Comparison and Improvement of Inverse Techniques for MEG Source Connectivity Network Reconstruction

Abstract

Recent studies on bio-electromagnetic inverse problems have shown that a satisfactory understanding of source mechanisms requires to perform source connectivity analyses. This paper focuses on the comparison of inverse techniques for reconstructing the source connectivity network. The results confirm that the noise effect for linear estimation technique is direct, while, for spatial filtering technique the effect is indirect. Linear estimation is advantageous for the connectivity reconstruction of high quality magnetoencephalography (MEG) data, while, the benefit for the case of spatial filter is low SNR environments. This paper also proposes a modified spatial filtering method to improve the source connectivity reconstruction by using the correlation gram matrix. The results show that the proposed method can increase the reconstruction accuracy, decrease the error fluctuation and enhance the representation for profiles of the original source connectivity network.