Joint MEG-EEG signal decomposition using the coupled SECSI framework: Validation on a controlled experiment

Abstract

Simultaneously recorded magnetoencephalography (MEG) and electroencephalography (EEG) signals can benefit from a joint analysis based on coupled Canonical Polyadic (CP) tensor decompositions. The coupled CP decomposition jointly decomposes tensors that have at least one factor matrix in common. The Coupled — Semi-Algebraic framework for approximate CP decomposition via SImultaneous matrix diagonalization framework (C-SECSI) efficiently estimates the factor matrices with adjustable complexity-accuracy trade-offs. Our objective is to decompose simultaneously recorded MEG and EEG signals above intact skull and above two conducting skull defects using C-SECSI in order to determine how such a tissue anomaly of the head is reflected in the tensor rank. The source of the MEG and EEG signals is a miniaturized electric dipole that is implanted into a rabbit's brain. The dipole is shifted along a line under the skull defects, and measurements are taken at regularly spaced points. The coupled SECSI analysis is conducted for MEG and EEG measurement series and ranks 1–3. This coupled decomposition produces meaningful components representing the three characteristic signal topographies for source positions under defect 1 and the positions on either side of defect 1. The rank estimation with respect to the complexity-accuracy trade-off of rank 3 reflects the three characteristic cases well and matches the dimensions spanned by the data set. The intact skull MEG signals show a higher complexity (rank 3) than the corresponding EEG signals (rank 1). The C-SECSI framework is a very promising method for blind signal separation in multidimensional data with coupled modalities, such as simultaneous MEG-EEG.