Model order estimation for blind source separation of multichannel magnetoencephalogram and electroencephalogram signals

Most blind source separation (BSS) approaches — especially independent component analysis (ICA) — assume a noiseless mixture of the same number of sources as sensors. It is doubtful, however, whether this assumption actually holds for multichannel magnetoencephalogram (MEG) and electroencephalogram (EEG) measurements comprising a large number of channels. Corroborating and extending previous results, this work further examines the utility of second-order statistical methods based on probabilistic principal component analysis (PPCA) and factor analysis (FA) models for estimating the number of underlying sources in multichannel MEG and EEG. Compared with conventional PCA-based eigenvalue thresholding, both PPCA and FA approaches yield stable model order estimates which are almost independent of total signal power. The FA model provides a more optimal description of both MEG and EEG data than PPCA, in terms of balancing goodness-of-fit and parsimony. These findings add to the growing evidence that anisotropic “sensor noise” may be a statistically robust characteristic of both the EEG and MEG, which most BSS algorithms and applications do not address.