2017/08/01 by Lara Escuain-Poole, Escuain-Poole, Lara, Jordi García‐Ojalvo +3 · 1 citation
Neuroscience · #EEG and Brain-Computer Interfaces #FOS: Biological sciences #Functional Brain Connectivity Studies #Neural dynamics and brain function #Neurons and Cognition (q-bio.NC)
paper · pdf · doi:10.48550/arxiv.1708.05282
openalex publication_date 2017/08/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Data assimilation, defined as the fusion of data with preexisting knowledge,\nis particularly suited to elucidating underlying phenomena from\nnoisy/insufficient observations. Although this approach has been widely used in\ndiverse fields, only recently have efforts been directed to problems in\nneuroscience, using mainly intracranial data and thus limiting its\napplicability to invasive measurements involving electrode implants. Here we\nintend to apply data assimilation to non-invasive electroencephalography (EEG)\nmeasurements to infer brain states and their characteristics. For this purpose,\nwe use Kalman filtering to combine synthetic EEG data with a coupled\nneural-mass model together with Ary's model of the head, which projects\nintracranial signals onto the scalp. Our results show that using several\nextracranial electrodes allows to successfully estimate the state and\nparameters of the neural masses and their interactions, whereas one single\nelectrode provides only a very partial and insufficient view of the system. The\nsuperiority of using multiple extracranial electrodes over using only one, be\nit intra- or extracranial, is shown over a wide variety of dynamical\nbehaviours. Our results show potential towards future clinical applications of\nthe method.\n