Data analysis using a combination of independent component analysis and empirical mode decomposition

Lin, Shih-Lin; Tung, Pi Cheng; Huang, Norden E.

doi:10.1103/physreve.79.066705

Cited by 29 publications

(15 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Owing to no a priori assumptions regarding the data nature, EMD has found applications in the analysis of nonlinear and non-stationary signals (e.g. Duffy 2004;Gautama et al 2004;Janosi & Muller 2005;Wu & Hu 2006;Huang & Wu 2008;Lin et al 2009). …”

Section: Introductionmentioning

confidence: 99%

Multivariate empirical mode decomposition

2009

View full text Add to dashboard Cite

Despite empirical mode decomposition (EMD) becoming a de facto standard for time-frequency analysis of nonlinear and non-stationary signals, its multivariate extensions are only emerging; yet, they are a prerequisite for direct multichannel data analysis. An important step in this direction is the computation of the local mean, as the concept of local extrema is not well defined for multivariate signals. To this end, we propose to use real-valued projections along multiple directions on hyperspheres (n-spheres) in order to calculate the envelopes and the local mean of multivariate signals, leading to multivariate extension of EMD. To generate a suitable set of direction vectors, unit hyperspheres (n-spheres) are sampled based on both uniform angular sampling methods and quasi-Monte Carlo-based low-discrepancy sequences. The potential of the proposed algorithm to find common oscillatory modes within multivariate data is demonstrated by simulations performed on both hexavariate synthetic and real-world human motion signals.

show abstract

Section: Introductionmentioning

confidence: 99%

Multivariate empirical mode decomposition

2009

View full text Add to dashboard Cite

show abstract

“…Meanwhile, we also believe that appropriate integration of biologically meaningful, statistically sound, and computational feasible models of fMRI signals into the data-driven signal analysis processes could significantly contribute to achieving desirable outcomes. In this sense, a trade-off between the data-driven and model-driven approaches that takes the advantages of both methodologies can be achieved [55]. …”

Section: Discussionmentioning

confidence: 99%

FMRI Signal Analysis Using Empirical Mean Curve Decomposition

Fan

Zhu

et al. 2013

IEEE Trans. Biomed. Eng.

View full text Add to dashboard Cite

Functional magnetic resonance imaging (fMRI) time series is non-linear and composed of components at multiple temporal scales, which presents significant challenges to its analysis. In the literature, significant effort has been devoted into model-based fMRI signal analysis, while much less attention has been directed to data-driven fMRI signal analysis. In this paper, we present a novel data-driven multi-scale signal decomposition framework named Empirical Mean Curve Decomposition (EMCD). Targeted on functional brain mapping, the EMCD optimizes mean envelopes from fMRI signals and iteratively extracts coarser-to-finer scale signal components. The EMCD framework was applied to infer meaningful low-frequency information from Blood Oxygenation Level Dependent (BOLD) signals from resting state fMRI, task-based fMRI, and natural stimulus fMRI, and promising results are obtained.

show abstract

“…Nevertheless, EMD has been successfully applied in many scientific and engineering fields, e.g. (Erturk et al, 2013;Lin et al, 2009;Huang and Wu, 2008), producing insightful results.…”

Section: Huang-hilbert Transformmentioning

confidence: 99%

On the Phase Coupling of Two Components Mixing in Empirical Mode Decomposition

Laszuk

Cadenas

Nasuto

2016

Adv. Data Sci. Adapt. Data Anal.

View full text Add to dashboard Cite

This paper investigates frequency mixing effect of empirical mode decomposition (EMD) and explores whether it can be explained by simple phase coupling between components of the input signal. The input is assumed to be a linear combination of harmonic oscillators. The hypothesis was tested assuming that phases of input signals’ components would couple according to Kuramoto’s model. Using a Kuramoto’s model with as many oscillators as the number of intrinsic mode functions (result of EMD), the model’s parameters were adjusted by a particle swarm optimization (PSO) method. The results show that our hypothesis is plausible, however, a different coupling mechanism than the simple sine-coupling Kuramoto’s model are likely to give better results.

show abstract

Data analysis using a combination of independent component analysis and empirical mode decomposition

Cited by 29 publications

References 40 publications

Multivariate empirical mode decomposition

Multivariate empirical mode decomposition

FMRI Signal Analysis Using Empirical Mean Curve Decomposition

On the Phase Coupling of Two Components Mixing in Empirical Mode Decomposition

Contact Info

Product

Resources

About