A method for decomposing multivariate time series into a causal hierarchy within specific frequency bands

DOI10.1007/s10827-018-0691-yzbMath1402.92267OpenAlexW2886081614WikidataQ90656040 ScholiaQ90656040MaRDI QIDQ1628354

Nicholas D. Schiff, Jonathan D. Drover

Publication date: 4 December 2018

Published in: Journal of Computational Neuroscience (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10827-018-0691-y

zbMATH Keywords

Granger causalityautoregressionhierarchical decompositionelectroencephalogram

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to biology and medical sciences; meta analysis (62P10) Neural biology (92C20) Biomedical imaging and signal processing (92C55)

Cites Work

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• Nonlinear autoregressive analysis of the \(3/s\) ictal electroencephalogram: Implications for underlying dynamics
• Measurement of Linear Dependence and Feedback Between Multiple Time Series
• A new look at the statistical model identification

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