Time-Frequency Analysis of EEG Asymmetry Using Bivariate Empirical Mode Decomposition

Park, Cheolsoo; Looney, David; Kidmose, Preben; Ungstrup, M.; Mandic, Danilo P.

doi:10.1109/tnsre.2011.2116805

Cited by 87 publications

(21 citation statements)

References 28 publications

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“…Theta oscillation was selected using EMD in Reference [43]. In Reference [44], the authors demonstrated a better localization of time-varying frequency components of mu and beta rhythms during motor imagery using the EMD algorithm. In their following work [45], they used an extension of the algorithm of EMD, named multivariate EMD (MEMD), to circumvent the problem of cross-channel interdependence in a 64-channel setup.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Gamma-Band Activity from Human EEG Using Empirical Mode Decomposition

Amo

Santiago

Barea

et al. 2017

Sensors

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The purpose of this paper is to determine whether gamma-band activity detection is improved when a filter, based on empirical mode decomposition (EMD), is added to the pre-processing block of single-channel electroencephalography (EEG) signals. EMD decomposes the original signal into a finite number of intrinsic mode functions (IMFs). EEGs from 25 control subjects were registered in basal and motor activity (hand movements) using only one EEG channel. Over the basic signal, IMF signals are computed. Gamma-band activity is computed using power spectrum density in the 30–60 Hz range. Event-related synchronization (ERS) was defined as the ratio of motor and basal activity. To evaluate the performance of the new EMD based method, ERS was computed from the basic and IMF signals. The ERS obtained using IMFs improves, from 31.00% to 73.86%, on the original ERS for the right hand, and from 22.17% to 47.69% for the left hand. As EEG processing is improved, the clinical applications of gamma-band activity will expand.

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Section: Introductionmentioning

confidence: 99%

Analysis of Gamma-Band Activity from Human EEG Using Empirical Mode Decomposition

Amo

Santiago

Barea

et al. 2017

Sensors

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“…EMD is a fully data-driven method for decomposing a time series into AM/FM components which reflect its natural oscillations. As EMD makes no prior assumptions on the data it becomes suitable for the analysis of nonlinear and non-stationary processes (Huang et al 1998;Park et al 2011). The EMD algorithm extracts the IMFs using an iterative technique called "sifting" (Huang et al 1998).…”

Section: Empirical Mode Decomposition (Emd)mentioning

confidence: 99%

“…The HMS has been used by different authors after applying the Hilbert-Huang method to the EEG signal (Chen et al 2010;Li 2006;Li et al 2008;Chen et al 2016;Xiangjun et al 2017;Zhu et al 2015;Park et al 2011) but to our knowledge, the possible differences between the results obtained using the HMS and the conventional FFT spectra in resting conditions in healthy humans have not been studied in detail, and it results imperative, because for many years the universally used method for the spectral analysis of the EEG has used the FFT spectra. The advantages and possible limitations of the novel alternative approach, in this case the use of the HMS, could be better appreciated and could contribute to their more extended use in EEG investigations.…”

Section: Introductionmentioning

confidence: 99%

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

Arrufat-Pié¹,

Jme

et al. 2020

Preprint

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The 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.

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“…EMD-based noise removal method has been used recently in many fields such as biology, ocean, medicine, acoustics, fault diagnosis (Huang et al 1999; Liu et al 2006; Lee et al 2011; Park et al 2011; Ahrabian et al 2013; Moghtaderi et al 2013). It does not need to select the basis function in advance and has better adaptive feature.…”

Section: Introductionmentioning

confidence: 99%

An approach using ensemble empirical mode decomposition to remove noise from prototypical observations on dam safety

Li²,

Chen³

et al. 2016

SpringerPlus

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It is very important for dam safety control to identify reasonably dam behavior according to the prototypical observations on deformation, seepage, stress, etc. However, there are many cases in which the noise corrupts the prototypical observations, and it must be removed from the data. Considering the nonlinear and non-stationary characteristics of data series with signal intermittency, an ensemble empirical mode decomposition (EEMD)-based method is presented to remove noise from prototypical observations on dam safety. Its basic principle and implementation process are discussed. The key parameters and rules, which can adapt the noise removal requirements of prototypical observations on dam safety, are given. The displacement of one actual dam is taken as an example. The noise removal capability of EEMD-based method is assessed. It is indicated that the dam displacement feature can be reflected more clearly by removing noise from prototypical observations on dam displacement. The statistical model, which is built according to noise-removed data series, can provide the more precise forecast for structural behavior.

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Time-Frequency Analysis of EEG Asymmetry Using Bivariate Empirical Mode Decomposition

Cited by 87 publications

References 28 publications

Analysis of Gamma-Band Activity from Human EEG Using Empirical Mode Decomposition

Analysis of Gamma-Band Activity from Human EEG Using Empirical Mode Decomposition

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

An approach using ensemble empirical mode decomposition to remove noise from prototypical observations on dam safety

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