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Entropy 2017, 19(5), 199; doi:10.3390/e19050199

Cooperative Particle Filtering for Tracking ERP Subcomponents from Multichannel EEG

1
NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore 119077, Singapore
2
Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford OX3 7DQ, UK
3
Department of Electrical and Computer Engineering, National University of Singapore, Singapore 119077, Singapore
4
Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, UK
*
Author to whom correspondence should be addressed.
Academic Editors: Osvaldo Anibal Rosso and Kevin H. Knuth
Received: 12 January 2017 / Revised: 12 April 2017 / Accepted: 23 April 2017 / Published: 29 April 2017
(This article belongs to the Special Issue Entropy and Electroencephalography II)
View Full-Text   |   Download PDF [5512 KB, uploaded 29 April 2017]   |  

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. View Full-Text
Keywords: event related potential (ERP); P300; Rao-Blackwellised particle filter (RBPF); cooperative particle filtering; coupled RBPF event related potential (ERP); P300; Rao-Blackwellised particle filter (RBPF); cooperative particle filtering; coupled RBPF
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Monajemi, S.; Jarchi, D.; Ong, S.-H.; Sanei, S. Cooperative Particle Filtering for Tracking ERP Subcomponents from Multichannel EEG. Entropy 2017, 19, 199.

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