Development of a method for increasing the resolution of neuroimaging based on solving the inverse problem of electroencephalography

Subject of the study. The spatial resolution of functional images reconstructed by solving the electroencephalography (EEG) inverse problem is investigated. Objective of the work . This study aims to develop a method for increasing the spatial resolution of functional images and for optimizing the selection of image reconstruction algorithms using metrics derived from statistical analysis of neural activity distributions. Method . Modeling of the solution of the inverse EEG problem was performed using the MNE-Python library in Python. A computational experiment was designed using EEG and functional magnetic resonance imaging data to verify the proposed methodology. Main results . A method for integrating spatial filtering into neuroimaging algorithms was developed, enabling a twofold improvement in the spatial resolution of a functional image using only functional EEG data. In addition, a metric for assessing the spatial resolution of a neuroimaging method was proposed, allowing unambiguous selection of the most appropriate algorithm for functional image processing. Practical significance . The proposed spatial filtering approach makes it possible to distinguish sources of electrophysiological activity separated by approximately 20 mm. The developed image quality criterion enables the selection of the optimal image processing algorithm for a specific application problem.