Influence of anisotropic white matter modeling on EEG source localization

Morales, Ernesto Cuartas; Cárdenas-Peña, David; Castellanos-Domínguez, Germán

doi:10.1109/embc.2014.6944727

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…Experimental data obtained from visual stimulation suggest that anisotropic models incorporating realistic white matter anisotropic conductivity do not substantially improve the accuracy of EEG dipole localization in the primary visual cortex [11]. Forward modeling based on the white matter anisotropic assumption becomes important in reconstruction and localization of deep sources neighboring the white matter tissues [12].…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Impact of White Matter Conductivity Anisotropy on Reconstructing EEG Sources by Linearly Constrained Minimum Variance Beamformer

Samadzadehaghdam

Makkiabadi

Masjoodi

2020

ABE

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EEG source imaging aims to reconstruct the neural activities of the brain accountable for the recorded scalp potentials. This procedure requires solving two problems, namely, forward and inverse problems. For the forward problem, the head is modeled as a volume conductor and the Poisson s equation that describes the relation between neural activities and the observed EEG signals is solved. In this study, we enhanced the forward model by considering the white matter anisotropic conductivity tensor estimated from diffusion-weighted images. The second step is to solve the inverse problem in which the activity of the brain sources is estimated from measured data using the forward solution obtained in the previous step. Spatial ltering, also called beamforming, is an inverse method that reconstructs the time course of the source at a particular location by a linear combination of the sensor space data. We evaluated quantitatively the impact of the enhanced anisotropic forward model on linearly constrained minimum variance beamformer for both super cial and deep sources in a simulation environment, in terms of normalized mean squared error. Results showed that the anisotropic head forward model moderately enhanced the reconstruction of the sources, especially deep thalamic and olfactory sources. (54) Nasser SAMADZADEHAGHDAM Dr. Nasser SAMADZADEHAGHDAM received the B.S. degree in electrical engineering (bioelectric) from Sahand University of Technology, Tabriz, Iran, in 2009. He received M.Sc. and Ph.D. degrees in biomedical engineering (bioelectric) from Isfahan

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Section: Introductionmentioning

confidence: 99%

Evaluating the Impact of White Matter Conductivity Anisotropy on Reconstructing EEG Sources by Linearly Constrained Minimum Variance Beamformer

Samadzadehaghdam

Makkiabadi

Masjoodi

2020

ABE

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“…However, using experimental data obtained from visual stimulation, it seems that anisotropic models incorporating realistic white matter anisotropic conductivity do not substantially improve the accuracy of EEG dipole localization in the primary visual cortex . Forward modeling based on the white matter anisotropic assumption becomes important in reconstruction and localization of deep sources neighboring the white matter tissues . After model construction, the Poisson's differential equation derived from Maxwell's equations must be solved.…”

Section: Introductionmentioning

confidence: 99%

“…11 Forward modeling based on the white matter anisotropic assumption becomes important in reconstruction and localization of deep sources neighboring the white matter tissues. 12 After model construction, the Poisson's differential equation derived from Maxwell's equations must be solved. This equation represents the relationship between the scalp potentials and the applied current sources at any position in the volume conductor model.…”

Section: Introductionmentioning

confidence: 99%

A new linearly constrained minimum variance beamformer for reconstructing EEG sparse sources

Samadzadehaghdam

Makkiabadi

Masjoodi

et al. 2019

Int J Imaging Syst Tech

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Brain source imaging based on EEG aims to reconstruct the neural activities producing the scalp potentials. This includes solving the forward and inverse problems. The aim of the inverse problem is to estimate the activity of the brain sources based on the measured data and leadfield matrix computed in the forward step. Spatial filtering, also known as beamforming, is an inverse method that reconstructs the time course of the source at a particular location by weighting and linearly combining the sensor data. In this paper, we considered a temporal assumption related to the time course of the source, namely sparsity, in the Linearly Constrained Minimum Variance (LCMV) beamformer. This assumption sounds reasonable since not all brain sources are active all the time such as epileptic spikes and also some experimental protocols such as electrical stimulations of a peripheral nerve can be sparse in time. Developing the sparse beamformer is done by incorporating L1‐norm regularization of the beamformer output in the relevant cost function while obtaining the filter weights. We called this new beamformer SParse LCMV (SP‐LCMV). We compared the performance of the SP‐LCMV with that of LCMV for both superficial and deep sources with different amplitudes using synthetic EEG signals. Also, we compared them in localization and reconstruction of sources underlying electric median nerve stimulation. Results show that the proposed sparse beamformer can enhance reconstruction of sparse sources especially in the case of sources with high amplitude spikes.

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