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Multiple dipole source localization of EEG measurements using particle filter with partial stratified resampling

  • Santhosh Kumar VeeramallaEmail author
  • V. K. Hanumantha Rao Talari
Original Article
  • 11 Downloads

Abstract

Tracking and detection of neural activity has numerous applications in the medical research field. By considering neural sources, it can be monitored by electroencephalography (EEG). In this paper, we focus primarily on developing advanced signal processing methods for locating neural sources. Due to its high performance in state estimation and tracking, particle filter was used to locate neural sources. However, particle degeneracy limits the performance of particle filters in the most utmost situations. A few resampling methods were subsequently proposed to ease this issue. These resampling methods, however, take on heavy computational costs. In this article, we aim to investigate the Partial Stratified Resampling algorithm which is time-efficient that can be used to locate neural sources and compare them to conventional resampling algorithms. This work is aimed at reflecting on the capabilities of various resampling algorithms and estimating the performance of locating neural sources. Simulated data and real EEG data are used to conduct evaluation and comparison experiments.

Keywords

EEG Particle filter Resampling Localization Inverse problems 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Korean Society of Medical and Biological Engineering 2020

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringNational Institute of TechnologyWarangalIndia

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