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

Lookup NU author(s): Dr Kianoush Nazarpour

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Abstract

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.


Publication metadata

Author(s): Mohseni HR, Nazarpour K, Wilding EL, Sanei S

Publication type: Article

Journal: Physiological Measurement

Year: 2009

Volume: 30

Issue: 10

Pages: 1101-1116

ISSN (print): 0967-3334

ISSN (electronic): 1361-6579

Publisher: Institute of Physics Publishing Ltd.

URL: http://dx.doi.org/10.1088/0967-3334/30/10/010

DOI: 10.1088/0967-3334/30/10/010


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