An inversion method based on random sampling for real-time MEG neuroimaging

TL;DR

This paper introduces a fast, memory-efficient inversion method based on random sampling for real-time MEG neuroimaging, enabling quick localization of brain activity with high accuracy.

Contribution

The paper presents a novel random sampling inversion technique that significantly improves speed and memory efficiency for real-time MEG brain activity localization.

Findings

Method produces accurate brain activity maps in a few hundredths of a second.

Requires minimal memory storage, suitable for real-time applications.

Numerical tests on synthetic data validate effectiveness.

Abstract

The MagnetoEncephaloGraphy (MEG) has gained great interest in neurorehabilitation training due to its high temporal resolution. The challenge is to localize the active regions of the brain in a fast and accurate way. In this paper we use an inversion method based on random spatial sampling to solve the real-time MEG inverse problem. Several numerical tests on synthetic but realistic data show that the method takes just a few hundredths of a second on a laptop to produce an accurate map of the electric activity inside the brain. Moreover, it requires very little memory storage. For this reasons the random sampling method is particularly attractive in real-time MEG applications.