PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Alleviating the Influence of Weak Data Asymmetries on Granger-Causal Analyses
S Haufe, V Nikulin and G Nolte
In: LATENT VARIABLE ANALYSIS AND SIGNAL SEPARATION Lecture Notes in Computer Science , 7191/2012 . (2012) Springer-Verlag , Berlin - Heidelberg , pp. 25-33. ISBN 7191/2012

Abstract

We introduce the concepts of weak and strong asymmetries in multivariate time series in the context of causal modeling. Weak asymmetries are by definition differences in univariate properties of the data, which are not necessarily related to causal relationships between time series. Nevertheless, they might still mislead (in particular Granger-) causal analyses. We propose two general strategies to overcome the negative influence of weak asymmetries in causal modeling. One is to assess the confidence of causal predictions using the antisymmetry-symmetry ratio, while the other one is based on comparing the result of a causal analysis to that of an equivalent analysis of time-reversed data. We demonstrate that Granger Causality applied to the SiSEC challenge on causal analysis of simulated EEG data greatly benefits from our suggestions

EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
Theory & Algorithms
ID Code:9488
Deposited By:Benjamin Blankertz
Deposited On:16 March 2012