Institute of Electrical and Electronics Engineers Inc.;IEEE
摘要:
摘要： This article addresses data-driven time-frequency (T-F) analysis of multivariate signals, which is achieved through the empirical mode decomposition (EMD) algorithm and its noise assisted and multivariate extensions, the ensemble EMD (EEMD) and multivariate EMD (MEMD). Unlike standard approaches that project data onto predefined basis functions (harmonic, wavelet) thus coloring the representation and blurring the interpretation, the bases for EMD are derived from the data and can be nonlinear and nonstationary. For multivariate data, we show how the MEMD aligns intrinsic joint rotational modes across the intermittent, drifting, and noisy data channels, facilitating advanced synchrony and data fusion analyses. Simulations using real-world case studies illuminate several practical aspects, such as the role of noise in T-F localization, dealing with unbalanced multichannel data, and nonuniform sampling for computational efficiency. 其他題名： MSP 出版者： IEEE 出版日期： 2013-11 出處： IEEE signal processing magazine, 2013-11, Vol.30 (6), p.74-86 資源來源： IEEE Electronic Library (IEL) 識別號： ISSN: 1053-5888 識別號： DOI: 10.1109/MSP.2013.2267931 識別號： CODEN: ISPRE6
