1 Multimodal Functional Imaging Lab, Department of Physics and PERFORM Centre, Concordia University, Montréal, Québec, Canada.
2 PERFORM Centre, Center for Studies in Behavioral Neurobiology, Department of Health, Kinesiology and Applied Physiology, Concordia University, Montréal, Québec, Canada.
3 Institut Universitaire de Gériatrie de Montréal and CRIUGM, CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montréal, Québec, Canada.
4 Aix-Marseille University, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France.
5 Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK.
6 Departement de Genie Electrique, Ecole de Technologie Superieure, Montreal, Quebec, Canada.
7 Centre de Recherches Mathematiques, Montréal, Québec, Canada.
8 Multimodal Functional Imagi

Simultaneous recording of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is a very promising non-invasive neuroimaging technique. However, EEG data obtained from the simultaneous EEG-fMRI are strongly influenced by MRI-related artefacts, namely gradient artefacts (GA) and ballistocardiogram (BCG) artefacts. When compared to the GA correction, the BCG correction is more challenging to remove due to its inherent variabilities and dynamic changes over time. The standard BCG correction (i.e., average artefact subtraction [AAS]), require detecting cardiac pulses from simultaneous electrocardiography (ECG) recording. However, ECG signals are also distorted and will become problematic for detecting reliable cardiac peaks. In this study, we focused on a beamforming spatial filtering technique to attenuate all unwanted source activities outside of the brain. Specifically, we applied the beamforming technique to attenuate the BCG artefact in EEG-fMRI, and also to recover meaningful task-based neural signals during an attentional network task (ANT) which required participants to identify visual cues and respond accurately. We analysed EEG-fMRI data in 20 healthy participants during the ANT, and compared four different BCG corrections (non-BCG corrected, AAS BCG corrected, beamforming + AAS BCG corrected, beamforming BCG corrected). We demonstrated that the beamforming approach did not only significantly reduce the BCG artefacts, but also significantly recovered the expected task-based brain activity when compared to the standard AAS correction. This data-driven beamforming technique appears promising especially for longer data acquisition of sleep and resting EEG-fMRI. Our findings extend previous work regarding the recovery of meaningful EEG signals by an optimized suppression of MRI-related artefacts.