A Multivariate Time-Frequency Based Phase Synchrony Measure for Quantifying Functional Connectivity in the Brain

Tarih: 23.12.2014
Yer: Kandilli Kampüs, AZ-19

Ali Yener MUTLU, Ph.D.

Time-varying phase synchrony is an important bivariate measure that quantifies the dynamics between nonstationary signals and has been widely used in many applications including chaotic oscillators in physics and multichannel electroencephalography recordings in neuroscience. Current state-of-the-art in time-varying phase estimation uses either the Hilbert transform or the continuous wavelet transform of the signals. Both of these methods have some major drawbacks such as the assumption that the signals are narrowband for the Hilbert transform and the nonuniform time-frequency resolution inherent to the wavelet analysis. In this talk, a novel phase synchrony measure based on the Rihaczek distribution and Reduced Interference Rihaczek distribution belonging to Cohen's class and its applications to characterizing functional brain connectivity will be introduced. These distributions offer phase estimates with uniformly high time-frequency resolution which can be used for defining time and frequency dependent phase synchrony. The proposed method is evaluated through both simulations and  application to electroencephalogram (EEG) data containing error-related negativity (ERN) related to cognitive control. The new measure is compared with existing methods and its effectiveness in quantifying multivariate synchronization of different brain regions is demonstrated.

About the Speaker:

Dr. Ali Yener Mutlu obtained an undergraduate degree in Electrical and Electronic Engineering at Bogazici University in 2008 and a PhD from Electrical Engineering at Michigan State University in 2012. His research focus on analyzing time-varying functional brain networks using statistical signal processing methods, in particularly non-stationary signal analysis, time-frequency distributions and graph theory. This research addresses the problem of understanding neuronal mechanisms underlying many brain diseases and psychopathologies including schizophrenia. Due to his graduate research work, he received ‘College of Engineering Outstanding Graduate Research Award’. Apart from academic progress, he also worked at General Electric Global Research Center in NY as a research engineer. After completing his studies, he returned to Turkey where he currently works as a senior R&D engineer and project manager at Netas.