TY - JOUR

T1 - Synchronization likelihood

T2 - An unbiased measure of generalized synchronization in multivariate data sets

AU - Stam, C. J.

AU - Van Dijk, B. W.

PY - 2002/3/15

Y1 - 2002/3/15

N2 - The study of complex systems consisting of many interacting subsystems requires the use of analytical tools which can detect statistical dependencies between time series recorded from these subsystems. Typical examples are the electroencephalogram (EEG) and magnetoencephalogram (MEG) which may involve the simultaneous recording of 150 or more time series. Coherency, which is often used to study such data, is only sensitive to linear and symmetric interdependencies and cannot deal with non-stationarity. Recently, several algorithms based upon the concept of generalized synchronization have been introduced to overcome some of the limitations of coherency estimates (e.g. [Physica D 134 (1999) 419; Brain Res. 792 (1998) 24]). However, these methods are biased by the degrees of freedom of the interacting subsystems [Physica D 134 (1999) 419; Physica D 148 (2001) 147]. We propose a novel measure for generalized synchronization in multivariate data sets which avoids this bias and can deal with non-stationary dynamics.

AB - The study of complex systems consisting of many interacting subsystems requires the use of analytical tools which can detect statistical dependencies between time series recorded from these subsystems. Typical examples are the electroencephalogram (EEG) and magnetoencephalogram (MEG) which may involve the simultaneous recording of 150 or more time series. Coherency, which is often used to study such data, is only sensitive to linear and symmetric interdependencies and cannot deal with non-stationarity. Recently, several algorithms based upon the concept of generalized synchronization have been introduced to overcome some of the limitations of coherency estimates (e.g. [Physica D 134 (1999) 419; Brain Res. 792 (1998) 24]). However, these methods are biased by the degrees of freedom of the interacting subsystems [Physica D 134 (1999) 419; Physica D 148 (2001) 147]. We propose a novel measure for generalized synchronization in multivariate data sets which avoids this bias and can deal with non-stationary dynamics.

KW - Alzheimer

KW - Electroencephalogram

KW - Epilepsy

KW - Interdependent systems

KW - Magnetoencephalogram

KW - Non-linear systems

UR - http://www.scopus.com/inward/record.url?scp=0037087387&partnerID=8YFLogxK

U2 - 10.1016/S0167-2789(01)00386-4

DO - 10.1016/S0167-2789(01)00386-4

M3 - Article

AN - SCOPUS:0037087387

SN - 0167-2789

VL - 163

SP - 236

EP - 251

JO - Physica D: Nonlinear Phenomena

JF - Physica D: Nonlinear Phenomena

IS - 3-4

ER -