MEGnet: Automatic ICA-based artifact removal for MEG using spatiotemporal convolutional neural networks.

ABSTRACT: Magnetoencephalography (MEG) is a functional neuroimaging tool that records the magnetic fields induced by neuronal activity; however, signal from non-neuronal sources can corrupt the data. Eye-blinks, saccades, and cardiac activity are three of the most common sources of non-neuronal artifacts. They can be measured by affixing eye proximal electrodes, as in electrooculography (EOG), and chest electrodes, as in electrocardiography (ECG), however this complicates imaging setup, decreases patient comfort, and can induce further artifacts from movement. This work proposes an EOG- and ECG-free approach to identify eye-blinks, saccades, and cardiac activity signals for automated artifact suppression. The contribution of this work is three-fold. First, using a data driven, multivariate decomposi

SUBMITTER: Treacher AH 

PROVIDER: S-EPMC9125748 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature