Noise Adjusted PCA for Finding the Subspace of Evoked Dependent Signals from MEG Data

Evoked signals that underlie multi-channel magnetoencephalography (MEG) data can be dependent. It follows that ICA can fail to separate the evoked dependent signals. As a first step towards separation, we adress the problem of finding a subspace of possibly mixed evoked signals that are separated from the non-evoked signals. Specifically, a vector basis of the evoked subspace and the associated mixed signals are of interest. It was conjectured that ICA followed by clustering is suitable for this subspace analysis. As an alternative, we propose the use of noise adjusted PCA (NAPCA). This method uses two covariance matrices obtained from pre- and post-stimulation data in order to find a subspace basis. Subsequently, the associated signals are obtained by linear projection onto the estimated basis. Synthetic and recorded data are analyzed and the performance of NAPCA and the ICA approach is compared. Our results suggest that ICA followed by clustering is a valid approach. Nevertheless, NAPCA outperforms the ICA approach for synthetic and for real MEG data from a study with simultaneous visual and auditory stimulation. Hence, NAPCA should be considered as a viable alternative for the analysis of evoked MEG data.