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

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.

Sample Regenerating Particle Filter Combined With Unequal Weight Ensemble Kalman Filter for Nonlinear Systems

“…Since in reality, many problems are nonlinear, and their analytical solutions are rarely available, so particle filter is developed to deal with these problems [1], [2], [3]. And it was already used in many areas, like air traffic control [4], [5], meteorology [6], [7], aerospace [8], [9], oceanography [10], autonomous vehicles [11] and robotics [12], remote sensing [13], computer vision [14], [15] and biomedical research [16].…”