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

• Published in: Journal of neuroscience methods Vol. 150; no. 2; pp. 228 - 237
• Main Authors: Chen, Yonghong, Bressler, Steven L., Ding, Mingzhou
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 30-01-2006
• Subjects: Algorithms; Animals; Autoregressive moving average (ARMA) process; Brain - physiology; Conditional Granger causality; Evoked Potentials - physiology; Frequency domain; Granger causality; Models, Neurological; Multiple time series; Multivariate autoregressive (MVAR) model; Neurons - physiology; Partition matrix; Frequency domain; Multiple time series; Conditional Granger causality; Multivariate autoregressive (MVAR) model; Autoregressive moving average (ARMA) process; Partition matrix; Granger causality
• ISSN: 0165-0270; 1872-678X
• DOI: 10.1016/j.jneumeth.2005.06.011

It 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.