Vadim Zotev1, Han Yuan1, Raquel Phillips1, Jerzy Bodurka1
1Laureate Institute for Brain Research, Tulsa, OK, United States
EEG performed simultaneously with fMRI with millisecond temporal resolution is particularly sensitive to rapid head rotations. Motion artifacts in EEG-fMRI recordings contain useful real-time information about such rotations. We describe a novel and simple approach for deriving additional fMRI motion regressors directly from EEG motion artifacts, and demonstrate its efficiency for patients with major depression. We show that inclusion of four EEG based motion regressors to the GLM model in addition to six fMRI motion parameters reduces average standard deviation of the GLM fit error by 5-10%, especially in frontal and occipital regions. Possible applications of this approach are discussed.