Frequency decomposition of conditional Granger causality and application to multivariate neural field potential data

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dc.creatorChen, Yonghong
dc.creatorBressler, Steven L.
dc.creatorDing, Mingzhou
dc.date2006-08-23
dc.date.accessioned2026-07-07T07:26:19Z
dc.date.available2026-07-07T07:26:19Z
dc.descriptionIt is often useful in multivariate time series analysis to determine statistical causal relations between different time series. Granger causality is a fundamental measure for this purpose. Yet the traditional pairwise approach to Granger causality analysis may not clearly distinguish between direct causal influences from one time series to another and indirect ones acting through a third time series. In order to differentiate direct from indirect Granger causality, a conditional Granger causality measure in the frequency domain is derived based on a partition matrix technique. Simulations and an application to neural field potential time series are demonstrated to validate the method.
dc.description18 pages, 6 figures, Journal published
dc.identifierhttps://arxiv.org/abs/q-bio/0608034
dc.identifierhttp://arxiv.org/abs/q-bio/0608034
dc.identifierJournal of Neuroscience Methods, 150(2), 2006: 228-237
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/117035
dc.subjectNeurons and Cognition
dc.subjectQuantitative Methods
dc.titleFrequency decomposition of conditional Granger causality and application to multivariate neural field potential data
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