Abstract
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.
Highlights
After several decades studying its morphology and dynamics [1], the basic mechanisms that describe the functioning of the brain are still far from being completely understood
For the purpose of providing a proof-of-concept of our proposed data assimilation approach, we use in silico data, instead of real experimental observations, generated by Jansen and Rit’s model [26, 51], as a way to represent the dynamical evolution of the cortical structures
In this paper we focus on estimating the amplitudes A of the EPSPs of the different cortical columns, and we choose values for these amplitudes that produce signals that reflect various dynamic regimes that we
Summary
After several decades studying its morphology and dynamics [1], the basic mechanisms that describe the functioning of the brain are still far from being completely understood. The fact that the brain receives continuous external inputs from the sensory system makes its dynamical and experimental interpretation more complex because, even though experiments are designed to minimise uncontrolled inputs, they cannot completely rule them out. Another important limitation for studying the brain is that experimental recordings (such as EEG or fRMI) are almost always indirect reflections of the underlying neural activity [13]
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