Fast Approximation of EEG Forward Problem and Application to Tissue Conductivity Estimation

Maksymenko, Kostiantyn; Clerc, Maureen; Papadopoulo, Théodore

doi:10.1109/tmi.2019.2936921

Cited by 6 publications

(4 citation statements)

References 23 publications

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“…where it requires to calculate the electrical potentials Φ (V ), lead field matrix L (V/m), and the current density J (A/m 2 ) located at the source position r (mm), as denoted in [28,44]. We adopted high-resolution techniques for solving EEG-FP since solutions in the forward problem highly influence localization errors in the inverse problem.…”

Section: Solving the Eeg Forward Problemmentioning

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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Section: Solving the Eeg Forward Problemmentioning

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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“…Recently, deep learning approaches have been applied for such purposes (Bore et al 2021). The EEG localization accuracy used for the conventional approach may depend on the algorithms used to solve the forward (Hallez et al 2007, Maksymenko et al 2019 and inverse (Friston et al 2008, Acharya et al 2015 problems. In addition to uncertainty and error that originate from the measurements themselves (i.e.…”

Section: Introductionmentioning

confidence: 99%

High-resolution EEG source localization in personalized segmentation-free head model with multi-dipole fitting

Hirata,

Niitsu,

Phang

et al. 2024

Phys. Med. Biol.

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Objective. Electroencephalograms (EEGs) are often used to monitor brain activity. Several source localization methods have been proposed to estimate the location of brain activity corresponding to EEG readings. However, only a few studies evaluated source localization accuracy from measured EEG using personalized head models in a millimeter resolution. In this study, based on a volume conductor analysis of a high-resolution personalized human head model constructed from magnetic resonance images, a finite difference method was used to solve the forward problem and to reconstruct the field distribution. Approach. We used a personalized segmentation-free head model developed using machine learning techniques, in which the abrupt change of electrical conductivity occurred at the tissue interface is suppressed. Using this model, a smooth field distribution was obtained to address the forward problem. Next, multi-dipole fitting was conducted using EEG measurements for each subject (N = 10 male subjects, age: 22.5 ± 0.5), and the source location and electric field distribution were estimated. Main results. For measured somatosensory evoked potential for electrostimulation to the wrist, a multi-dipole model with lead field matrix computed with the volume conductor model was superior than a single dipole model when using personalized segmentation-free models (6/10). The correlation coefficient between measured and estimated scalp potentials was 0.89 for segmentation-free head models and 0.71 for conventional segmented models. The proposed method is straightforward model development and comparable localization difference of the maximum electric field from the target wrist reported using fMR (i.e., 16.4 ± 5.2 mm) in previous study. For comparison, DUNEuro based on sLORETA was (EEG: 17.0±4.0 mm). Significance. For measured EEG signals, our procedures using personalized head models demonstrated that effective localization of the somatosensory cortex, which is located in a non-shallower cortex region. This method may be potentially applied for imaging brain activity located in other non-shallow regions.

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“…Comparatively, electrical impedance tomography (EIT) is a safe, low cost, reliable and fast method that has been successfully applied in various fields of medical imaging [7][8][9][10]. In the brain imaging, conductivity variation caused by hemorrhage can be also recovered by EIT [11][12][13]. It is known that conductivity distribution in the measured region can be reconstructed by processing voltage measurement with EIT imaging methods.…”

Section: Introductionmentioning

confidence: 99%

Classification of Hemorrhage Using Priori Information of Electrode Arrangement With Electrical Impedance Tomography

et al. 2023

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Electrical impedance tomography is an emerging technique for brain disease detection. Generally, it requires that electrodes should be equidistantly placed around the detected region. However, this may be not possible for some patients who are undergoing post-surgical monitoring. Aiming at this problem, four kinds of non-uniform electrode arrangements are developed. To accurately detect the location of intracranial hemorrhage, a novel classification method based on a priori information of electrode arrangement is also proposed in this paper. According to the electrode arrangement information, the weight which corresponds to different kinds of electrode arrangement is separately determined during the training process. The proposed method is quantitatively evaluated with basic test dataset, test datasets under noise interruption, test datasets in the case of large contact impedance, test datasets with conductivity variation in different layers, and test datasets when considering modeling error and double inclusions. Comparisons with general classification methods are also conducted. The results show that the proposed method with residual network incorporated outperforms the classification methods of fully connected neural network and residual network. For all the test datasets, the results show that the accuracy is higher than 0.9 and the specificity reaches as high as 1 when the proposed method incorporating residual network is used.

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Fast Approximation of EEG Forward Problem and Application to Tissue Conductivity Estimation

Cited by 6 publications

References 23 publications

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

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

High-resolution EEG source localization in personalized segmentation-free head model with multi-dipole fitting

Classification of Hemorrhage Using Priori Information of Electrode Arrangement With Electrical Impedance Tomography

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