Fused multivariate empirical mode decomposition (MEMD) and inverse solution method for EEG source localization

Khosropanah, Pegah; Ramli, Abdul Rahman; Lim, Kheng Seang; Marhaban, Mohammad Hamiruce; Ahmedov, Anvarjon

doi:10.1515/bmt-2017-0011

Cited by 15 publications

(8 citation statements)

References 32 publications

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“…They extracted and selected suitable feature sets to classify neural activity based on a multiscale time-frequency representation of the EEG signals by applying MEMD. In Khosropanah et al (2018), a fusion between MEMD, source reconstruction algorithms, and an unsupervised wavelet eye blink artifact remover were introduced. The fusion of those methods was applied for the accurate localization of epileptogenic sources in five subjects, the results of which suggest than MEMD can improve source localization when the standardized lowresolution tomography (sLORETA) inverse method is applied.…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…Otherwise, SISCOM has the highest specificity providing reliability by false positives. The objective of this work is not to evaluate the veracity of the individual techniques used (ESI or SISCOM) to identify EZ in drug-resistant epilepsy, which has been the goal of some publications [15,16,17,18]. The disadvantages of each individual technique are balanced by establishing a methodology that uses and combines different functional modalities.…”

Section: Discussionmentioning

confidence: 99%

A Novel Noninvasive Approach Based on SPECT and EEG for the Location of the Epileptogenic Zone in Pharmacoresistant Non-Lesional Epilepsy

et al. 2019

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Background and objectives: The aim of this study is to propose a methodology that combines non-invasive functional modalities electroencephalography (EEG) and single photon emission computed tomography (SPECT) to estimate the location of the epileptogenic zone (EZ) for the presurgical evaluation of patients with drug-resistant non-lesional epilepsy. Materials and Methods: This methodology consists of: (i) Estimation of ictal EEG source imaging (ESI); (ii) application of the subtraction of ictal and interictal SPECT co-registered with MRI (SISCOM) methodology; and (iii) estimation of ESI but using the output of the SISCOM as a priori information for the estimation of the sources. The methodology was implemented in a case series as an example of the application of this novel approach for the presurgical evaluation. A gold standard and a coincidence analysis based on measures of sensitivity and specificity were used as a preliminary assessment of the proposed methodology to localize EZ. Results: In patients with good postoperative evolution, the estimated EZ presented a spatial coincidence with the resection site represented by high values of sensitivity and specificity. For the patient with poor postoperative evolution, the methodology showed a partial incoherence between the estimated EZ and the resection site. In cases of multifocal epilepsy, the method proposed spatially extensive epileptogenic zones. Conclusions: The results of the case series provide preliminary evidence of the methodology’s potential to epileptogenic zone localization in non-lesion drug-resistant epilepsy. The novelty of the article consists in estimating the sources of ictal EEG using SISCOM result as a prior for the inverse solution. Future studies are necessary in order to validate the described methodology. The results constitute a starting point for further studies in order to support the clinical reliability of the proposed methodology and advocate for their implementation in the presurgical evaluation of patients with intractable non-lesional epilepsy.

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“…The realizations are termed as intrinsic mode functions (IMFs). EEG signal mode decomposition becomes important to reconstruct or separate out various neuronal activities (Soler et al, 2020), source localization (Khosropanah et al, 2018), artifact removal (Wang et al, 2015), detection of seizures (Bajaj and Pachori, 2011), and so on.…”

Section: Eeg Mode Decomposition and Optimizationmentioning

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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Fused multivariate empirical mode decomposition (MEMD) and inverse solution method for EEG source localization

Cited by 15 publications

References 32 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

A Novel Noninvasive Approach Based on SPECT and EEG for the Location of the Epileptogenic Zone in Pharmacoresistant Non-Lesional Epilepsy

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

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