The application of particle filters in single trial event-related potential estimation

  1. Lookup NU author(s)
  2. Dr Kianoush Nazarpour
Author(s)Mohseni HR, Nazarpour K, Wilding EL, Sanei S
Publication type Article
JournalPhysiological Measurement
Year2009
Volume30
Issue10
Pages1101-1116
ISSN (print)0967-3334
ISSN (electronic)1361-6579
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
In this paper, an approach for the estimation of single trial event-related potentials (ST-ERPs) using particle filters (PFs) is presented. The method is based on recursive Bayesian mean square estimation of ERP wavelet coefficients using their previous estimates as prior information. To enable a performance evaluation of the approach in the Gaussian and non-Gaussian distributed noise conditions, we added Gaussian white noise (GWN) and real electroencephalogram (EEG) signals recorded during rest to the simulated ERPs. The results were compared to that of the Kalman filtering (KF) approach demonstrating the robustness of the PF over the KF to the added GWN noise. The proposed method also outperforms the KF when the assumption about the Gaussianity of the noise is violated. We also applied this technique to real EEG potentials recorded in an odd-ball paradigm and investigated the correlation between the amplitude and the latency of the estimated ERP components. Unlike the KF method, for the PF there was a statistically significant negative correlation between amplitude and latency of the estimated ERPs, matching previous neurophysiological findings.
PublisherInstitute of Physics Publishing Ltd.
URLhttp://dx.doi.org/10.1088/0967-3334/30/10/010
DOI10.1088/0967-3334/30/10/010
Actions    Link to this publication