scholarly journals Analysis in the frequency domain of multicomponent oscillatory modes of the human electroencephalogram extracted with multivariate empirical mode decomposition

2020 ◽  
Author(s):  
Eduardo Arrufat-Pié ◽  
Mario Estévez-Báez ◽  
José Mario Estévez-Carreras ◽  
Calixto Machado Curbelo ◽  
Gerry Leisman ◽  
...  
Keyword(s):  
Spectral Analysis ◽  
Empirical Mode Decomposition ◽  
A Priori ◽  
Quantitative Eeg ◽  
Frequency Resolution ◽  
Intrinsic Mode Functions ◽  
Multivariate Empirical Mode Decomposition ◽  
Healthy Humans ◽  
Mode Decomposition ◽  
Mixing Problem

AbstractConsidering the properties of the empirical mode decomposition to extract from a signal its natural oscillatory components known as intrinsic mode functions (IMFs), the spectral analysis of these IMFs could provide a novel alternative for the quantitative EEG analysis without a priori establish more or less arbitrary band limits. This approach has begun to be used in the last years for studies of EEG records of patients included in database repositories or including a low number of individuals or of limited EEG leads, but a detailed study in healthy humans has not yet been reported. Therefore, in this study the aims were to explore and describe the main spectral indices of the IMFs of the EEG in healthy humans using a method based on the FFT and another on the Hilbert-Huang transform (HHT). The EEG of 34 healthy volunteers was recorded and decomposed using a recently developed multivariate empirical mode decomposition algorithm. Extracted IMFs were submitted to spectral analysis with, and the results were compared with an ANOVA test. The first six decomposed IMFs from the EEG showed frequency values in the range of the classical bands of the EEG (1.5 to 56 Hz). Both methods showed in general similar results for mean weighted frequencies and estimations of power spectral density, although the HHT is recommended because of its better frequency resolution. It was shown the presence of the mode-mixing problem producing a slight overlapping of spectral frequencies mainly between the IMF3 and IMF4 modes.

A method of eliminating the vibration signal noise of hydropower unit based on NA-MEMD and approximate entropy

2016 ◽  
Vol 231 (2) ◽  
pp. 317-328 ◽  
Author(s):  
Xueli An ◽  
Junjie Yang
Keyword(s):  
Empirical Mode Decomposition ◽  
Real World ◽  
Approximate Entropy ◽  
Vibration Signal ◽  
Signal Denoising ◽  
Intrinsic Mode Functions ◽  
Denoising Method ◽  
Multivariate Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Simulation Signal

A new vibration signal denoising method of hydropower unit based on noise-assisted multivariate empirical mode decomposition (NA-MEMD) and approximate entropy is proposed. Firstly, the NA-MEMD is used to decompose the signal into a number of intrinsic mode functions. Then, the approximate entropy of each component is computed. According to a preset threshold of approximate entropy, these components are reconstructed to denoise vibration signal of hydropower unit. The analysis results of simulation signal and real-world signal show that the proposed method is adaptive and has a good denoising performance. It is very suitable for online denoising of hydropower unit's vibration signal.

scholarly journals The Measurement and Elimination of Mode Splitting: From the Perspective of the Partly Ensemble Empirical Mode Decomposition

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Bin Liu ◽  
Peng Zheng ◽  
Qilin Dai ◽  
Zhongli Zhou
Keyword(s):  
Spectral Analysis ◽  
White Noise ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Permutation Entropy ◽  
Intrinsic Mode Functions ◽  
Mode Splitting ◽  
Mode Decomposition ◽  
Microseismic Signal ◽  
Mode Mixing

The problems of mode mixing, mode splitting, and pseudocomponents caused by intermittence or white noise signals during empirical mode decomposition (EMD) are difficult to resolve. The partly ensemble EMD (PEEMD) method is introduced first. The PEEMD method can eliminate mode mixing via the permutation entropy (PE) of the intrinsic mode functions (IMFs). Then, bilateral permutation entropy (BPE) of the IMFs is proposed as a means to detect and eliminate mode splitting by means of the reconstructed signals in the PEEMD. Moreover, known ingredient component signals are comparatively designed to verify that the PEEMD method can effectively detect and progressively address the problem of mode splitting to some degree and generate IMFs with better performance. The microseismic signal is applied to prove, by means of spectral analysis, that this method is effective.

EEG Signal Processing Based on Multivariate Empirical Mode Decomposition and Common Spatial Pattern Hybrid Algorithm

2019 ◽  
Vol 33 (09) ◽  
pp. 1959030 ◽  
Author(s):  
Linyan Wu ◽  
Tao Wang ◽  
Qi Wang ◽  
Qing Zhu ◽  
Jinhuan Chen
Keyword(s):  
Empirical Mode Decomposition ◽  
Nearest Neighbor ◽  
Classification Model ◽  
Eeg Signal ◽  
Intrinsic Mode Functions ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Common Spatial Pattern ◽  
Multivariate Empirical Mode Decomposition ◽  
Mode Decomposition

The high accuracy of electroencephalogram (EEG) signal classification is the premise for the wide application of brain computer interface (BCI). In this paper, a hybrid method consisting of multivariate empirical mode decomposition (MEMD) and common space pattern (CSP) is proposed to recognize left-hand and right-hand hypothetical motion from EEG signals. Experiments were carried out using the BCI competition II imagery database. EEG signals were decomposed into multiple intrinsic mode functions (IMFs) by MEMD. The IMF functions with high correlation were processed by CSP, and AR coefficients and entropy values were extracted as features. After genetic algorithm optimization, classification is carried out. Our research results show that the K nearest neighbor (KNN) as an optimal classification model produces 85.36% accuracy. We also compare the proposed algorithm with the existing algorithms. The experimental results show that the performance of the proposed algorithm is comparable to or better than that of many existing algorithms.

Rotating Machinery Fault Detection Using EEMD and Bispectrum

2009 ◽  
Author(s):  
Yaguo Lei ◽  
Ming J. Zuo ◽  
Mohammad Hoseini
Keyword(s):  
Empirical Mode Decomposition ◽  
Reconstruction Algorithm ◽  
Ensemble Empirical Mode Decomposition ◽  
Rotating Machinery ◽  
Intrinsic Mode Functions ◽  
Phase Coupling ◽  
Third Order ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Mixing Problem

Ensemble empirical mode decomposition (EEMD) was developed to alleviate the mode-mixing problem in empirical mode decomposition (EMD). With EEMD, the components with physical meaning can be extracted from the signal. The bispectrum, a third-order statistic, helps identify phase-coupling effects, which are useful for detecting faults in rotating machinery. Combining the advantages of EEMD and bispectrum, this paper proposes a new method for detecting such faults. First, the original vibration signals collected from rotating machinery are decomposed by EEMD and a set of intrinsic mode functions (IMFs) is produced. Then, the IMFs are reconstructed into new signals using the weighted reconstruction algorithm developed in this paper. Finally, the reconstructed signals are analyzed via the bispectrum to detect faults. Both simulation examples and gearbox experiments demonstrate that the proposed method can detect gear faults more clearly than can directly performing bispectrum analysis on the original vibration signals.

scholarly journals Spectral Analysis of Electricity Demand Using Hilbert–Huang Transform

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2912
Author(s):  
Joaquin Luque ◽  
Davide Anguita ◽  
Francisco Pérez ◽  
Robert Denda
Keyword(s):  
Spectral Analysis ◽  
Electricity Demand ◽  
Frequency Resolution ◽  
Electrical Networks ◽  
Intrinsic Mode Functions ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Sequence Representation ◽  
Electrical Demand ◽  
Computer Resources

The large amount of sensors in modern electrical networks poses a serious challenge in the data processing side. For many years, spectral analysis has been one of the most used approaches to extract physically meaningful information from a sea of data. Fourier Transform (FT) and Wavelet Transform (WT) are by far the most employed tools in this analysis. In this paper we explore the alternative use of Hilbert–Huang Transform (HHT) for electricity demand spectral representation. A sequence of hourly consumptions, spanning 40 months of electrical demand in Spain, has been used as dataset. First, by Empirical Mode Decomposition (EMD), the sequence has been time-represented as an ensemble of 13 Intrinsic Mode Functions (IMFs). Later on, by applying Hilbert Transform (HT) to every IMF, an HHT spectrum has been obtained. Results show smoother spectra with more defined shapes and an excellent frequency resolution. EMD also fosters a deeper analysis of abnormal electricity demand at different timescales. Additionally, EMD permits information compression, which becomes very significant for lossless sequence representation. A 35% reduction has been obtained for the electricity demand sequence. On the negative side, HHT demands more computer resources than conventional spectral analysis techniques.

scholarly journals A New Islanding Detection Technique using Ensemble Empirical Mode Decomposition

2020 ◽  
Vol 9 (3) ◽  
pp. 461-466
Keyword(s):  
Empirical Mode Decomposition ◽  
Distributed Generation ◽  
Gaussian White Noise ◽  
Ensemble Empirical Mode Decomposition ◽  
Islanding Detection ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Signal Processing Methods ◽  
Mixing Problem ◽  
Mode Mixing

Penetration of distributed generation (DG) is rapidly increasing but their main issue is islanding. Advanced signal processing methods needs a renewed focus in detecting islanding. The proposed scheme is based on Ensemble Empirical Mode Decomposition (EEMD) in which Gaussian white noise is added to original signal which solves the mode mixing problem of Empirical mode decomposition (EMD) and Hilbert transform is applied to obtained Intrinsic mode functions(IMF). The proposed method reliably and accurately detects disturbances at different events

scholarly journals Comparison between traditional FFT and marginal spectra using the Hilbert-Huang transform method for the broadband spectral analysis of the EEG in healthy humans

2020 ◽  
Author(s):  
Eduardo Arrufat-Pié ◽  
Mario Estévez Báez ◽  
José Mario Estévez Carreras ◽  
Calixto Machado Curbelo ◽  
Gerry Leisman ◽  
...  
Keyword(s):  
Spectral Analysis ◽  
Main Tool ◽  
Good Alternative ◽  
Transform Method ◽  
Hilbert Huang Transform ◽  
Multivariate Empirical Mode Decomposition ◽  
Healthy Humans ◽  
Mode Decomposition ◽  
Stationary Signals ◽  
Mean Differences

AbstractThe fast Fourier transform (FFT), has been the main tool for the EEG spectral analysis (SPA). However, as the EEG dynamics shows nonlinear and non-stationary behavior, results using the FFT approach may result meaningless. A novel method has been developed for the analysis of nonlinear and non-stationary signals known as the Hilbert-Huang transform method. In this study we describe and compare the spectral analyses of the EEG using the traditional FFT approach with those calculated with the Hilbert marginal spectra (HMS) after decomposition of the EEG with a multivariate empirical mode decomposition algorithm. Segments of continuous 60-seconds EEG recorded from 19 leads of 47 healthy volunteers were studied. Although the spectral indices calculated for the explored EEG bands showed significant statistical differences for different leads and bands, a detailed analysis showed that for practical purposes both methods performed substantially similar. The HMS showed a reduction of the alpha activity (−5.64%), with increment in the beta-1 (+1.67%), and gamma (+1.38%) fast activity bands, and also an increment in the theta band (+2.14%), and in the delta (+0.45%) band, and vice versa for the FFT method. For the weighted mean frequencies insignificant mean differences (lower than 1Hz) were observed between both methods for the delta, theta, alpha, beta-1 and beta-2 bands, and only for the gamma band values for the HMS were 3 Hz higher than with the FFT method. The HMS may be considered a good alternative for the SPA of the EEG when nonlinearity or non-stationarity may be present.

Ensemble Noise-Reconstructed Empirical Mode Decomposition for Mechanical Fault Detection

2013 ◽  
Vol 135 (2) ◽  
Author(s):  
Jing Yuan ◽  
Zhengjia He ◽  
Jun Ni ◽  
Adam John Brzezinski ◽  
Yanyang Zi
Keyword(s):  
Fault Detection ◽  
Empirical Mode Decomposition ◽  
Measured Signal ◽  
Accelerometer Data ◽  
Intrinsic Mode Functions ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Mixing Problem ◽  
Emd Method ◽  
Mode Mixing

Various faults inevitably occur in mechanical systems and may result in unexpected failures. Hence, fault detection is critical to reduce unscheduled downtime and costly breakdowns. Empirical mode decomposition (EMD) is an adaptive time-frequency domain signal processing method, potentially suitable for nonstationary and/or nonlinear processes. However, the EMD method suffers from several problems such as mode mixing, defined as intrinsic mode functions (IMFs) with incorrect scales. In this paper, an ensemble noise-reconstructed EMD method is proposed to ameliorate the mode mixing problem and denoise IMFs for enhancing fault signatures. The proposed method defines the IMF components as an ensemble mean of EMD trials, where each trial is obtained by sifting signals that have been reconstructed using the estimated noise present in the measured signal. Unlike traditional denoising methods, the noise inherent in the input data is reconstructed and used to reduce the background noise. Furthermore, the reconstructed noise helps to project different scales of the signal onto their corresponding IMFs, instrumental in alleviating the mode mixing problem. Two critical issues concerned in the method, i.e., the noise estimation strategy and the number of EMD trials required for denoising are discussed. Furthermore, a comprehensive noise-assisted EMD method is proposed, which includes the proposed method and ensemble EMD (EEMD). Numerical simulations and experimental case studies on accelerometer data collected from an industrial shaving process are used to demonstrate and validate the proposed method. Results show that the proposed method can both detect impending faults and isolate multiple faults. Hence, the proposed method can act as a promising tool for mechanical fault detection.

A fast online bandwidth empirical mode decomposition scheme for avoidance of the mode mixing problem

2017 ◽  
Vol 232 (20) ◽  
pp. 3652-3674 ◽  
Author(s):  
SH Momeni Massouleh ◽  
Seyed Ali Hosseini Kordkheili ◽  
H Mohammad Navazi
Keyword(s):  
Empirical Mode Decomposition ◽  
Low Frequency ◽  
Dominant Frequency ◽  
Auxiliary Function ◽  
Intrinsic Mode Functions ◽  
Online Data ◽  
Decomposition Scheme ◽  
Mode Decomposition ◽  
Mixing Problem ◽  
Mode Mixing

The main objective of this work is to propose a scheme to extract intrinsic mode functions of online data with an acceptable speed as well as accuracy. For this purpose, an individual block framework method is firstly employed to extract the intrinsic mode functions. In this method, buffers are selected such that they overlap with their neighbors to prevent the end effect errors with no need for the averaging process. And in order to avoid the mode mixing problem, a bandwidth empirical mode decomposition scheme is developed to effectively improve the results. Through this scheme, an auxiliary function made of both high- and low-frequency components corresponding to noise and dominant frequency is added to data for the strengthening of the components for the better extraction of intrinsic mode functions during sifting process. An index criterion as well as a threshold limit is also introduced to separate high- and low-frequency parts of data at desired frequency range. Advantages of the proposed scheme are assessed and comparisons with the available methods are presented. Solution of different types of examples and experimentally generated data for two faulty ball bearings reveals that the present easily implemented scheme achieves results with lower computational efforts and accuracy.

SIMULATION OF EARTHQUAKE RECORDS BY MEANS OF EMPIRICAL MODE DECOMPOSITION AND HILBERT SPECTRAL ANALYSIS

2014 ◽  
Vol 08 (01) ◽  
pp. 1450002 ◽  
Author(s):  
ABDOLLAH BAGHERI ◽  
AMIR A. FATEMI ◽  
GHOLAMREZA GHODRATI AMIRI
Keyword(s):  
Spectral Analysis ◽  
Empirical Mode Decomposition ◽  
Time History ◽  
Response Spectra ◽  
Frequency Function ◽  
Intrinsic Mode Functions ◽  
Hilbert Huang Transform ◽  
Earthquake Records ◽  
Mode Decomposition ◽  
Time Histories

One of the most important problems in the design of earthquake resistance structures at sites with no strong ground motion data is the generation and simulation of earthquake records. In this paper, an effective method based on Hilbert–Huang transform for the simulation of earthquake time histories is presented. The Hilbert–Huang transform consists of the empirical mode decomposition and Hilbert spectral analysis. Earthquake time histories decompose via empirical mode decomposition to obtain the intrinsic mode functions of earthquake time history. Any of intrinsic mode functions is simulated based on the proposed method for simulation. The ground frequency function of the presented model is estimated using Hilbert spectral analysis for the simulation of earthquake accelerograms. The proposed method has been applied to three earthquake records to demonstrate the efficiency and reliability of the approach. The obtained results of simulating method by comparison between pseudo-acceleration and pseudo-velocity response spectra of actual and the average of simulated time histories for these three earthquakes reveal that the simulated earthquake time histories well preserve the significant properties and the nonstationary characteristics of the actual earthquake records. The results indicated that there is a good accord between the response spectra of simulated and genuine time histories.

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