Title: Resampling schemes within a particle filter framework for brain source localisation

Authors: Santhosh Kumar Veeramalla; T.V.K. Hanumantha Rao

Addresses: Department of Electronics and Communication Engineering, National Institute of Technology, Warangal, Telangana, India ' Department of Electronics and Communication Engineering, National Institute of Technology, Warangal, Telangana, India

Abstract: One of the critical aspects of neuroscience research is locating neural sources from EEG data. Due to its superior performance in tracking and prediction, the particle filter was used to locate sources. To alleviate particle degeneracy of the particle filter, some improvements have been suggested by developing resampling techniques for EEG applications. In this paper, we proposed a new approach for localisation of the neural source of the real EEG data based on residual and residual systematic resampling methods in the particle filters. We show that with the proposed residual systematic resampling algorithm, the proposed filter improves the RMSE estimation performance, improves the exact position of the source, and reduces time to run. The suggested approach for the source localisation, by taking into account the efficiency measures, provides better performance than the other methods of resampling used in particle filter for source localisation.

Keywords: particle filter; resampling; electroencephalography; EEG; state estimation; source localisation; inverse problem.

DOI: 10.1504/IJBET.2022.125100

International Journal of Biomedical Engineering and Technology, 2022 Vol.40 No.1, pp.18 - 38

Received: 24 May 2019
Accepted: 31 Oct 2019
Published online: 30 Aug 2022 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article