On estimating the signal subspace dimension of high-density multichannel magnetoencephalogram measurements

Accurate estimates of the dimension and an (orthogonal) basis of the signal subspace of noise corrupted multi-channel measurements are essential for accurate identification and extraction of any signals of interest within that subspace. For most biomedical signals comprising very large numbers of channels, including the magnetoencephalogram (MEG), the "true" number of underlying signals ¿ although ultimately unknown ¿ is unlikely to be of the same order as the number of measurements, and has to be estimated from the available data. This work examines several second-order statistical approaches to signal subspace (dimension) estimation with respect to their underlying assumptions and their performance in high-dimensional measurement spaces using 151-channel MEG data. The purpose is to identify which of these methods might be most appropriate for modeling the signal subspace structure of high-density MEG data recorded under controlled conditions, and what are the practical consequences with regard to the subsequent application of biophysical modeling and statistical source separation techniques.