Neural source localization using particle filter with optimal proportional set resampling

Neural source localization using particle filter with optimal proportional set resampling

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 imple...

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Bibliographic Details
Main Authors: Santhosh Kumar Veeramalla, V.K. Hanumantha Rao Talari
Format: Article
Language:English
Published: Electronics and Telecommunications Research Institute (ETRI) 2020-02-01
Series:ETRI Journal
Subjects:
eeg
particle filter
resampling
source localization
systematic resampling
Online Access:https://doi.org/10.4218/etrij.2019-0020
Description
Summary: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.
ISSN:1225-6463

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