Deep Learning for Non-Invasive Cortical Potential Imaging

Abstract

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.

ConvDip: A convolutional neural network for better EEG Source Imaging

“…In a recent work Razorenova et al (2020) showed that the inverse problem for cortical potential imaging could be solved using a deep U-Net architecture, once more providing a strong case for the feasibility of ANNs to solve the EEG inverse problem . Tankelevich (2019) showed that a deep feed-forward network can find the correct set of source clusters that produced a given scalp signal.…”