Detection of propagating phase gradients in EEG signals using Model Field Theory of non-Gaussian mixtures

Model field theory (MFT) is a powerful tool of pattern recognition, which has been used successfully for various tasks involving noisy data and high level of clutter. Detection of spatio-temporal activity patterns in EEG experiments is a very challenging task and it is well-suited for MFT implementation. Previous work on applying MFT for EEG analysis used Gaussian assumption on the mixture components. The present work uses non-Gaussian components for the description of propagating phase-cones, which are more realistic models of the experimentally observed physiological processes. This work introduces MFT equations for non-Gaussian transient processes, and describes the identification algorithm. The method is demonstrated using simulated phase cone data.