Statistical feature extraction for artifact removal from concurrent fMRI-EEG recordings.
We propose a set of algorithms for sequentially removing artifacts related to MRI gradient switching and cardiac pulsations from electroencephalography (EEG) data recorded during functional magnetic resonance imaging (fMRI). Special emphasis is directed upon the use of statistical metrics and methods for the extraction and selection of features that characterize gradient and pulse artifacts. To remove gradient artifacts, we use channel-wise filtering based on singular value decomposition (SVD). To remove pulse artifacts, we first decompose data into temporally independent components and then select a compact cluster of components that possess sustained high mutual information with the electrocardiogram (ECG). After the removal of these components, the time courses of remaining components are filtered by SVD to remove the temporal patterns phase-locked to the cardiac timing markers derived from the ECG. The filtered component time courses are then inversely transformed into multi-channel EEG time series free of pulse artifacts. Evaluation based on a large set of simultaneous EEG-fMRI data obtained during a variety of behavioral tasks, sensory stimulations and resting conditions showed excellent data quality and robust performance attainable with the proposed methods. These algorithms have been implemented as a Matlab-based toolbox made freely available for public access and research use.
Advanced MRI Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20982-1065, USA. liuz5@mail.nih.gov
Zhongming
Z
Liu
Jacco A
JA
de Zwart
Peter
P
van Gelderen
Li-Wei
LW
Kuo
Jeff H
JH
Duyn
D000328:Adult; D000465:Algorithms; D000513:Alpha Rhythm / Q000502:physiology; D016477:Artifacts; D003627:Data Interpretation, Statistical; D004562:Electrocardiography; D004569:Electroencephalography / Q000706:statistics & numerical data*; D005074:Evoked Potentials, Visual / Q000502:physiology; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D008297:Male; D010100:Oxygen / Q000097:blood; D010363:Pattern Recognition, Automated; D019047:Phantoms, Imaging; D010775:Photic Stimulation; D025341:Principal Component Analysis; D055815:Young Adult
D010100:Oxygen