Electroencephalography source localization analysis in epileptic children during a visual working‐memory task

Galaris, Evangelos; Gallos, Ioannis; Myatchin, Ivan; Lagae, Lieven; Siettos, Constantinos

doi:10.1002/cnm.3404

Cited by 8 publications

(3 citation statements)

References 80 publications

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“…ESI has also been employed in pediatric epilepsy. In a recent study [61], using a visual working-memory task as a stimulus and a source localization framework, critical biomarkers (that could not be detected by sensor-level analysis alone) were identified, leading to a more localized and specified treatment policy.…”

Section: Clinical Applicationsmentioning

confidence: 99%

Advances in Electrical Source Imaging: A Review of the Current Approaches, Applications and Challenges

Zorzos

Kakkos

Ventouras

et al. 2021

Signals

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Brain source localization has been consistently implemented over the recent years to elucidate complex brain operations, pairing the high temporal resolution of the EEG with the high spatial estimation of the estimated sources. This review paper aims to present the basic principles of Electrical source imaging (ESI) in the context of the recent progress for solving the forward and the inverse problems, and highlight the advantages and limitations of the different approaches. As such, a synthesis of the current state-of-the-art methodological aspects is provided, offering a complete overview of the present advances with regard to the ESI solutions. Moreover, the new dimensions for the analysis of the brain processes are indicated in terms of clinical and cognitive ESI applications, while the prevailing challenges and limitations are thoroughly discussed, providing insights for future approaches that could help to alleviate methodological and technical shortcomings.

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Section: Clinical Applicationsmentioning

confidence: 99%

Advances in Electrical Source Imaging: A Review of the Current Approaches, Applications and Challenges

Zorzos

Kakkos

Ventouras

et al. 2021

Signals

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“…Its primary objective is to estimate the location of the electrical sources responsible for the electrical activity inside the brain. The ESL has been used to develop a better understanding of the human brain functionality, especially for patients suffering from epilepsy, sleep disorders, and other brain diseases [19].…”

Section: Introductionmentioning

confidence: 99%

Neural stochastic differential equations network as uncertainty quantification method for EEG source localization

Wabina

Silpasuwanchai

2023

Biomed. Phys. Eng. Express

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EEG source localization remains a challenging problem given the uncertain conductivity values of the volume conductor models (VCMs). As uncertain conductivities vary across people, they may considerably impact the forward and inverse solutions of the EEG, leading to an increase in localization mistakes and misdiagnoses of brain disorders. Calibration of conductivity values using uncertainty quantification (UQ) techniques is a promising approach to reduce localization errors. The widely-known UQ methods involve Bayesian approaches, which utilize prior conductivity values to derive their posterior inference and estimate their optimal calibration. However, these approaches have two significant drawbacks: solving for posterior inference is intractable, and choosing inappropriate priors may lead to increased localization mistakes. This study used the Neural Stochastic Differential Equations Network (SDE-Net), a combination of dynamical systems and deep learning techniques that utilizes the Wiener process to minimize conductivity uncertainties in the VCM and improve the inverse problem. Results revealed that SDE-Net generated a lower localization error rate in the inverse problem compared to Bayesian techniques. Future studies may employ new stochastic dynamical systems-based techniques as a UQ technique to address further uncertainties in the EEG Source Localization problem. Our code can be found here: https://github.com/rrwabina/SDE-Net-UQ-ESL.

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“…The use of an accurate and efficient algorithm for Brain Source Localization (BSL) using Electroencephalography (EEG) measurements has been used in various neuroscience applications such as pre-surgical mapping in patients undergoing resection of epileptogenic zones [1][2][3][4] , Brain Computer Interface (BCI) based prosthetic limbs 5,6 , and attention deficit hyperactive disorder 7,8 . Among these diseases, epilepsy is the most important and common neurological disorder as 1% of the world population is suffering from it.…”

Section: Introductionmentioning

confidence: 99%

Anatomical Harmonics Basis based Brain Source Localization with Application to Epilepsy

Giri

Kumar

Kurwale

et al. 2021

Preprint

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Brain Source Localization (BSL) using Electroencephalogram (EEG) has been a useful noninvasive modality for the diagnosis of epileptogenic zones, study of evoked related potentials, and brain disorders. The inverse solution of BSL is limited by high computational cost and localization error. The performance is additionally limited by head shape assumption and the corresponding harmonics basis function. In this work, an anatomical harmonics basis (Spherical Harmonics (SH), and more particularly Head Harmonics (H2)) based BSL is presented. The spatio-temporal four shell head model is formulated in SH domain. The performance of spatial subspace based Multiple Signal Classification (MUSIC) and Recursively Applied and Projected (RAP)-MUSIC method is compared with the proposed SH and H2 counterparts on simulated data. SH and H2 domain processing effectively resolves the problem of high computational cost without sacrificing the inverse source localization accuracy. The proposed H2 MUSIC was additionally validated for epileptogenic zone localization on clinical EEG data. The proposed framework offers an effective solution to clinicians in automated and time efficient seizure localization.

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Electroencephalography source localization analysis in epileptic children during a visual working‐memory task

Cited by 8 publications

References 80 publications

Advances in Electrical Source Imaging: A Review of the Current Approaches, Applications and Challenges

Advances in Electrical Source Imaging: A Review of the Current Approaches, Applications and Challenges

Neural stochastic differential equations network as uncertainty quantification method for EEG source localization

Anatomical Harmonics Basis based Brain Source Localization with Application to Epilepsy

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