Automated Pipeline for EEG Artifact Reduction (APPEAR) Recorded during fMRI

TL;DR

This paper introduces APPEAR, an open-source, fully automatic pipeline that effectively reduces MRI and physiological artifacts in EEG data collected during fMRI, matching manual correction quality and enabling large-scale studies.

Contribution

The paper presents the first comprehensive automatic toolbox for EEG artifact reduction during simultaneous fMRI, combining average template subtraction and ICA.

Findings

Automated correction matches manual review in frequency and wavelet analyses.

No significant differences between manual and automated corrections in ERP measures.

APPEAR enables efficient processing of large EEG-fMRI datasets.

Abstract

Objective. EEG data collected during fMRI acquisition are contaminated with MRI gradients and ballistocardiogram (BCG) 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 (ICA) 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 [ERP]; 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.