Removal of physiological artifacts from simultaneous EEG and fMRI recordings. Issue 10 (October 2021)

• Record Type: Journal Article
• Authors: Daly, Ian
• Is Part Of: Clinical neurophysiology. Volume 132:Issue 10(2021)
• Page Start: 2371
• Page End: 2383
• Publication Date: 2021-10
• Subjects: fMRI -- EEG -- Artifact removal -- ICA; Neurophysiology -- Periodicals; Electroencephalography -- Periodicals; Electromyography -- Periodicals; Neurology -- Periodicals; 612.8
• DOI: 10.1016/j.clinph.2021.05.036
• Languages: English
• ISSNs: 1388-2457

Abstract

Highlights: A novel EEG physiological artifact removal method is presented for jointly recorded EEG with fMRI. We evaluate the method on a joint EEG-fMRI dataset and compare our method to a state-of-the-art artefact removal method. The results show that our method is able to effectively remove physiological artefacts. Abstract: Objective: Simultaneous recording of the electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) allows a combination of eletrophysiological and haemodynamic information to be used to form a more complete picture of cerebral dynamics. However, EEG recorded within the MRI scanner is contaminated by both imaging artifacts and physiological artifacts. The majority of the techniques used to pre-process such EEG focus on removal of the imaging and balistocardiogram artifacts, with some success, but don't remove all other physiological artifacts. Methods: We propose a new offline EEG artifact removal method based upon a combination of independent component analysis and fMRI-based head movement estimation to aid the removal of physiological artifacts from EEG recorded during EEG-fMRI recordings. Our method makes novel use of head movement trajectories estimated from the fMRI recording in order to assist with identifying physiological artifacts in the EEG and is designed to be used after removal of the fMRI imaging artifact from the EEG. Results: We evaluate our method on EEG recorded during a joint EEG-fMRI session from healthy adult participants. Our method significantly reduces the influence of all types of physiological artifacts on the EEG. We also compare our method with a state-of-the-art physiological artifact removal method and demonstrate superior performance removing physiological artifacts. Conclusions: Our proposed method is able to remove significantly more physiological artifact components from the EEG, recorded during a joint EEG-fMRI session, than other state-of-the-art methods. Significance: Our proposed method represents a marked improvement over current processing pipelines for removing physiological noise from EEG recorded during a joint EEG-fMRI session.