Multivariate Variational Mode Decomposition

Rehman, Naveed ur; Aftab, Hania

doi:10.1109/tsp.2019.2951223

Cited by 326 publications

(152 citation statements)

References 36 publications

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“…Recently, in Rehman and Aftab (2019), a new method of time-frequency decomposition for multivariate signal called multivariate variational mode decomposition (MVMD) was presented, this method as the MEMD decomposes the signal in intrinsic mode functions while keeping the mode-alignment property. The MVMD was applied to EEG signals showing robustness to noise.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2020

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Several approaches can be used to estimate neural activity. The main differences between them concern the a priori information used and its sensitivity to high noise levels. Empirical mode decomposition (EMD) has been recently applied to electroencephalography EEG-based neural activity reconstruction to provide a priori time-frequency information to improve the estimation of neural activity. EMD has the specific ability to identify independent oscillatory modes in non-stationary signals with multiple oscillatory components. However, attempts to use EMD in EEG analysis have not yet provided optimal reconstructions, due to the intrinsic mode-mixing problem of EMD. Several studies have used single-channel analysis, whereas others have used multiple-channel analysis for other applications. Here, we present the results of multiple-channel analysis using multivariate empirical mode decomposition (MEMD) to reduce the mode-mixing problem and provide useful a priori time-frequency information for the reconstruction of neuronal activity using several low-density EEG electrode montages. The methods were evaluated using real and synthetic EEG data, in which the reconstructions were performed using the multiple sparse priors (MSP) algorithm with EEG electrode montages of 32, 16, and 8 electrodes. The quality of the source reconstruction was assessed using the Wasserstein metric. A comparison of the solutions without pre-processing and those after applying MEMD showed the source reconstructions to be improved using MEMD as a priori information for the low-density montages of 8 and 16 electrodes. The mean source reconstruction error on a real EEG dataset was reduced by 59.42 and 66.04% for the 8 and 16 electrode montages, respectively, and that on a simulated EEG with three active sources, by 87.31 and 31.45% for the same electrode montages.

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Section: Discussionmentioning

confidence: 99%

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

et al. 2020

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“…Owing to the higher sampling rates and the increased number of channels in EEG, the amount of processing time and resources required for the EEG data is huge. For instance, decomposing a multi-channel EEG data with a high sampling rate using the MVMD (Rehman and Aftab, 2019 ) can be very slow, computationally very complex and requires huge amount of memory.…”

Section: Challenges Involved In Optimization Of Bci Pipelinesmentioning

confidence: 99%

Formulation of the Challenges in Brain-Computer Interfaces as Optimization Problems—A Review

Fathima¹,

Kore

2021

Front. Neurosci.

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Electroencephalogram (EEG) is one of the common modalities of monitoring the mental activities. Owing to the non-invasive availability of this system, its applicability has seen remarkable developments beyond medical use-cases. One such use case is brain-computer interfaces (BCI). Such systems require the usage of high resolution-based multi-channel EEG devices so that the data collection spans multiple locations of the brain like the occipital, frontal, temporal, and so on. This results in huge data (with high sampling rates) and with multiple EEG channels with inherent artifacts. Several challenges exist in analyzing data of this nature, for instance, selecting the optimal number of EEG channels or deciding what best features to rely on for achieving better performance. The selection of these variables is complicated and requires a lot of domain knowledge and non-invasive EEG monitoring, which is not feasible always. Hence, optimization serves to be an easy to access tool in deriving such parameters. Considerable efforts in formulating these issues as an optimization problem have been laid. As a result, various multi-objective and constrained optimization functions have been developed in BCI that has achieved reliable outcomes in device control like neuro-prosthetic arms, application control, gaming, and so on. This paper makes an attempt to study the usage of optimization techniques in formulating the issues in BCI. The outcomes, challenges, and major observations of these approaches are discussed in detail.

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“…The alternate direction method of multipliers (ADMM) is used to find the saddle point of the above equation, and the optimal solution of the objective function is obtained by updating   in an alternating fashion [31].…”

Section: A 2d-vmd Algorithmmentioning

confidence: 99%

Multi-Scale Edge Detection Method for Potential Field Data Based on Two-Dimensional Variation Mode Decomposition and Mathematical Morphology

2020

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Using observational data to determine the edges of the sources is an important task in the interpretation of potential field data. Extracting the edges of deep and shallow bodies effectively is the key to correctly understanding the underground structure. Based on the good multi-scale decomposition ability of two-dimensional variational mode decomposition (2D-VMD) and the outstanding shape analysis capability of mathematical morphology (MM), a new multi-scale edge detection method for potential field data is proposed. We propose using the variance of this morphological filter as a basis for selecting the optimal structural element (SE) scale. By establishing theoretical models and comparing the results of our method with those of traditional edge detection methods, the proposed method is shown to be effective at detecting edges within potential field data. Taking the Hanmiao area of Chifeng city, Inner Mongolia, China, as an example, 1:50000 aeromagnetic data are processed and analysed by this method. The physical properties of the rocks in the study area are also discussed. The results of theoretical calculations and real data processing show that this method can accurately extract the edges of the sources at different scales. And the real data processing results show that this method is suitable for the identification of structural faults. INDEX TERMS Multi-scale edge detection, potential field data, two-dimensional variational mode decomposition (2D-VMD), mathematical morphology (MM)

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Multivariate Variational Mode Decomposition

Cited by 326 publications

References 36 publications

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

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

Formulation of the Challenges in Brain-Computer Interfaces as Optimization Problems—A Review

Multi-Scale Edge Detection Method for Potential Field Data Based on Two-Dimensional Variation Mode Decomposition and Mathematical Morphology

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