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Variational Bayes for Spatiotemporal Identification of Event-Related Potential Subcomponents

Mohseni, Hamid Reza, Ghaderi, Foad, Wilding, Edward Lewis ORCID: and Sanei, Saeid 2010. Variational Bayes for Spatiotemporal Identification of Event-Related Potential Subcomponents. IEEE Transactions on Biomedical Engineering 57 (10) , pp. 2413-2428. 10.1109/TBME.2010.2050318

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We propose a novel method for detection and tracking of event-related potential (ERP) subcomponents. The ERP subcomponent sources are assumed to be electric current dipoles (ECDs), and their locations and parameters (amplitude, latency, and width) are estimated and tracked from trial to trial. Variational Bayes implies that the parameters can be estimated separately using the likelihood function of each parameter. Estimations of ECD locations, which have nonlinear relations to the measurement, are obtained by particle filtering. Estimations of the amplitude and noise covariance matrix of the measurement are optimally given by the maximum likelihood (ML) approach, while estimations of the latency and the width are obtained by the Newton-Raphson technique. New recursive methods are introduced for both the ML and Newton-Raphson approaches to prevent divergence in the filtering procedure where there is a very low SNR. The main advantage of the method is the ability to track varying ECD locations. The proposed method is assessed using simulated as well as real data, and the results emphasize the potential of this new approach for the analysis of real-time measures of neural activity.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Cardiff University Brain Research Imaging Centre (CUBRIC)
Subjects: R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
T Technology > T Technology (General)
Uncontrolled Keywords: Event-related potentials (ERPs) , Newton–Raphson technique , maximum likelihood (ML) estimation , particle filtering (PF) , variational bayes
Publisher: Institute of Electrical and Electronic Engineers
ISSN: 0018-9294
Last Modified: 20 Oct 2022 07:52

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