Testing for directed influences among neural signals using partial directed coherence.
One major challenge in neuroscience is the identification of interrelations between signals reflecting neural activity. When applying multivariate time series analysis techniques to neural signals, detection of directed relationships, which can be described in terms of Granger-causality, is of particular interest. Partial directed coherence has been introduced for a frequency domain analysis of linear Granger-causality based on modeling the underlying dynamics by vector autoregressive processes. We discuss the statistical properties of estimates for partial directed coherence and propose a significance level for testing for nonzero partial directed coherence at a given frequency. The performance of this test is illustrated by means of linear and non-linear model systems and in an application to electroencephalography and electromyography data recorded from a patient suffering from essential tremor.
Schelter
Björn
B
FDM, Freiburg Center for Data Analysis and Modeling, University of Freiburg, and Department of Neurology, University Hospital of Freiburg, Germany. schelter@fdm.uni-freiburg.de
Winterhalder
Matthias
M
Eichler
Michael
M
Peifer
Martin
M
Hellwig
Bernhard
B
Guschlbauer
Brigitte
B
Lücking
Carl Hermann
CH
Dahlhaus
Rainer
R
Timmer
Jens
J
D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004569:Electroencephalography / Q000706:statistics & numerical data; D004576:Electromyography / Q000706:statistics & numerical data; D006801:Humans; D016014:Linear Models; D009482:Neurophysiology / Q000379:methods* / Q000706:statistics & numerical data; D017711:Nonlinear Dynamics; D013269:Stochastic Processes; D014202:Tremor / Q000503:physiopathology