Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Multivariate Empirical Mode Decomposition for Quantifying Multivariate Phase Synchronization | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2011 Article ID 615717 13 pages doi 2011 615717 Research Article Multivariate Empirical Mode Decomposition for Quantifying Multivariate Phase Synchronization Ali Yener Mutlu and Selin Aviyente Department of Electrical and Computer Engineering Michigan State University East Lansing MI 48824 USA Correspondence should be addressed to Selin Aviyente aviyente@ Received 3 August 2010 Accepted 8 November 2010 Academic Editor Patrick Flandrin Copyright 2011 A. Y. Mutlu and S. Aviyente. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Quantifying the phase synchrony between signals is important in many different applications including the study of the chaotic oscillators in physics and the modeling of the joint dynamics between channels of brain activity recorded by electroencephalogram EEG . Current measures of phase synchrony rely on either the wavelet transform or the Hilbert transform of the signals and suffer from constraints such as the limit on time-frequency resolution in the wavelet analysis and the prefiltering requirement in Hilbert transform. Furthermore the current phase synchrony measures are limited to quantifying bivariate relationships and do not reveal any information about multivariate synchronization patterns which are important for understanding the underlying oscillatory networks. In this paper we address these two issues by employing the recently introduced multivariate empirical mode decomposition MEMD for quantifying multivariate phase synchrony. First an MEMD-based bivariate phase synchrony measure is defined for a more robust description of time-varying phase synchrony across frequencies. Second the proposed bivariate phase synchronization index is used to quantify multivariate .
