Exploring the origins of EEG motion artefacts during simultaneous fMRI acquisition: Implications for motion artefact correction

Abstract

Motion artefacts (MAs) are induced within EEG data collected simultaneously with fMRI when the subject's head rotates relative to the magnetic field. The effects of these artefacts have generally been ameliorated by removing periods of data during which large artefact voltages appear in the EEG traces. However, even when combined with other standard post-processing methods, this strategy does not remove smaller MAs which can dominate the neuronal signals of interest. A number of methods are therefore being developed to characterise the MA by measuring reference signals and then using these in artefact correction. These methods generally assume that the head and EEG cap, plus any attached sensors, form a rigid body which can be characterised by a standard set of six motion parameters. Here we investigate the motion of the head/EEG cap system to provide a better understanding of MAs. We focus on the reference layer artefact subtraction (RLAS) approach, as this allows measurement of a separate reference signal for each electrode that is being used to measure brain activity.Through a series of experiments on phantoms and subjects, we find that movement of the EEG cap relative to the phantom and skin on the forehead is relatively small and that this non-rigid body movement does not appear to cause considerable discrepancy in artefacts between the scalp and reference signals. However, differences in the amplitude of these signals is observed which may be due to differences in geometry of the system from which the reference signals are measured compared with the brain signals. In addition, we find that there is non-rigid body movement of the skull and skin which produces an additional MA component for a head shake, which is not present for a head nod. This results in a large discrepancy in the amplitude and temporal profile of the MA measured on the scalp and reference layer, reducing the efficacy of MA correction based on the reference signals.Together our data suggest that the efficacy of the correction of MA using any reference-based system is likely to differ for different types of head movement with head shake being the hardest to correct. This provides new information to inform the development of hardware and post-processing methods for removing MAs from EEG data acquired simultaneously with fMRI data.

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

“…These EEG data were combined with neuronal EEG data acquired on a human subject outside of the MRI environment. The MAs were then corrected using motion information collected from each of the different methods in conjunction with number of previously described analysis pipelines Masterton et al, 2007;Maziero et al, 2016;Spencer et al, 2018). We showed that the MA was best corrected using the RLAS motion information combined with a multichannel recursive least squares (M-RLS) fitting algorithm.…”

“…Given the known discrepancy between the MAs induced on the scalp and reference layers (Spencer, Smith, Chowdhury, Bowtell, & Mullinger, 2018), a simple linear fit of each reference electrode signal to the corresponding scalp electrode signal was also performed. Given the known discrepancy between the MAs induced on the scalp and reference layers (Spencer, Smith, Chowdhury, Bowtell, & Mullinger, 2018), a simple linear fit of each reference electrode signal to the corresponding scalp electrode signal was also performed.…”

“…The simplest artefact correction method then consisted of a subtraction of the signal from the reference layer electrode directly overlaying each of the scalp layer electrodes, as previously implemented . Given the known discrepancy between the MAs induced on the scalp and reference layers (Spencer, Smith, Chowdhury, Bowtell, & Mullinger, 2018), a simple linear fit of each reference electrode signal to the corresponding scalp electrode signal was also performed. This fitting was performed with a least-squares fit, which was non-adaptive over the timecourse, minimising the chance of over-fitting and consequent removal of neuronal signals of interest.…”

Simultaneous PET-MR-EEG: Technology, Challenges and Application in Clinical Neuroscience

“…Simple head motions such as nodding and shaking greatly affect the low frequency spectrum (less than 10 Hz) [18] of the EEG signals. A number of techniques have been proposed for motion correction [19]- [21], but none offer perfect correction of motion related artifacts [22]. Considering the difficulties associated with the correction of motion related artifacts, many studies simply remove the period of EEG data during motion via visual inspection [23], [24].…”

Evaluation of smartphone interactions on drivers’ brain function and vehicle control in an immersive simulated environment

“…Clearly, the use of EEG in driving research has multiple benefits over fMRI, particularly regarding its amenability to more naturalistic environments and tasks. However, similar to fMRI, EEG often suffers from methodological drawbacks, primarily related to motion artifacts 23 and environmental noise 24 , that hinder its use in studies of real-world driving. Moreover, the poor spatial resolution of EEG 25 often makes localization of cortical activity difficult, thus further reducing its ability to highlight specific cortical regions that underlie common driving behaviors.…”