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Some Sampling Properties of Common Phase Estimators.
MedLine Citation:
PMID:  23339610     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
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.
Authors:
Kyle Q Lepage; Mark A Kramer; Uri T Eden
Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2013-1-22
Journal Detail:
Title:  Neural computation     Volume:  -     ISSN:  1530-888X     ISO Abbreviation:  Neural Comput     Publication Date:  2013 Jan 
Date Detail:
Created Date:  2013-1-23     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9426182     Medline TA:  Neural Comput     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Affiliation:
Department of Mathematics, Boston University, Boston, MA 02446, U.S.A. lepage@math.bu.edu.
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