Recursive sLORETA-FOCUSS Algorithm for EEG Dipoles Localization

Rafik, Kurundwad Musaddik; Ahmed, Ben Hamida; Imed, F.; Abdelmalik, Taleb-Ahmed

doi:10.1109/ipta.2008.4743745

Cited by 4 publications

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These neural sources can either be localized or mapped by electroencephalography (EEG). Magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI), near-infrared spectroscopy (NIRS) and electrocorticography (ECoG) techniques [4][5]. In EEG, the neural responses generate event-related potentials (ERPs) on the scalp surface, measured by EEG multi-channel system composed of electrodes, amplifiers, digitizers and filters [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

MSP Patches Based Optimized EEG Source Localization and Validation in Visual Cortex of Human Brain

Gaho¹,

Khan²,

Musavi³

et al. 2022

PakJET

View full text Add to dashboard Cite

This paper is focused on optimizing EEG source localization of visual neural activities generated in the posterior lobe of the human brain. The visual and neural systems of the human brain process the captured images of faces or scenes into optical and chemical neurons in order to produce electrical potentials over the scalp surface, where EEG electrodes measure these signals to sense the underneath visual brain activity. However, it is categorically hard to localize the true neural sources in the human brain's visual cortex due to overlapping and interaction of other active areas of the lobes of the brain. Thus, a novel algorithm of MSP inversion-based Bayesian framework with varying patches for providing the optimal free energy and minimum location in the visual cortex is proposed to address this issue. This algorithm is integrated with a synthetic EEG dataset generation scheme to validate active neural sources. This proposed algorithm provides satisfactory results in terms of optimal free energy with minimum localization error and validates true active sources in the brain. The SPM12 Toolbox is applied in processing the visual EEG dataset in this research. The application of this proposed algorithm is beneficial in terms of localizing the optimum visual sources and identifying the visual disorders or diseases in the human brain.

show abstract

Section: Introductionmentioning

confidence: 99%

MSP Patches Based Optimized EEG Source Localization and Validation in Visual Cortex of Human Brain

Gaho¹,

Khan²,

Musavi³

et al. 2022

PakJET

View full text Add to dashboard Cite

show abstract

Array-Gain Constraint Minimum-Norm Spatial Filter With Recursively Updated Gram Matrix For Biomagnetic Source Imaging

Kumihashi

Sekihara

2010

IEEE Trans. Biomed. Eng.

View full text Add to dashboard Cite

This paper proposes a novel spatial filter for biomagnetic source imaging. The proposed spatial filter is derived based on a modified version of the minimum-norm spatial filter and is designed to have a performance close to that of the adaptive minimum-variance spatial filter through the use of an estimated covariance matrix. In this method, the theoretical form of the measurement covariance matrix is estimated as an updated gram matrix in a recursive procedure. Since the proposed method does not use the sample covariance matrix, it is free of the well-known weaknesses of the minimum-variance spatial filter, namely, the proposed spatial filter does not require a large number of time samples, and it can even be applied to single-time-sample data. It is also robust to source correlation. We have validated the method's effectiveness by our computer simulations as well as through experiments using auditory-evoked magnetoencephalographic data.

show abstract

Minimum Variance Brain Source Localization for Short Data Sequences

Ravan

Reilly

Hasey

2014

IEEE Trans. Biomed. Eng.

View full text Add to dashboard Cite

In the electroencephalogram (EEG) or magnetoencephalogram (MEG) context, brain source localization methods that rely on estimating second-order statistics often fail when the number of samples of the recorded data sequences is small in comparison to the number of electrodes. This condition is particularly relevant when measuring evoked potentials. Due to the correlated background EEG/MEG signal, an adaptive approach to localization is desirable. Previous work has addressed these issues by reducing the adaptive degrees of freedom (DoFs). This reduction results in decreased resolution and accuracy of the estimated source configuration. This paper develops and tests a new multistage adaptive processing technique based on the minimum variance beamformer for brain source localization that has been previously used in the radar statistical signal processing context. This processing, referred to as the fast fully adaptive (FFA) approach, can significantly reduce the required sample support, while still preserving all available DoFs. To demonstrate the performance of the FFA approach in the limited data scenario, simulation and experimental results are compared with two previous beamforming approaches; i.e., the fully adaptive minimum variance beamforming method and the beamspace beamforming method. Both simulation and experimental results demonstrate that the FFA method can localize all types of brain activity more accurately than the other approaches with limited data.

show abstract

Recursive sLORETA-FOCUSS Algorithm for EEG Dipoles Localization

Cited by 4 publications

References 7 publications

MSP Patches Based Optimized EEG Source Localization and Validation in Visual Cortex of Human Brain

MSP Patches Based Optimized EEG Source Localization and Validation in Visual Cortex of Human Brain

Array-Gain Constraint Minimum-Norm Spatial Filter With Recursively Updated Gram Matrix For Biomagnetic Source Imaging

Minimum Variance Brain Source Localization for Short Data Sequences

Contact Info

Product

Resources

About