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

Frontiers in Applied Mathematics and Statistics

Garcia-Ojalvo, Jordi¹  Escuain-Poole, Lara²  Pons, Antonio J.² 

[1] Department of Experimental and Health Sciences, Pompeu Fabra University, Spain;Physics Department, Polytechnic University of Catalonia, Spain

关键词: Unscented Kalman filter;    data assimilation;    EEG;    neural mass model;    parameter estimation;   

DOI  :  10.3389/fams.2018.00046

学科分类：数学（综合）

来源: Frontiers

【 摘 要 】

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 pa rameters of the neural masses and their interactions, 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 extracranial, is shown in different dynamical behaviours. Our results show potential towards future clinical applications of the method.

【 授权许可】

CC BY