Exploring the relative efficacy of motion artefact correction techniques for EEG data acquired during simultaneous fMRI

Abstract

Simultaneous EEG‐fMRI allows multiparametric characterisation of brain function, in principle enabling a more complete understanding of brain responses; unfortunately the hostile MRI environment severely reduces EEG data quality. Simply eliminating data segments containing gross motion artefacts [MAs] (generated by movement of the EEG system and head in the MRI scanner's static magnetic field) was previously believed sufficient. However recently the importance of removal of all MAs has been highlighted and new methods developed. A systematic comparison of the ability to remove MAs and retain underlying neuronal activity using different methods of MA detection and post‐processing algorithms is needed to guide the neuroscience community. Using a head phantom, we recorded MAs while simultaneously monitoring the motion using three different approaches: Reference Layer Artefact Subtraction (RLAS), Moiré Phase Tracker (MPT) markers and Wire Loop Motion Sensors (WLMS). These EEG recordings were combined with EEG responses to simple visual tasks acquired on a subject outside the MRI environment. MAs were then corrected using the motion information collected with each of the methods combined with different analysis pipelines. All tested methods retained the neuronal signal. However, often the MA was not removed sufficiently to allow accurate detection of the underlying neuronal signal. We show that the MA is best corrected using the RLAS combined with post‐processing using a multichannel, recursive least squares (M‐RLS) algorithm. This method needs to be developed further to enable practical utility; thus, WLMS combined with M‐RLS currently provides the best compromise between EEG data quality and practicalities of motion detection.

Alpha/beta power decreases track the fidelity of stimulus-specific information

“…This helped improve the accuracy of the gradient and BCG templates that were subtracted from the data. After gradient and pulse artefact correction the data from the motion sensors were used in a multi-channel recursive least squares algorithm to regress out the remaining movement-related artifacts (Bouchard and Quednau, 2000; Masterton et al, 2007) (while retaining brain signal; Daniel et al, 2019) using custom scripts previously implemented by Jorge and colleagues (Jorge et al, 2015).…”

Alpha/beta power decreases track the fidelity of stimulus-specific information

“…This helped improve the accuracy of the gradient and BCG templates that were subtracted from the data. After gradient and pulse artefact correction the data from the motion sensors were used in a multi-channel recursive least squares algorithm to regress out the remaining movement-related artifacts 42,43 (while retaining brain signal 44 ) using custom scripts previously implemented by Jorge and colleagues 38 .…”

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

“…In the literature search conducted, no papers specifically dealt with removal techniques for EOG artifact from EEG-fMRI datasets. There are, however, many papers that describe removal techniques for EOG artifact from datasets recorded outside the MRI environment [for a recent review, see (138)]. These methods are likely also suitable for within-MRI recording, provided that GA and BCG artifact are adequately removed in previous steps.…”

“…In addition, techniques used to directly measure BCG-related artifact (as outlined in section 4.2.3) can also be used to filter out gross head motion. A recent study ( 138 ) compared an Optical Motion Tracking system (MPT) ( 139 ) and two direct artifact recording methods: a reference layer cap ( 129 ) and isolation of electrodes from the scalp ( 123 ). The direct artifact recording measures both outperformed the MPT in reducing artifact during different tasks and different types of motion ( 138 ).…”

“…A recent study ( 138 ) compared an Optical Motion Tracking system (MPT) ( 139 ) and two direct artifact recording methods: a reference layer cap ( 129 ) and isolation of electrodes from the scalp ( 123 ). The direct artifact recording measures both outperformed the MPT in reducing artifact during different tasks and different types of motion ( 138 ). Therefore, hardware to directly record artifact may be a good investment, as it is able to reduce both BCG and gross head motion artifact with greater accuracy than many data-driven methods ( 132 , 136 ), and it may provide more accurate artifact estimation compared with motion detection hardware such as Optical Motion Tracking systems ( 138 ).…”