Automatic Control and Systems Engineering, University of Sheffield
Abstract
This paper introduces a novel method called the ERR-Causality, or Error Reduction Ratio Causality test, that can be used to detect and track causal relationships
between two signals using a new adaptive forward
orthogonal least squares (Adaptive-Forward-OLS) algorithm.
In comparison to the traditional Granger method,
one advantage of the new ERR-Causality test is that it
can effectively detect the time-varying direction of linear
or nonlinear causality between two signals without fitting
a complete model. Another important advantage is that
the ERR-Causality test can detect both the direction of
interactions and estimate the relative time shift between
the two signals. Several numerical examples are provided
to illustrate the effectiveness of the new method for causal
relationship detection between two signals. An important
real application, relating to the analysis of the causality
of EEG signals from different cortical sites which can be
very useful for understanding brain activity during an
epileptic seizure by inspecting the high-resolution time varying directed information flow, is also discussed
