A state space modeling approach to real-time phase estimation
- Boston University, United States
- 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).
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Phastimatehttps://gin.g-node.org/bnplab/phastimate/src/master/murhythmdataset.mat, https://doi.org/10.1016/j.neuroimage.2020.116761.
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SSPE dataSSPE data, 10.6084/m9.figshare.14374355.
Article and author information
Author details
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.
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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A state space modeling approach to real-time phase estimation
eLife 10:e68803.
https://doi.org/10.7554/eLife.68803
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