Multi-dimensional model order estimation using LineAr Regression of Global Eigenvalues (LaRGE) with applications to EEG and MEG recordings

摘要

The efficient estimation of an approximate model order is very important for real applications with multi-dimensional data if the observed low rank data is corrupted by additive noise. In this paper, we present a novel robust method for model order estimation of multi-dimensional data based on the LineAr Regression of Global Eigenvalues (LaRGE). The LaRGE method uses the multi-linear singular values obtained from the HOSVD of the measurement tensor to construct global eigenvalues. In contrast to the Modified Exponential Test (EFT) that also exploits the approximate exponential profile of the noise eigenvalues, LaRGE does not require the calculation of the probability of false alarm. Therefore, it is well suited for the analysis of biomedical data. The excellent performance of the LaRGE method is illustrated via simulations and results obtained from EEG as well as MEG recordings.