Towards high-quality simultaneous EEG-fMRI at 7 T: Detection and reduction of EEG artifacts due to head motion

• Published in: NeuroImage (Orlando, Fla.) Vol. 120; pp. 143 - 153
• Main Authors: Jorge, João, Grouiller, Frédéric, Gruetter, Rolf, van der Zwaag, Wietske, Figueiredo, Patrícia
• Format: Journal Article
• Language: English
• Published: United States 15.10.2015
• Subjects: Adult; Brain - physiology; Brain Waves - physiology; Electroencephalography - instrumentation; Electroencephalography - methods; Electroencephalography - standards; Evoked Potentials, Visual - physiology; Female; Head; Humans; Magnetic Resonance Imaging - standards; Male; Motion; Multimodal Imaging - instrumentation; Multimodal Imaging - methods; Multimodal Imaging - standards; Young Adult; Ultra-high field; Adaptive filtering; Visual evoked potential; Simultaneous EEG-fMRI; Head motion
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2015.07.020

The enhanced functional sensitivity offered by ultra-high field imaging may significantly benefit simultaneous EEG-fMRI studies, but the concurrent increases in artifact contamination can strongly compromise EEG data quality. In the present study, we focus on EEG artifacts created by head motion in the static B0 field. A novel approach for motion artifact detection is proposed, based on a simple modification of a commercial EEG cap, in which four electrodes are non-permanently adapted to record only magnetic induction effects. Simultaneous EEG-fMRI data were acquired with this setup, at 7 T, from healthy volunteers undergoing a reversing-checkerboard visual stimulation paradigm. Data analysis assisted by the motion sensors revealed that, after gradient artifact correction, EEG signal variance was largely dominated by pulse artifacts (81-93%), but contributions from spontaneous motion (4-13%) were still comparable to or even larger than those of actual neuronal activity (3-9%). Multiple approaches were tested to determine the most effective procedure for denoising EEG data incorporating motion sensor information. Optimal results were obtained by applying an initial pulse artifact correction step (AAS-based), followed by motion artifact correction (based on the motion sensors) and ICA denoising. On average, motion artifact correction (after AAS) yielded a 61% reduction in signal power and a 62% increase in VEP trial-by-trial consistency. Combined with ICA, these improvements rose to a 74% power reduction and an 86% increase in trial consistency. Overall, the improvements achieved were well appreciable at single-subject and single-trial levels, and set an encouraging quality mark for simultaneous EEG-fMRI at ultra-high field.