A state space modeling approach to real-time phase estimation.
A state space modeling approach to real-time phase estimation.
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DOI：
10.7554/elife.68803
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发表时间：
2021-09-27
期刊：
影响因子：
7.7
通讯作者：
Kramer MA
中科院分区：
文献类型：
--
作者：
Wodeyar A;Schatza M;Widge AS;Eden UT;Kramer MA
脑节律被认为可以促进大脑功能，其中一个特别重要的作用被归因于低频节律的阶段。要了解时相在神经功能中的作用，需要干预扰乱目标时相的神经活动，从而有必要实时估计时相。当前的实时相位估计方法依赖于带通滤波，该方法假设窄带信号，并在相位估计中耦合信号和噪声，增加了噪声，并影响了对相位和行为之间关系的检测。针对这一问题，我们提出了一种状态空间相位估计器，用于实时跟踪相位。该框架通过将解析信号作为潜在状态进行跟踪，避免了带通滤波的要求，将信号和噪声分开建模，解决了节奏混杂问题，并为相位估计提供了可靠的间隔。我们在仿真中证明了状态空间相位估计器在宽带节律、相位重置和共生节律等常见混杂情况下的性能优于当前最先进的实时方法。最后，我们展示了这种方法在活体数据中的应用。该方法是Open Ephys采集系统的现成插件，可广泛用于实验。
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 Open Ephys acquisition system, making it widely available for use in experiments.