Journal ArticleDOI
Multivariate empirical mode decomposition
TLDR
The proposed algorithm to use real-valued projections along multiple directions on hyperspheres in order to calculate the envelopes and the local mean of multivariate signals, leading to multivariate extension of EMD.Abstract:
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.read more
Citations
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Journal ArticleDOI
Classification of Seizure and Nonseizure EEG Signals Using Empirical Mode Decomposition
Varun Bajaj,Ram Bilas Pachori +1 more
TL;DR: The proposed method for classification of EEG signals based on the bandwidth features (BAM and BFM) and the LS-SVM has provided better classification accuracy than the method adopted by Liang and coworkers in their study published in 2010.
Journal ArticleDOI
Extracting spatial–temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition
TL;DR: The DMD approach is validated on sub-dural electrode array recordings from human subjects performing a known motor task, and the resulting analysis combines key features of performing PCA in space and power spectral analysis in time, making it particularly suitable for analyzing large-scale neural recordings.
Journal ArticleDOI
Filter Bank Property of Multivariate Empirical Mode Decomposition
TL;DR: It is found that, similarly to EMD, MEMD also essentially acts as a dyadic filter bank on each channel of the multivariate input signal, but better aligns the corresponding intrinsic mode functions from different channels across the same frequency range which is crucial for real world applications.
Journal ArticleDOI
Empirical Mode Decomposition-Based Time-Frequency Analysis of Multivariate Signals: The Power of Adaptive Data Analysis
TL;DR: 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.
Journal ArticleDOI
Seizure classification in EEG signals utilizing Hilbert-Huang transform
Rami J. Oweis,Enas Abdulhay +1 more
TL;DR: An original tool for EEG signal processing giving physicians the possibility to diagnose brain functionality abnormalities is presented in this paper and results indicate the usefulness of the tool and its use as an efficient diagnostic tool.
References
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Journal ArticleDOI
The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
Norden E. Huang,Zheng Shen,Steven R. Long,Man-Li C. Wu,Hsing H. Shih,Quanan Zheng,Nai-Chyuan Yen,C. C. Tung,Henry H. Liu +8 more
TL;DR: In this paper, a new method for analysing nonlinear and nonstationary data has been developed, which is the key part of the method is the empirical mode decomposition method with which any complicated data set can be decoded.
Book
Random number generation and quasi-Monte Carlo methods
TL;DR: This chapter discusses Monte Carlo methods and Quasi-Monte Carlo methods for optimization, which are used for numerical integration, and their applications in random numbers and pseudorandom numbers.
Journal ArticleDOI
A review on Hilbert‐Huang transform: Method and its applications to geophysical studies
Norden E. Huang,Zhaohua Wu +1 more
TL;DR: Hilbert-Huang transform, consisting of empirical mode decomposition and Hilbert spectral analysis, is a newly developed adaptive data analysis method, which has been used extensively in geophysical research.
Journal ArticleDOI
A confidence limit for the empirical mode decomposition and Hilbert spectral analysis
Norden E. Huang,Man-Li C. Wu,Steven R. Long,Samuel S. P. Shen,Wendong Qu,Per Gloersen,Kuang L. Fan +6 more
TL;DR: The confidence limit of the method here termed EMD/HSA (for empirical mode decomposition/Hilbert spectral analysis) is introduced by using various adjustable stopping criteria in the sifting processes of the EMD step to generate a sample set of intrinsic mode functions (IMFs) as mentioned in this paper.
BookDOI
Hilbert-Huang transform and its applications
TL;DR: The principle and insufficiency of Hilbert-Huang transform is introduced, several improved strategies are put forward, and some simulations are proceeds some simulations.
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Ensemble empirical mode decomposition: a noise-assisted data analysis method
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