The concept of brain connectivity is introduced to explore and understand the organized structure of cortical regions, during the execution of a specific task. Popular methods for measuring brain connectivity include synchronization, coherency and correlation among EEG sites and exploiting the statistical properties of the EEG signals to define the direction of the information flow between the cortical regions. Our approach on the problem of brain connectivity uses the theory of time-space adaptive filters. We use a network of adaptive filters that is self-organised in space and evolves through time in order to model a certain pattern of propagation of EEG signals. Exploiting the properties of these filters can lead us to a robust and flexible method, which can be used in a variety of applications.