Combined EMD-sLORETA Analysis of EEG Data Collected during a Contour Integration Task

Al-Subari, Karema; Al-Baddai, Saad; Tomé, Ana Maria; Volberg, Gregor; Ludwig, Bernd

doi:10.1371/journal.pone.0167957

Cited by 13 publications

(11 citation statements)

References 61 publications

(96 reference statements)

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“…It proves excessive activation of beta activity in the middle frontal by the sLORETA-VMD approach than the conventional approaches. This outcome is in line with the conclusions drawn in previous studies that stated decreased precentral gyrus activation in depression and suicidal attempts [13,14].…”

Section: Resultssupporting

confidence: 93%

“…Zosso presented an alternative to EMD, a new non-recursive algorithm, the VMD model. Also, latest forms of studies that have combined the EEG denoising in the pre-processing stage with inverse solution methods have been done using EMD [13]. Better localization results have been attained, as EMD has been successful in the removal of fractional and white Gaussian noise.…”

Section: Introductionmentioning

confidence: 99%

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Electroencephalography-Based Source Localization for Depression Using Standardized Low Resolution Brain Electromagnetic Tomography – Variational Mode Decomposition Technique

Kaur¹,

Singh²,

Sahni³

2019

Eur Neurol

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Background: Electroencephalography (EEG) may be used as an objective diagnosis tool for diagnosing various disorders. Recently, source localization from EEG is being used in the analysis of real-time brain monitoring applications. However, inverse problem reduces the accuracy in EEG signal processing systems. Objectives: This paper presents a new method of EEG source localization using variational mode decomposition (VMD) and standardized the low resolution brain electromagnetic tomography (sLORETA) inverse model. The focus is to compare the effectiveness of the proposed approach for EEG signals of depression patients. Method: As the first stage, real EEG recordings corresponding to depression patients are decomposed into various mode functions by applying VMD. Then, closely related functions are analyzed using the inverse modelling-based source localization procedures such as sLORETA. Simulations have been carried out on real EEG databases for depression to demonstrate the effectiveness of the proposed techniques. Results: The performance of the algorithm has been assessed using localization error (LE), mean square error and signal to noise ratio output corresponding to simulated EEG dipole sources and real EEG signals for depression. In order to study the spatial resolution for cortical potential distribution, the main focus has been on studying the effects of noise sources and estimating LE of inverse solutions. More accurate and robust localization results show that this methodology is very promising for EEG source localization of depression signals. Conclusion: It can be said that proposed algorithm efficiently suppresses the influence of noise in the EEG inverse problem using simulated EEG activity and EEG database for depression. Such a system may offer an effective solution for clinicians as a crucial stage of EEG pre-processing in automated depression detection systems and may prevent delay in diagnosis.

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Section: Resultssupporting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Electroencephalography-Based Source Localization for Depression Using Standardized Low Resolution Brain Electromagnetic Tomography – Variational Mode Decomposition Technique

Kaur¹,

Singh²,

Sahni³

2019

Eur Neurol

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“…A novel fully data-driven method (Huang et al 1998) for the analysis of non-Gaussian, nonlinear and non-stationary signals, the empirical mode decomposition (EMD), followed by the Hilbert transform of the extracted modal components with the EMD, known also as the Hilbert-Huang transform method, has been introduced for the study of EEG signals (Al-Subari et al 2015;Al-Subari et al 2016;Carella et al 2018;Chatterjee 2019;Chuang et al 2019;Dinares-Ferran et al 2018;Estevez-Baez et al 2017a, b, c;Hansen et al 2019;Hassan, Bhuiyan 2017;Hou et al 2018;Javed et al 2019;Rahman, Fattah 2017). The modal components extracted using the EMD can be considered as an adaptive spectral band (Noshadi et al 2014).…”

Section: Introductionmentioning

confidence: 99%

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

Arrufat-Pié¹,

Estévez‐Báez²,

Estévez‐Carreras³

et al. 2020

Preprint

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Considering 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.

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“…for the analysis of non-Gaussian, nonlinear and non-stationary signals, the empirical mode decomposition (EMD), followed by the Hilbert transform of the extracted modal components with the EMD, known also as the Hilbert-Huang transform method, has been successfully used in geophysical studies (Huang et al 1998;Huang, Wu 2008) , image analysis (Nunes et al 2003) , thermal profiles analysis (Subhani et al 2016), and power quality analysis (Camarena-Martinez et al 2016). EMD has demonstrated itself to be a reliable and effective method in the processing of different biomedical signals such as the EEG (Al-Subari et al 2015;Al-Subari et al 2016;Amo et al 2017;Estevez-Baez et al 2017a, b;Hassan, Bhuiyan 2017;Hou et al 2018;Rahman, Fattah 2017;Carella et al 2018;Chatterjee 2019;Dinares-Ferran et al 2018;Chuang et al 2019;Hansen et al 2019;Javed et al 2019). There are two main procedures to calculate and graphically represent the results of the Hilbert-Huang method: the time frequency Hilbert spectrum, and as an alternative the Hilbert marginal spectrum (HMS), consisting in selecting a lower frequency resolution value, leaving the time axis undisturbed.…”

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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Combined EMD-sLORETA Analysis of EEG Data Collected during a Contour Integration Task

Cited by 13 publications

References 61 publications

Electroencephalography-Based Source Localization for Depression Using Standardized Low Resolution Brain Electromagnetic Tomography – Variational Mode Decomposition Technique

Electroencephalography-Based Source Localization for Depression Using Standardized Low Resolution Brain Electromagnetic Tomography – Variational Mode Decomposition Technique

Analysis in the frequency domain of multicomponent oscillatory modes of the human electroencephalogram extracted with multivariate 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

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