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Mapping neural activity from EEG data using a Kalman approach |
| Project members: |
E. Giraldo (Technological University of Pereira, Colombia), prof. dr. G. Castellanos-Dominguez (National University of Colombia), A.J. den Dekker
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| Keywords: |
Statistical signal processing, Imaging and adaptive optics, Optimal filtering, EEG
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| Functional neuroimaging aims to non-invasively characterize the dynamics of the distributed neural networks that mediate brain function in healthy and pathological states. Well-known and widely used functional neuroimaging techniques are functional magnetic resonance imaging (fMRI) and
electroencephalography (EEG) source reconstruction. EEG source reconstruction is a technique that reconstructs the sources of electrical currents (i.e. the current distribution)
within the brain that give rise to recordable potential fields at the scalp. fMRI records hemodynamic activity (changes in blood flow), which is an indirect marker of the brain’s
electrical activity. Both techniques map neuronal activity and are complementary in the sense that fMRI is known to provide a high spatial resolution but a relatively low temporal resolution, whereas EEG source reconstruction, which is also known as electroencephalographic source
localization (ESL), allows a high temporal resolution, but a relatively low spatial resolution.
This project focuses on EEG source reconstruction, which is known to be an ill-posed problem (as there are an infinite number of different current sources that give rise to identical
scalp recordings) that cannot be solved without some kind of regularization. Until recently, most attempts to solve the inverse problem were based on scalp recordings at one
single time pint. However, neural activity has inherent strong spatial and temporal dynamics that may be taken into account when solving the inverse problem. In this project this goal is pursued by developing EEG source reconstruction methods that solve the inverse problem in a Kalman filter framework using linear and nonlinear physiology based dynamic models with time varying parameters
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