Artifact Reduction in Simultaneous EEG-fMRI: A Systematic Review of Methods and Contemporary Usage

Abstract

Simultaneous electroencephalography-functional MRI (EEG-fMRI) is a technique that combines temporal (largely from EEG) and spatial (largely from fMRI) indicators of brain dynamics. It is useful for understanding neuronal activity during many different event types, including spontaneous epileptic discharges, the activity of sleep stages, and activity evoked by external stimuli and decision-making tasks. However, EEG recorded during fMRI is subject to imaging, pulse, environment and motion artifact, causing noise many times greater than the neuronal signals of interest. Therefore, artifact removal methods are essential to ensure that artifacts are accurately removed, and EEG of interest is retained. This paper presents a systematic review of methods for artifact reduction in simultaneous EEG-fMRI from literature published since 1998, and an additional systematic review of EEG-fMRI studies published since 2016. The aim of the first review is to distill the literature into clear guidelines for use of simultaneous EEG-fMRI artifact reduction methods, and the aim of the second review is to determine the prevalence of artifact reduction method use in contemporary studies. We find that there are many published artifact reduction techniques available, including hardware, model based, and data-driven methods, but there are few studies published that adequately compare these methods. In contrast, recent EEG-fMRI studies show overwhelming use of just one or two artifact reduction methods based on literature published 15-20 years ago, with newer methods rarely gaining use outside the group that developed them. Surprisingly, almost 15% of EEG-fMRI studies published since 2016 fail to adequately describe the methods of artifact reduction utilized. We recommend minimum standards for reporting artifact reduction techniques in simultaneous EEG-fMRI studies and suggest that more needs to be done to make new artifact reduction techniques more accessible for the researchers and clinicians using simultaneous EEG-fMRI.

Keywords: BOLD; artifact; ballistocardiogram; electroencephalography; motion; simultaneous EEG-fMRI.

Figure 1

Visual examples of (A) gradient; and (B) ballistocardiogram (shown by arrows) and motion (circled) artifact on EEG recorded during fMRI. For (B), GA has been removed using an adaptive average artifact subtraction (AAS) method (12). EEG channel numbers are given on the left of each figure. Channels below the ECG (mvmt 2–4) are recordings from carbon wire motion loops for measuring motion. The horizontal axis of each figure shows time in seconds. Environmental artifact is not seen visually in this recording, and for visual examples of environmental artifact, we refer the reader to (13, 14).

Figure 2

PRISMA chart (25), EEG-fMRI novel artifact reduction methods, 1998–2019.

Figure 3

PRISMA chart (25), showing search strategy for recent EEG-fMRI papers, 2016–2019.

Figure 4

Gradient artifact removal in EEG-fMRI papers, published between 2016 and 2019 (n = 244). AAS, average artifact subtraction; OBS, optimal basis set.

Figure 5

Ballistocardiogram (BCG) artifact removal method in literature using EEG-fMRI published between 2016 and 2019 (n = 244). AAS, average artifact subtraction; OBS, optimal basis set; ICA, independent components analysis; PCA, principle components analysis.

Figure 6

Use of software toolboxes for filtering artifact from EEG collected during fMRI, papers published 2016–2019 (n = 244).

Figure 7

Recommendations for Removal of artifact in EEG-fMRI studies. Top: Recommendations for all EEG-fMRI setups; Bottom: Recommendations based on hardware available and study design.