scholarly journals Low-Density EEG for Neural Activity Reconstruction Using Multivariate Empirical Mode Decomposition

2020 ◽  
Vol 14 ◽  
Author(s):  
Andres Soler ◽  
Pablo A. Muñoz-Gutiérrez ◽  
Maximiliano Bueno-López ◽  
Eduardo Giraldo ◽  
Marta Molinas
Keyword(s):  
Empirical Mode Decomposition ◽  
Neural Activity ◽  
Low Density ◽  
Multivariate Empirical Mode Decomposition ◽  
Mode Decomposition

scholarly journals Low-Density EEG for Neural Activity Reconstruction Using Multivariate Empirical Mode Decomposition

2019 ◽  
Author(s):  
Andrés Felipe Soler ◽  
Pablo A. Muñoz-Gutiérrez ◽  
Maximiliano Bueno-López ◽  
Eduardo Giraldo ◽  
Marta Molinas
Keyword(s):  
Empirical Mode Decomposition ◽  
Neural Activity ◽  
Low Density ◽  
Multiple Channel ◽  
Time Frequency ◽  
Multivariate Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Channel Analysis ◽  
The Mean ◽  
Mixing Problem

AbstractSeveral approaches can be used for estimating neural activity. The main differences between them are in the apriori information used and their sensibility to high noise levels. Empirical Mode Decomposition (EMD) has been recently applied to Electroencephalography EEG-based neural activity reconstruction to provide apriori time-frequency information to improve the neural activity estimation. EMD has the specific ability to identify independent oscillatory modes in non-stationary signals with multiple oscillatory components. The various attempts to use EMD in EEG analysis, however, did not provide yet the best reconstructions due to the intrinsic mode mixing problem of EMD. Some previous works have used a single-channel analysis and in other cases, multiple-channel have been used for other applications. In this paper, we present a study about multiple-channel analysis using Multivariate Empirical Mode Decomposition (MEMD) as a method to attenuate the mode mixing problem and to provide apriori useful time-frequency information to the reconstruction of neuronal activity using several low-density EEG electrode montages. The methods were evaluated over real and synthetic EEG data, in which the reconstructions were performed using multiple sparse priors (MSP) method with several electrode numbers of 32, 16, and 8, and the source reconstruction quality was measured using the Wasserstein Metric. Comparing the solutions when no pre-processing was made and when MEMD was applied, the source reconstructions were improved using MEMD as apriori information in the low-density montage of 8 and 16 electrodes. The mean source reconstruction error on a real EEG dataset was reduced a 59.42% and 66.04% for the 8 and 16 electrodes montages respectively, and on a simulated EEG with three active sources, the mean error was reduced an 87.31% and 31.45% for the same electrodes montages.

scholarly journals Multivariate empirical mode decomposition–based structural damage localization using limited sensors

2021 ◽  
pp. 107754632110069
Author(s):  
Sandeep Sony ◽  
Ayan Sadhu
Keyword(s):  
Empirical Mode Decomposition ◽  
Structural Damage ◽  
Cost Effective ◽  
Threshold Value ◽  
Damage Localization ◽  
Time Frequency ◽  
Multivariate Empirical Mode Decomposition ◽  
Percentage Difference ◽  
Mode Decomposition

In this article, multivariate empirical mode decomposition is proposed for damage localization in structures using limited measurements. Multivariate empirical mode decomposition is first used to decompose the acceleration responses into their mono-component modal responses. The major contributing modal responses are then used to evaluate the modal energy for the respective modes. A damage localization feature is proposed by calculating the percentage difference in the modal energies of damaged and undamaged structures, followed by the determination of the threshold value of the feature. The feature of the specific sensor location exceeding the threshold value is finally used to identify the location of structural damage. The proposed method is validated using a suite of numerical and full-scale studies. The validation is further explored using various limited measurement cases for evaluating the feasibility of using a fewer number of sensors to enable cost-effective structural health monitoring. The results show the capability of the proposed method in identifying as minimal as 2% change in global modal parameters of structures, outperforming the existing time–frequency methods to delineate such minor global damage.

scholarly journals Early Fault Detection Method for Rotating Machinery Based on Harmonic-Assisted Multivariate Empirical Mode Decomposition and Transfer Entropy

Entropy ◽  
2018 ◽  
Vol 20 (11) ◽  
pp. 873 ◽  
Author(s):  
Zhe Wu ◽  
Qiang Zhang ◽  
Lixin Wang ◽  
Lifeng Cheng ◽  
Jingbo Zhou
Keyword(s):  
Fault Detection ◽  
Empirical Mode Decomposition ◽  
High Frequency ◽  
Rotating Machinery ◽  
Transfer Entropy ◽  
Multivariate Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Coupling Characteristics ◽  
High Frequency Harmonic ◽  
Frequency Harmonic

It is a difficult task to analyze the coupling characteristics of rotating machinery fault signals under the influence of complex and nonlinear interference signals. This difficulty is due to the strong noise background of rotating machinery fault feature extraction and weaknesses, such as modal mixing problems, in the existing Ensemble Empirical Mode Decomposition (EEMD) time–frequency analysis methods. To quantitatively study the nonlinear synchronous coupling characteristics and information transfer characteristics of rotating machinery fault signals between different frequency scales under the influence of complex and nonlinear interference signals, a new nonlinear signal processing method—the harmonic assisted multivariate empirical mode decomposition method (HA-MEMD)—is proposed in this paper. By adding additional high-frequency harmonic-assisted channels and reducing them, the decomposing precision of the Intrinsic Mode Function (IMF) can be effectively improved, and the phenomenon of mode aliasing can be mitigated. Analysis results of the simulated signals prove the effectiveness of this method. By combining HA-MEMD with the transfer entropy algorithm and introducing signal processing of the rotating machinery, a fault detection method of rotating machinery based on high-frequency harmonic-assisted multivariate empirical mode decomposition-transfer entropy (HA-MEMD-TE) was established. The main features of the mechanical transmission system were extracted by the high-frequency harmonic-assisted multivariate empirical mode decomposition method, and the signal, after noise reduction, was used for the transfer entropy calculation. The evaluation index of the rotating machinery state based on HA-MEMD-TE was established to quantitatively describe the degree of nonlinear coupling between signals to effectively evaluate and diagnose the operating state of the mechanical system. By adding noise to different signal-to-noise ratios, the fault detection ability of HA-MEMD-TE method in the background of strong noise is investigated, which proves that the method has strong reliability and robustness. In this paper, transfer entropy is applied to the fault diagnosis field of rotating machinery, which provides a new effective method for early fault diagnosis and performance degradation-state recognition of rotating machinery, and leads to relevant research conclusions.

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