A Bayesian particle filtering method for brain source localisation

Chen, Xi; Särkkä, Simo; Godsill, Simon

doi:10.1016/j.dsp.2015.06.007

Cited by 10 publications

(4 citation statements)

References 32 publications

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“…Starting from [36], Bayesian inference for dynamic dipoles has been described in several studies [40,38,25,2,8,44]. Like for the distributed case, the easiest thing to do is to use the Bayesian filtering recursion described by equations ( 26) - (27).…”

Section: Bayesian Monte Carlo Methods For Dynamic Dipolesmentioning

confidence: 99%

Inverse Modeling for MEG/EEG Data

Sorrentino

Piana

2017

Preprint

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We provide an overview of the state-of-the-art for mathematical methods that are used to reconstruct brain activity from neurophysiological data. After a brief introduction on the mathematics of the forward problem, we discuss standard and recently proposed regularization methods, as well as Monte Carlo techniques for Bayesian inference. We classify the inverse methods based on the underlying source model, and discuss advantages and disadvantages. Finally we describe an application to the pre-surgical evaluation of epileptic patients.

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Section: Bayesian Monte Carlo Methods For Dynamic Dipolesmentioning

confidence: 99%

Inverse Modeling for MEG/EEG Data

Sorrentino

Piana

2017

Preprint

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“…The BEM relies only upon the head geometry and conductivities. The volume current can be approximated using forward calculation [25][26][27].…”

Section: Modelmentioning

confidence: 99%

Neural source localization using particle filter with optimal proportional set resampling

Veeramalla

Talari

2020

ETRI Journal

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To recover the neural activity from Magnetoencephalography (MEG) and Electroencephalography (EEG) measurements, we need to solve the inverse problem by utilizing the relation between dipole sources and the data generated by dipolar sources. In this study, we propose a new approach based on the implementation of a particle filter (PF) that uses minimum sampling variance resampling methodology to track the neural dipole sources of cerebral activity. We use this approach for the EEG data and demonstrate that it can naturally estimate the sources more precisely than the traditional systematic resampling scheme in PFs.

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“…In particle filters, each probability density function (pdf) in is represented by a set of random samples (ie, particles) of that pdf instead of its actual functional form. Unlike Kalman filters, particle filters can be implemented in non‐Gaussian environments and systems with nonlinear dynamic models . Implementation of particle filters is easy.…”

Section: Introductionmentioning

confidence: 99%

Implementation of a square‐root filtering approach in marginalized particle filters for mixed linear/nonlinear state‐space models

Hesar

Mohebbi

2019

Adaptive Control & Signal

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Marginalized particle filter (MPF) takes advantage of both Kalman filter and particle filter frameworks to estimate nonlinear state-space models with reduced number of calculations in comparison to particle filter. However, due to existence of Kalman filter framework inside MPF, some limitations are introduced in implementation of MPF especially in embedded systems with finite numerical accuracies. In this paper, for the first time, we propose a novel square-root filtering strategy for MPFs to alleviate these restrictions using modified  factorization. Typical square-root Kalman filters cannot be employed inside MPF due to the presence of minus operations in some equations of MPF. However, our method can be easily implemented inside the MPF structure. The proposed method can be used in any application that employs MPFs to estimate the mixed linear/nonlinear state-space models. In order to demonstrate its usefulness, we employed the proposed square-root filtering method inside a marginalized particle extended Kalman filter (MP-EKF) structure, which was specifically designed for ECG denoising. The experimental results showed that, in the field of ECG denoising, the square-root MP-EKF performs more consistently than MP-EKF in white Gaussian noises. KEYWORDSmarginalized particle filtering, model-based filtering, square-root filtering,  factorization(1a) and (1b) are called the state dynamic and measurement models, respectively. In the above models, f and h are general nonlinear functions. x k and y k are state and measurement vectors at time step k. w k and e k are noise vectors defined by Int J Adapt Control Signal Process. 2019;33:493-511. wileyonlinelibrary.com/journal/acs

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A Bayesian particle filtering method for brain source localisation

Cited by 10 publications

References 32 publications

Inverse Modeling for MEG/EEG Data

Inverse Modeling for MEG/EEG Data

Neural source localization using particle filter with optimal proportional set resampling

Implementation of a square‐root filtering approach in marginalized particle filters for mixed linear/nonlinear state‐space models

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