EEG/MEG Source Reconstruction with Spatial-Temporal Two-Way Regularized Regression | Academic Article individual record
abstract

In this work, we propose a spatial-temporal two-way regularized regression method for reconstructing neural source signals from EEG/MEG time course measurements. The proposed method estimates the dipole locations and amplitudes simultaneously through minimizing a single penalized least squares criterion. The novelty of our methodology is the simultaneous consideration of three desirable properties of the reconstructed source signals, that is, spatial focality, spatial smoothness, and temporal smoothness. The desirable properties are achieved by using three separate penalty functions in the penalized regression framework. Specifically, we impose a roughness penalty in the temporal domain for temporal smoothness, and a sparsity-inducing penalty and a graph Laplacian penalty in the spatial domain for spatial focality and smoothness. We develop a computational efficient multilevel block coordinate descent algorithm to implement the method. Using a simulation study with several settings of different spatial complexity and two real MEG examples, we show that the proposed method outperforms existing methods that use only a subset of the three penalty functions.

author list (cited authors)
Tian, T. S., Huang, J. Z., Shen, H., & Li, Z.
publication date
2013
published in
keywords
  • Magnetoencephalography
  • Brain Mapping
  • Electroencephalography
  • Algorithms
  • Regression Analysis
  • Computer Simulation
  • Humans
  • Brain
citation count

2

PubMed Central ID
23842791
identifier
128592SE
Digital Object Identifier (DOI)
start page
477
end page
493
volume
11
issue
4