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

Samadzadehaghdam, Nasser; Makkiabadi, Bahador; Masjoodi, Sadegh; Mohammadi, Mohammad Reza; Mohagheghian, Fahimeh

doi:10.1002/ima.22355

Cited by 7 publications

(3 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fusion of white matter ber orientation map (principal tensor eigenvector) on T1-weighted MRI in coronal (left), sagittal (middle) and axial (right) views. Red, green, and blue indicate mediolateral, anteroposterior, and superioinferior directions, respectively[23].…”

mentioning

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

Self Cite

View full text Add to dashboard Cite

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

show abstract

mentioning

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…The majority of works that blend multimodal information from dMRI and EEG focus on source localisation techniques [20][21][22], using a digitiser to map electrode coordinates to the scalp which can be time-consuming. Others produced an automated, individualised localisation tool to map electrodes from high density EEG (HDEEG) to the scalp only, without extending the mapping to the cortex [23].…”

Section: Introductionmentioning

confidence: 99%

Structure-Function Coupling Reveals Seizure Onset Connectivity Patterns

et al. 2022

View full text Add to dashboard Cite

The implications of combining structural and functional connectivity to quantify the most active brain regions in seizure onset remain unclear. This study tested a new model that may facilitate the incorporation of diffusion MRI (dMRI) in clinical practice. We obtained structural connectomes from dMRI and functional connectomes from electroencephalography (EEG) to assess whether high structure-function coupling corresponded with the seizure onset region. We mapped individual electrodes to their nearest cortical region to allow for a one-to-one comparison between the structural and functional connectomes. A seizure laterality score and expected onset zone were defined. The patients with well-lateralised seizures revealed high structure-function coupling consistent with the seizure onset zone. However, a lower seizure lateralisation score translated to reduced alignment between the high structure-function coupling regions and the seizure onset zone. We illustrate that dMRI, in combination with EEG, can improve the identification of the seizure onset zone. Our model may be valuable in enhancing ultra-long-term monitoring by indicating optimal, individualised electrode placement.

show abstract

“…The majority of works that blend multimodal information from dMRI and EEG focus on source localisation techniques [20, 21, 22], using a digitiser to map electrodes coordinates to the scalp which can be time-consuming. Others produced an automated, individualised localisation tool to map electrodes from high density EEG (HDEEG) to the scalp only, without extending the mapping to the cortex [23].…”

Section: Introductionmentioning

confidence: 99%

Structure-function coupling reveals seizure onset connectivity patterns

Maher

D’Souza

Barnett

et al. 2022

Preprint

View full text Add to dashboard Cite

The implications of combining structural and functional connectivity to quantify the most active brain regions in seizure onset remain unclear. We obtained structural connectomes from diffusion MRI (dMRI) and functional connectomes from electroencephalography (EEG) to assess whether high structure-function coupling corresponded with the seizure onset region. We mapped individual electrodes to their nearest cortical region to allow for a one-to-one comparison between the structural and functional connectomes. A seizure laterality score and expected onset zone were defined. The patients with well-lateralised seizures revealed high structure-function coupling consistent with the seizure onset zone. However, a lower seizure lateralisation score translated to reduced alignment between the high structure-function coupling regions and the seizure onset zone. This feasibility study tested a new model for incorporating dMRI in clinical practice. We illustrate that dMRI, in combination with EEG, can improve the identification of the seizure onset zone. Our model may be valuable in enhancing ultra-long-term monitoring by indicating optimal, individualised electrode placement.

show abstract

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

Cited by 7 publications

References 34 publications

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

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

Structure-Function Coupling Reveals Seizure Onset Connectivity Patterns

Structure-function coupling reveals seizure onset connectivity patterns

Contact Info

Product

Resources

About