Modeling Spectral Properties in Stationary Processes of Varying Dimensions with Applications to Brain Local Field Potential Signals.
Type
ArticleKAUST Department
Statistics ProgramComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Date
2020-12-06Submitted Date
2020-10-22Permanent link to this record
http://hdl.handle.net/10754/660740
Metadata
Show full item recordAbstract
In some applications, it is important to compare the stochastic properties of two multivariate time series that have unequal dimensions. A new method is proposed to compare the spread of spectral information in two multivariate stationary processes with different dimensions. To measure discrepancies, a frequency specific spectral ratio (FS-ratio) statistic is proposed and its asymptotic properties are derived. The FS-ratio is blind to the dimension of the stationary process and captures the proportion of spectral power in various frequency bands. Here we develop a technique to automatically identify frequency bands that carry significant spectral power. We apply our method to track changes in the complexity of a 32-channel local field potential (LFP) signal from a rat following an experimentally induced stroke. At every epoch (a distinct time segment from the duration of the experiment), the nonstationary LFP signal is decomposed into stationary and nonstationary latent sources and the complexity is analyzed through these latent stationary sources and their dimensions that can change across epochs. The analysis indicates that spectral information in the Beta frequency band (12-30 Hertz) demonstrated the greatest change in structure and complexity due to the stroke.Citation
Sundararajan, R. R., Frostig, R., & Ombao, H. (2020). Modeling Spectral Properties in Stationary Processes of Varying Dimensions with Applications to Brain Local Field Potential Signals. Entropy, 22(12), 1375. doi:10.3390/e22121375Sponsors
This work is support in part by KAUST, NIH NS066001, Leducq Foundation 15CVD02 and NIH MH115697.Publisher
MDPI AGJournal
Entropy (Basel, Switzerland)PubMed ID
33279920arXiv
1911.12295Additional Links
https://www.mdpi.com/1099-4300/22/12/1375ae974a485f413a2113503eed53cd6c53
10.3390/e22121375
Scopus Count
Except where otherwise noted, this item's license is described as This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
Related articles
- Heuristic segmentation of a nonstationary time series.
- Authors: Fukuda K, Eugene Stanley H, Nunes Amaral LA
- Issue date: 2004 Feb
- Evolutionary State-Space Model and Its Application to Time-Frequency Analysis of Local Field Potentials.
- Authors: Gao X, Shen W, Shahbaba B, Fortin NJ, Ombao H
- Issue date: 2020 Jul
- Phase correlation among rhythms present at different frequencies: spectral methods, application to microelectrode recordings from visual cortex and functional implications.
- Authors: Schanze T, Eckhorn R
- Issue date: 1997 Jun
- The Oscillatory ReConstruction Algorithm adaptively identifies frequency bands to improve spectral decomposition in human and rodent neural recordings.
- Authors: Watrous AJ, Buchanan RJ
- Issue date: 2020 Dec 1
- Information theoretic equivalent bandwidths of random processes and their applications.
- Authors: Yoshida H, Kikkawa S
- Issue date: 2007