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.