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0000−0002−3386−6914] , Nikolay Yavich 1[0000−0002−8913−7710] , Mikhail Malovichko 1[0000−0002−7618−0998] , Maxim Fedorov 1[0000−0003−3901−3565] , Nikolay Koshev 1[0000−0001−7241−3304] , and Dmitry V. Dylov 1[0000−0003−2251−3221]Abstract. Electroencephalography (EEG) is a well-established non-invasive technique to measure the brain activity, albeit with a limited spatial resolution. Variations in electric conductivity between different tissues distort the electric fields generated by cortical sources, resulting in a smeared potential measurements on the scalp. One needs to solve an ill-posed inverse problem to recover the original neural activity. In this article, we present a generic methodology of recovering the cortical potentials from the EEG measurement by introducing a new inverse-problem solver based on deep convolutional neural networks (CNN) in a U-Net configuration. The solver was trained on a paired dataset from a synthetic head conductivity model by solving the diffusion equations with the finite element method (FEM). This is the first method that provides robust translation of EEG data to the cortex surface using deep learning. Supplying a fast and accurate interpretation of the tracked EEG signal, the proposed approach is a candidate for future non-invasive brain-computer interface devices.

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A stable relation between words and referent objects or events underlies human language. One of the most fundamental questions is how brain processes new words in order to form new lexical items. The answer to such questions will bring significant breakthrough in multiple fields, ranging from methods of language teaching and speech correction programs for children with late development to clinical rehabilitation of patients with speech impairments and neurophysiological functional tests of language network. This review presents the current state of Russian and foreign studies dedicated to new words learning in auditory modality. We tried to consider all varieties of techniques and paradigms in the field. Equal attention is paid both to studies of the phonological processing of a word (recognition of a phonetic pattern), and to works which consider the ways in which word acquire semantics. We discuss experiments carried out with an aid of such neuroimaging methods as fMRI, EEG / MEG, etc.