Independent Vector Analysis for Gradient Artifact Removal in Concurrent EEG-fMRI Data

Abstract

We consider the problem of removing gradient artifact from electroencephalogram (EEG) signal, recorded concurrently with functional magnetic resonance imaging (fMRI) acquisition. We estimate the artifact by exploiting its quasi-periodicity over the epochs and its similarity over the different channels by using independent vector analysis, a recent extension of independent component analysis for multiple datasets. The method fully makes use of the spatio-temporal information by using spatial dependences across channels to estimate the artifact for a particular channel. Thus, it provides robustness with respect to uncontrollable changes such as head movement and fluctuations in the B0 field during the acquisition. Results using both simulated data with gradient artifact and EEG data collected concurrently with fMRI show the desirable performance of the new method.