Yavich, N. | Search bioRxiv

bioRxiv2022

Yttrium-iron garnet magnetometer in MEG: Advance towards multi-channel arrays

Recently, a new kind of sensor applicable in magnetoencephalography (MEG) has been presented: a solid-state yttrium-iron garnet magnetometer (YIGM). The feasibility of YIGM was proved in alpha-rhythm registration experiment. In the current paper, we propose the analysis of lead-field matrices for different possible YIGM multichannel on-scalp sensor layouts with respect to the information theory. We use real noise level of the new sensor to compute signal power, signal-to-noise ration (SNR) and total information capacity, and compare them with corresponding metrics that can be obtained with well-established MEG systems: based on superconducting quantum interference devices (SQUIDs) and optically-pumped magnetometers (OPMs). This simulation study is aimed to shed some light on the direction for further development of YIGM sensor, namely creation of multi-channel YIG-MEG.

neuroscience↗

bioRxiv2020

Deep Learning for Cortical Potential Imaging

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 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 method of recovering the cortical potentials from the EEG measurement by introducing a new inverse-problem solver based on deep Convolutional Neural Networks (CNN) in paired (U-Net) and unpaired (DualGAN) configurations. The solvers were trained on synthetic EEG-ECoG pairs that were generated using a head conductivity model computed using the Finite Element Method (FEM). These solvers are the first of their kind, that provide robust translation of EEG data to the cortex surface using deep learning. Providing a fast and accurate interpretation of the tracked EEG signal, our approach promises a boost to the spatial resolution of the future EEG devices.

neuroscience↗