Effective detection of coupling in short and noisy bivariate data

Bhattacharya, Joydeep; Pereda, Ernesto and Petsche, Hellmuth. 2003. Effective detection of coupling in short and noisy bivariate data. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 33(1), pp. 85-95. ISSN 1083-4419 [Article]

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Abstract or Description

In the study of complex systems, one of the primary concerns is the characterization and quantification of interdependencies between different subsystems. In real-life systems, the nature of dependencies or coupling can be nonlinear and asymmetric, rendering the classical linear methods unsuitable for this purpose. Furthermore, experimental signals are noisy and short, which pose additional constraints for the measurement of underlying coupling. We discuss an index based on nonlinear dynamical system theory to measure the degree of coupling which can be asymmetric. The usefulness of this index has been demonstrated by several examples including simulated and real-life signals. This index is found to effectively disclose the nature and the degree of interactions even when the coupling is very weak and data are noisy and of limited length; by this way, new insight into the functioning of the underlying complex system is possible.

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Date Deposited:

22 Feb 2011 08:33

Last Modified:

30 Jun 2017 13:22

Peer Reviewed:

Yes, this version has been peer-reviewed.



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