Carbon-wire loop based artifact correction outperforms post-processing EEG/fMRI corrections—A validation of a real-time simultaneous EEG/fMRI correction method

Abstract

Simultaneous EEG-fMRI combines two powerful neuroimaging techniques, but the EEG signal suffers from severe artifacts in the MRI environment that are difficult to remove. These are the MR scanning artifact and the blood-pulsation artifact--strategies to remove them are a topic of ongoing research. Additionally large, unsystematic artifacts are produced across the full frequency spectrum by the magnet's helium pump (and ventilator) systems which are notoriously hard to remove. As a consequence, experimenters routinely deactivate the helium pump during simultaneous EEG-fMRI acquisitions which potentially risks damaging the MRI system and necessitates more frequent and expensive helium refills. We present a novel correction method addressing both helium pump and ballisto-cardiac (BCG) artifacts, consisting of carbon-wire loops (CWL) as additional sensors to accurately track unpredictable artifacts related to subtle movements in the scanner, and an EEGLAB plugin to perform artifact correction. We compare signal-to-noise metrics of EEG data, corrected with CWL and three conventional correction methods, for helium pump off and on measurements. Because the CWL setup records signals in real-time, it fits requirements of applications where immediate correction is necessary, such as neuro-feedback applications or stimulation time-locked to specific sleep oscillations. The comparison metrics in this paper relate to: (1) the EEG signal itself, (2) the "eyes open vs. eyes closed" effect, and (3) an assessment of how the artifact corrections impacts the ability to perform meaningful correlations between EEG alpha power and the BOLD signal. Results show that the CWL correction corrects for He pump artifact and also produces EEG data more comparable to EEG obtained outside the magnet than conventional post-processing methods.

Modulation of simultaneously collected hemodynamic and electrophysiological functional connectivity by ketamine and midazolam

“…There is the possibility that the EEG contains small motion artefacts (Fellner et al, 2016) that our rejection and correction methods were not able to capture, thereby limiting its usefulness as a direct measure of neural activity to compare the BOLD signal fluctuations with. However, our EEG pharmacological modulations look strikingly similar to previous MEG estimates (Hall, Barnes, Furlong, Seri, & Hillebrand, 2010;Muthukumaraswamy et al, 2015), although in the future the addition of hardware to better capture participant motion would be a valuable addition to studies such as this (Abbott et al, 2015;van der Meer et al, 2016). Additionally, the methods utilized in this study do not represent an exhaustive analysis of the different methodologies used to derive connectivity measures in either fMRI or EEG; the lack of a formal relationship found between the modalities of drug modulations to functional connectivity does not preclude future positive findings, which could involve using partial correlation measures to disentangle indirect and direct connections, or comparing dynamic connectivity.…”

Unimodal Versus Bimodal EEG-fMRI Neurofeedback of a Motor Imagery Task

“…The BCG artifact and motion artifacts from the subject or the environment (vibrations from helium pump and ventilation) are particularly hard to correct. The development of new methods for correcting these artifacts is an ongoing topic of research, but few options are available for online correction (Allen et al, 1998, 2000; Krishnaswamy et al, 2016; Mayeli et al, 2016; Wu et al, 2016; van der Meer et al, 2016). Interestingly, a recent approach consists in using the EEG not only as a brain imaging modality but also as a motion sensor to correct for motion artifact (Jorge et al, 2015; Wong et al, 2016).…”

Spurious correlations in simultaneous EEG-fMRI driven by in-scanner movement

“…An interesting solution to control for motion on EEG-fMRI data is to record motion co-registered to EEG recordings with a sufficiently high sampling rate. Recently, several motion-recording approaches have been proposed (Abbott et al, 2014;Chowdhury et al, 2014;Jorge et al, 2015;LeVan et al, 2013;van der Meer et al, 2015).…”