Neural Comput - Some sampling properties of common phase estimators.

Tópicos

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Resumo

The instantaneous phase of neural rhythms is important to many neuroscience-related studies. In this letter, we show that the statistical sampling properties of three instantaneous phase estimators commonly employed to analyze neuroscience data share common features, allowing an analytical investigation into their behavior. These three phase estimators-the Hilbert, complex Morlet, and discrete Fourier transform-are each shown to maximize the likelihood of the data, assuming the observation of different neural signals. This connection, explored with the use of a geometric argument, is used to describe the bias and variance properties of each of the phase estimators, their temporal dependence, and the effect of model misspecification. This analysis suggests how prior knowledge about a rhythmic signal can be used to improve the accuracy of phase estimates.

Resumo Limpo

instantan phase neural rhythm import mani neurosciencerel studi letter show statist sampl properti three instantan phase estim common employ analyz neurosci data share common featur allow analyt investig behavior three phase estimatorsth hilbert complex morlet discret fourier transformar shown maxim likelihood data assum observ differ neural signal connect explor use geometr argument use describ bias varianc properti phase estim tempor depend effect model misspecif analysi suggest prior knowledg rhythmic signal can use improv accuraci phase estim

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