Cooperative particle filtering for tracking ERP subcomponents from multichannel EEG

Monajemi, S, Jarchi, D, Ong, S-H and Sanei, S ORCID: https://orcid.org/0000-0002-3437-2801, 2017. Cooperative particle filtering for tracking ERP subcomponents from multichannel EEG. Entropy, 19 (5), p. 199. ISSN 1099-4300

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Abstract

In this study, we propose a novel method to investigate P300 variability over different trials. The method incorporates spatial correlation between EEG channels to form a cooperative coupled particle filtering method that tracks the P300 subcomponents, P3a and P3b, over trials. Using state space systems, the amplitude, latency, and width of each subcomponent are modeled as the main underlying parameters. With four electrodes, two coupled Rao-Blackwellised particle filter pairs are used to recursively estimate the system state over trials. A number of physiological constraints are also imposed to avoid generating invalid particles in the estimation process. Motivated by the bilateral symmetry of ERPs over the brain, the channels further share their estimates with their neighbors and combine the received information to obtain a more accurate and robust solution. The proposed algorithm is capable of estimating the P300 subcomponents in single trials and outperforms its non-cooperative counterpart.

Item Type:	Journal article
Publication Title:	Entropy
Creators:	Monajemi, S., Jarchi, D., Ong, S.-H. and Sanei, S.
Publisher:	MDPI AG
Date:	2017
Volume:	19
Number:	5
ISSN:	1099-4300
Identifiers:	Number Type 10.3390/e19050199 DOI e19050199 Publisher Item Identifier
Divisions:	Schools > School of Science and Technology
Record created by:	Linda Sullivan
Date Added:	31 Jan 2018 11:55
Last Modified:	31 Jan 2018 11:55
URI:	https://irep.ntu.ac.uk/id/eprint/32590

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