Automated pipeline for EEG artifact reduction (APPEAR) recorded during fMRI

Ahmad Mayeli,Jerzy Bodurka,Hazem Refai,Kaylee Henry,Obada Al Zoubi,Qingfei Luo,Vadim Zotev,Evan J White,Chung Ki Wong

doi:10.1088/1741-2552/ac1037

Abstract

Objective. Simultaneous electroencephalography-functional magnetic resonance imaging (EEG-fMRI) recordings offer a high spatiotemporal resolution approach to study human brain and understand the underlying mechanisms mediating cognitive and behavioral processes. However, the high susceptibility of EEG to MRI-induced artifacts hinders a broad adaptation of this approach. More specifically, EEG data collected during fMRI acquisition are contaminated with MRI gradients and ballistocardiogram artifacts, in addition to artifacts of physiological origin. There have been several attempts for reducing these artifacts with manual and time-consuming pre-processing, which may result in biasing EEG data due to variations in selecting steps order, parameters, and classification of artifactual independent components. Thus, there is a strong urge to develop a fully automatic and comprehensive pipeline for reducing all major EEG artifacts. In this work, we introduced an open-access toolbox with a fully automatic pipeline for reducing artifacts from EEG data collected simultaneously with fMRI (refer to APPEAR). Approach. The pipeline integrates average template subtraction and independent component analysis to suppress both MRI-related and physiological artifacts. To validate our results, we tested APPEAR on EEG data recorded from healthy control subjects during resting-state (n= 48) and task-based (i.e. event-related-potentials (ERPs); n= 8) paradigms. The chosen gold standard is an expert manual review of the EEG database. Main results. We compared manually and automated corrected EEG data during resting-state using frequency analysis and continuous wavelet transformation and found no significant differences between the two corrections. A comparison between ERP data recorded during a so-called stop-signal task (e.g. amplitude measures and signal-to-noise ratio) also showed no differences between the manually and fully automatic fMRI-EEG-corrected data. Significance. APPEAR offers the first comprehensive open-source toolbox that can speed up advancement of EEG analysis and enhance replication by avoiding experimenters’ preferences while allowing for processing large EEG-fMRI cohorts composed of hundreds of subjects with manageable researcher time and effort.

Highlights

Electroencephalography (EEG) and functional Magnetic Resonance Imaging have both been widely used as noninvasive and safe techniques for detecting and characterizing changes in brain states and their relation to neuronal activity [1]
When compared to manual correction, which could take up to hours, automated pipeline for EEG artifacts reduction (APPEAR) took less than 15min/subject
The percentage length of the original signal was marked as bad segments on an average across the different sessions for the EEG data are as follows: Rest: 15.7 ± 8.4 sec; Task: 14.04 ± 10.18 sec

Summary

Introduction

Electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) have both been widely used as noninvasive and safe techniques for detecting and characterizing changes in brain states and their relation to neuronal activity [1]. Simultaneous EEG-fMRI leverages fMRI’s capacity to measure whole brain hemodynamic activities at the high spatial resolution and high temporal resolution of EEG signals, directly reflecting electrophysiological brain activities [2]. Recording EEG inside an MRI scanner and during fMRI acquisition results in EEG signal contamination from MRI-related artifacts. The MRI gradient-induced artifact (gradient artifact) results from a combination of switching magnetic field gradients required for spatial encoding during the fMRI acquisition. The ballistocardiogram (BCG) artifact appears to be a result of cardiac activity-induced head movements in the static polarizing B0 magnetic field inside the MRI scanner [5]. Other types of artifacts, such as muscle and ocular artifacts, can be present in EEG data regardless of whether the EEG is recorded inside or outside the MRI scanner [6, 7]

Methods

Results

Discussion

Conclusion