Extracranial Estimation of Neural Mass Model Parameters Using the Unscented Kalman Filter

Data assimilation, defined as the fusion of data with preexisting knowledge, is particularly suited to elucidating underlying phenomena from noisy/insufficient observations. Although this approach has been widely used in diverse fields, only recently have efforts been directed to problems in neurosc...

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Bibliographic Details
Authors: Escuain i Poole, Lara Sofia, García Ojalvo, Jordi|||0000-0002-3716-7520, Pons Rivero, Antonio Javier|||0000-0002-1481-8159
Format: article
Publication Date:2018
Country:España
Institution:Universitat Politècnica de Catalunya (UPC)
Repository:UPCommons. Portal del coneixement obert de la UPC
Language:English
OAI Identifier:oai:upcommons.upc.edu:2117/122761
Online Access:https://hdl.handle.net/2117/122761
https://dx.doi.org/10.3389/fams.2018.00046
Access Level:Open access
Keyword:Kalman filtering
Neurosciences
Signal processing
Unscented Kalman filter
Data assimilation
EEG
Neural mass model
Parameter estimation
Neurociències
Tractament del senyal
Kalman, Filtratge de
Àrees temàtiques de la UPC::Física
Description
Summary:Data assimilation, defined as the fusion of data with preexisting knowledge, is particularly suited to elucidating underlying phenomena from noisy/insufficient observations. Although this approach has been widely used in diverse fields, only recently have efforts been directed to problems in neuroscience, using mainly intracranial data and thus limiting its applicability to invasive measurements involving electrode implants. Here we intend to apply data assimilation to non-invasive electroencephalography (EEG) measurements to infer brain states and their characteristics. For this purpose, we use Kalman filtering to combine synthetic EEG data with a coupled neural-mass model together with Ary’s model of the head, which projects intracranial signals onto the scalp. Our results show that using several extracranial electrodes allows to successfully estimate the state and a specific parameter of the model, whereas one single electrode provides only a very partial and insufficient view of the system. The superiority of using multiple extracranial electrodes over using only one, be it intra- or extra-cranial, is shown in different dynamical behaviours. Our results show potential toward future clinical applications of the method.