Source space reduction for eLORETA

Faes, Axel; Borman, Aurélie de; Hulle, Marc M. Van

doi:10.1088/1741-2552/ac2bb6

Cited by 4 publications

(4 citation statements)

References 30 publications

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“…One possibility is to use LSTM-NUE as part of a masking approach, on top of which another learner is stacked. This masking approach has already led to many advantages in source localization [25], and it may also facilitate connectivity detection with ANNs, especially when overly sensitive to it. In this sense, other ANNs, even with a lower Time Complexity than that of LSTM-NUE, could possibly also be considered as potentially directed connectivity estimators.…”

Section: Discussionmentioning

confidence: 99%

“…Afterwards, the simulated scalp-EEG data are source-reconstructed using exact lowresolution brain electromagnetic tomography (eLORETA) [24]. There have also been improvements to eLORETA, such as Sparse eLORETA, which uses a masking approach to improve the source localization density [25]. The eLORETA method is a discrete, threedimensional (3D), linear, weighted minimum norm inverse solution [24].…”

Section: Simulation Proceduresmentioning

confidence: 99%

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Neural Networks for Directed Connectivity Estimation in Source-Reconstructed EEG Data

2022

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Directed connectivity between brain sources identified from scalp electroencephalography (EEG) can shed light on the brain’s information flows and provide a biomarker of neurological disorders. However, as volume conductance results in scalp activity being a mix of activities originating from multiple sources, the correct interpretation of their connectivity is a formidable challenge despite source localization being applied with some success. Traditional connectivity approaches rely on statistical assumptions that usually do not hold for EEG, calling for a model-free approach. We investigated several types of Artificial Neural Networks in estimating Directed Connectivity between Reconstructed EEG Sources and assessed their accuracy with respect to several ground truths. We show that a Long Short-Term Memory neural network with Non-Uniform Embedding yields the most promising results due to its relative robustness to differing dipole locations. We conclude that certain network architectures can compete with the already established methods for brain connectivity analysis.

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Section: Discussionmentioning

confidence: 99%

Section: Simulation Proceduresmentioning

confidence: 99%

Neural Networks for Directed Connectivity Estimation in Source-Reconstructed EEG Data

2022

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“…Unfortunately, the numerical solution of the EEG inverse problem can be time and memory consuming, which makes it unattractive in real-time applications, especially when portable devices are employed. To reduce the computational cost, dimensionality reduction techniques can be used to extract the most significant features from the data or to select the brain regions of interest before applying an inversion method [3][4][5]. In addition, dimensionality reduction can act as a first regularization step, since it reduces the degrees of freedom of the inverse problem.…”

Section: Introductionmentioning

confidence: 99%

Solution of the EEG inverse problem by random dipole sampling

Della Cioppa,

Tartaglione,

Pascarella

et al. 2023

Inverse Problems

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Electroencephalography (EEG) source imaging aims to reconstruct brain activity maps from the neuroelectric potential difference measured on the skull. To obtain the brain activity map, we need to solve an ill-posed and ill-conditioned inverse problem that requires regularization techniques to make the solution viable. When dealing with real-time applications, dimensionality reduction techniques can be used to reduce the computational load required to evaluate the numerical solution of the EEG inverse problem. To this end, in this paper we use the random dipole sampling method, in which a Monte Carlo technique is used to reduce the number of neural sources. This is equivalent to reducing the number of the unknowns in the inverse problem and can be seen as a first regularization step. Then, we solve the reduced EEG inverse problem with two popular inversion methods, the weighted Minimum Norm Estimate (wMNE) and the standardized LOw Resolution brain Electromagnetic TomogrAphy (sLORETA). The main result of this paper is the error estimates of the reconstructed activity map obtained with the randomized version of wMNE and sLORETA. Numerical experiments on synthetic EEG data demonstrate the effectiveness of the random dipole sampling method.

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“…Numerous methods have been highlighted and discoursed for EEG brain source localization [19]. A new technique e sparse eLORETA for approximating a nonparametric way out to the source localization problem [20]. Spatial precision for EEG source localization measured by Local Autoregressive Average (LAURA), LORETA sLORETA, eLORETA was used [21].…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Low-Resolution Electroencephalogram Source Localization Techniques

Iqbal¹

2022

SSURJET

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the brain source localization has attained the significant fascination over the last few decades. Source localization in the human brain is a prospective complication that is derived in the multifaceted real-world complications because of the brain’s practical and biological density, other than medical precincts of assembling EEG from enormously various subjects. It is validated that the electromagnetic signal recorded on the top of scalp is owing to the collective actions of a neurons inside the brain. Any impulsive action of the brain, sensory stimulus, cognitive action, or the generation of motor yield possibly will offer intensification to such neuronal actions. Source- localization in human brain implicates the localization and detection of such primary neuronal originators into the brain. Although the renowned and different research in the area, the complications remnants to be mysterious inverse problem in the brain signal processing research. This paper highlighted the performance analysis the EEG source localization techniques based on standardized low resolution brain electromagnetic tomography (sLORETA) and exact low resolution brain electromagnetic tomography (eLORETA). The event related potential (ERP) records with chromatic stimulus are considered for analysis at diverse time intervals for both techniques and finally results are discussed in reports of scalp map, slice view and cortex map and proposed the optimum techniques for EEG source localization.

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Source space reduction for eLORETA

Cited by 4 publications

References 30 publications

Neural Networks for Directed Connectivity Estimation in Source-Reconstructed EEG Data

Neural Networks for Directed Connectivity Estimation in Source-Reconstructed EEG Data

Solution of the EEG inverse problem by random dipole sampling

Performance Analysis of Low-Resolution Electroencephalogram Source Localization Techniques

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