Cardiac artifact subspace identification and elimination in cognitive MEG data using time-delayed decorrelation

To reduce physiological artifacts in magnetoencephalographic (MEG) and electroencephalographic recordings, a number of methods have been applied in the past such as principal component analysis, signal-space projection, regression using secondary information, and independent component analysis. This method has become popular as it does not have constraints such as orthogonality between artifact and signal or the need for a priori information. Applying the time-delayed decorrelation algorithm to raw data from a visual stimulation MEG experiment, we show that several of the independent components can be attributed to the cardiac artifact. Calculating an average cardiac activity shows that physiologically different excitation states of the heart produce similar field distributions in the MEG sensor system. This is equivalent to differing spectral properties of cardiac field distributions in the raw data. As a consequence, the algorithm combines, e.g., the R peak and the T wave of the cardiac cycle into a single component and the one-to-one assignment of each independent component with a physiological source is not justified in this case. To improve the signal quality of visually evoked fields, the multidimensional cardiac artifact subspace is suppressed from the data. To assess the preservation of the evoked signal after artifact suppression, a geometrical and a temporal measure are introduced. The suppression of cardiac and a wave artifacts allows, in our experimental setting, the reduction of the number of epochs to one half while preserving the visually evoked signal.