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Title: Modeling brain connectivity in real time Authors:  Hernando Ombao - King Abdullah University of Science and Technology (KAUST) (Saudi Arabia) [presenting]
Abstract: The motivation comes from the problem of characterizing multi-scale changes in multivariate time series resulting from an external stimulus or shock to the system. One particular goal is to develop a method that can track real-time changes in dependence. A quick overview of the classical measures will be covered: coherence, partial coherence and dual-frequency coherence and then introduce some non-stationary generalizations of these (in particular, the evolutionary dual-frequency coherence). We then discuss partial directed coherence which, unlike the previously mentioned measures, can capture directionality between components under the framework of vector autoregressive processes. Some of the real-time techniques for estimating the different measures of connectivity and for extracting low-dimensional signal summaries will be covered. These methods will be critical for understanding biofeedback and adjusting the stimuli adaptively during the experiment. These methods will be applied to various brain signals to track dynamic changes in connectivity in an experiment that seeks to find associations between brain physiological signals and creative thinking.