Congratulations to Alexandra Kuznetsova on successfully defending her PhD thesis!

November 18, 2022 at the dissertation council of the Faculty of Computer Science defended the thesis of Alexandra Kuznetsova on "Regularization of the solution of the inverse problem of EEG and MEG based on physiologically conditioned models of the dynamics of neuronal activity. Supervisor of the work Osadtchi A.E.

Decision on awarding the degree of Candidate of Sciences was made unanimously!

The use of noninvasive neuroimaging techniques, such as electroencephalography (EEG) and magnetoencephalography (MEG), allows for effective cognitive studies and diagnosis of a wide range of neurological disorders without exposing the patient to additional risk. For a more accurate diagnosis, it is necessary to use EEG and MEG inverse problem solving methods, which allow to evaluate the electrical activity of neuronal populations on the cerebral cortex by noninvasive recordings of electrical activity. However, due to fundamental physical limitations, the inverse problem for EEG and MEG is undefined and has no single solution. A regularisation, which consists in the use of additional restrictions imposed on the final solution, makes it possible to solve the problem. Depending on the regularization technique, it is possible to synthesize a number of algorithms: MNE, wMNE, Loreta, MCE, FOCUSS that use different a priori assumptions about the properties of the desired cortical distributed activity. The aim of the proposed study was to develop new algorithms for solving the inverse EEG and MEG problem based precisely on physiologically determined constraints.

Read more about the study at the link.