Connectivity estimation of three parametric methods on simulated electroencephalogram signals.

The global framework of this paper is the connectivity estimation in multichannel electroencephalogram (EEG) recordings, modeled as multidimensional autoregressive (AR) processes. The coherence, directed transfer function and partial directed coherence functions are evaluated on two simulated EEG signals for their later application on real EEG recordings. The results were evaluated computing the relative error and a second proposed performance criterion (eta) based on the entropy of the estimated connectivity matrix.