S. Widge, Uri T. Eden, Mark A. Kramer
bioRxiv 2021.03.25.437024; doi: https://doi.org/10.1101/2021.03.25.437024
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A State Space Modeling Approach to Real-Time Phase Estimation
Anirudh Wodeyar, Mark Schatza, Alik S. Widge, Uri T. Eden, Mark A. Kramer
bioRxiv 2021.03.25.437024; doi: https://doi.org/10.1101/2021.03.25.437024

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