Ballistocardiogram Artifact Reduction in Simultaneous EEG-fMRI Using Deep Learning.

ABSTRACT:

Objective

The concurrent recording of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is a technique that has received much attention due to its potential for combined high temporal and spatial resolution. However, the ballistocardiogram (BCG), a large-amplitude artifact caused by cardiac induced movement contaminates the EEG during EEG-fMRI recordings. Removal of BCG in software has generally made use of linear decompositions of the corrupted EEG. This is not ideal as the BCG signal propagates in a manner which is non-linearly dependent on the electrocardiogram (ECG). In this paper, we present a novel method for BCG artifact suppression using recurrent neural networks (RNNs).

Methods

EEG signals were recovered by training RNNs on the nonlinear

SUBMITTER: McIntosh JR 

PROVIDER: S-EPMC7808341 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature