A two-way regularization method for MEG source reconstruction

TL;DR

This paper introduces a two-way regularization method for MEG source reconstruction that enforces focality and smoothness, improving accuracy and computational efficiency in solving the inverse problem.

Contribution

The paper proposes a novel two-way regularization approach with a two-stage algorithm for efficient MEG source reconstruction, combining sparsity and smoothness constraints.

Findings

Effective on synthetic data

Validated on real-world MEG data

Improves reconstruction accuracy

Abstract

The MEG inverse problem refers to the reconstruction of the neural activity of the brain from magnetoencephalography (MEG) measurements. We propose a two-way regularization (TWR) method to solve the MEG inverse problem under the assumptions that only a small number of locations in space are responsible for the measured signals (focality), and each source time course is smooth in time (smoothness). The focality and smoothness of the reconstructed signals are ensured respectively by imposing a sparsity-inducing penalty and a roughness penalty in the data fitting criterion. A two-stage algorithm is developed for fast computation, where a raw estimate of the source time course is obtained in the first stage and then refined in the second stage by the two-way regularization. The proposed method is shown to be effective on both synthetic and real-world examples.