Directed EEG neural network analysis by LAPPS (p[formula omitted]1) Penalized sparse Granger approach

Abstract

The conventional multivariate Granger Analysis (GA) of directed interactions has been widely applied in brain network construction based on EEG recordings as well as fMRI. Nevertheless, EEG is usually inevitably contaminated by strong noise, which may cause network distortion due to the L2-norm used in GAs for directed network recovery. The Lp (p ≤1) norm has been shown to be more robust to outliers as compared to LASSO and L2-GAs. Motivated to construct the sparse brain networks under strong noise condition, we hereby introduce a new approach for GA analysis, termed LAPPS (Least Absolute LP (0<p<1) Penalized Solution). LAPPS utilizes the L1-loss function for the residual error to alleviate the effect of outliers, and another Lp-penalty term (p=0.5) to obtain the sparse connections while suppressing the spurious linkages in the networks. The simulation results reveal that LAPPS obtained the best performance under various noise conditions. In a real EEG data test when subjects performed the left and right hand Motor Imagery (MI) for brain network estimation, LAPPS also obtained a sparse network pattern with the hub at the contralateral brain primary motor areas consistent with the physiological basis of MI.