An improved algorithm for the detection of dynamical interdependence in bivariate time-series.
We describe a new algorithm for the detection of dynamical interdependence in bivariate time-series data sets. By using geometrical and dynamical arguments, we produce a method that can detect dynamical interdependence in weakly coupled systems where previous techniques have failed. We illustrate this by comparison of our algorithm with another commonly used technique when applied to a system of coupled Hénon maps. In addition, an improvement of approximately 20% in the detection rate is observed when the technique is applied to human scalp EEG data, as compared with existing techniques. Such an improvement may assist an understanding of the role of large-scale nonlinear processes in normal brain function.
Terry
John R
JR
Department of Mathematical Sciences, Loughborough University, Leices, LEII 3TU, UK. J.R.Terry@lboro.ac.uk
Breakspear
Michael
M
D000465:Algorithms; D001921:Brain / Q000502:physiology*; D004569:Electroencephalography; D006801:Humans; D008959:Models, Neurological; D017711:Nonlinear Dynamics; D010507:Periodicity