Detection of spatiotemporal phase patterns in ECoG using adaptive mixture models

Propagating phase patterns of neural activity in the cortex have been found to serve as useful markers for the identification of neural correlates of cognition. In this work we develop an automatic method to detect phase propagation in the form of cones using adaptive mixture models. The present work is the first demonstration of this methodology in actual Electrocorticogram (ECoG) data. The paper discusses results obtained with this novel method.